Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

Today, we’re breaking down how Customer.io is packaging agents within their existing tiers, and why this bundled approach feels like the future for most SaaS companies.

Before we get there, our friend Kyle Poyar is running his annual State of B2B Monetization Report. Take a couple minutes to fill it out, and you’ll get access to the full report when Kyle publishes it. Check it out here.

On to today’s post.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

This week we observed 100+ changes. The highlights:

• Datadog Cloud SIEM added a minimum of 50 Bits AI investigations [Link]

• Projuris launched an ROI calculator to quantify law firm efficiency [Link]

• Deepbrain launched Interactive Avatar plans [Link]

• SocialPilot added MCP Server Integration across all four paid plans [Link]

• SentinelOne rebranded all three plans around AI Security messaging [Link]

• HubSpot Sales added Custom Channels API for messaging integrations [Link]

• HubSpot Service added ad automation and CRM segments in beta [Link]

• Contentful Adjusted usage metric capacity [Link]

• Shopify added a max to how much Plus customers can earn back [Link]

• Hexnode moved Web Content Filtering from Ultimate to Ultra [Link]

Check out more updates on PricingSaaS →

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PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Zendesk — 13 searches

• Zoom — 13 searches

• Notion — 12 searches

• Tableau — 12 searches

• Salesforce — 11 searches

🚨 Hot Topics

• AI monetization credits vs seats SaaS 2025

• AI feature packaging for standalone AI SKUs

• Value metric for pricing AI-generated content creation

• Average price increase percentage without feature additions

• Credit-based monetization for agents

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Good Better Best, for Agents

In the last couple weeks, both Anthropic and OpenAI have launched their own agents. Two weeks ago, Anthropic launched Managed Agents, which allow developers to build powerful AI workers:

Then yesterday, OpenAI launched Workspace Agents, which are designed to be AI employees that live inside your existing tools and help automate tedious tasks.

One pretty obvious pattern we’ve seen over the past few years is that when the frontier labs offer new functionality, it’s only a matter of time before it trickles down to the app layer. With that in mind, it seems like a given that we’re about to see a lot more agents on SaaS pricing pages.

A few weeks ago, I highlighted two examples of agent packaging: Notion and Hubspot [link]. Both companies bundle agents into their higher tiers, and use a credit system to monetize usage.

This week, I want to showcase another option, from Customer.io, who just launched their AI Agent in beta.

What I find interesting here is that they have broken the agent down into a set of features, with differentiated access by plan.

All plans get persistent memory and core execution skills, but you need to upgrade to get higher session limits, custom execution skills, scheduled tasks (routines), and higher volumes of scheduled tasks.

Same agent product. Three different levels of capability.

Why the tier-differentiator approach wins

While the add-on model works for companies with flexible agents like Notion, there are 3 reasons I think the tiered approach will be more popular among SaaS companies with more specific jobs to be done.

First, no separate sales motion. The agent sells itself as part of the upgrade path. Essentials customers who need Daily Routines don’t buy an “AI Agent Pro” add-on for $X/month — they move to Premium. The agent’s value gets absorbed into the tier decision customers are already making.

Second, the capability ladder matches how customers actually experience agent value. Agents aren’t a single feature — they’re a level of automation. A small team gets value from an agent that can be triggered. A growing team gets value from an agent that runs on its own schedule. A large org gets value from an agent with custom constraints. That progression maps cleanly onto plan tiers.

Third, credits create usage-based expansion inside the existing plan economics. Customer.io’s 100K LLM Actions credits on Essentials are a conversion trigger — hit the limit, and you either buy more credits or move up. That’s the same Hybrid-Led Growth mechanic we’ve been watching for a year, but now applied to the agent surface specifically.

Looking Ahead

When foundation model providers ship new infrastructure, the app layer tends to pick it up within two to four months. Usually as an experimental add-on first, then as a core part of the product once the playbook stabilizes.

Customer.io is one of the first vendors we’ve seen to have a packaged option, and they’re not going to be the last. Watch for the next wave in categories where agents can replace or accelerate seat-based work — categories like marketing automation, customer support, sales engagement, project management.

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Thanks for reading! If you’re working on monetization and want to learn more about how we help, book time here.

Until next time,

Rob

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

Today, we’re breaking down a move by Buffer, who added a dedicated markdown file to make it easy for agents to understand its pricing. I covered what, why, and how they did it below.

Before we get there — we’re cohosting some awesome events next week that you should definitely check out:

• PricingSaaS Office Hours: On Monday, I’m hosting a session with Ulrik Lehrskov-Schmidt, breaking down how to monetize professional services in the age of AI. Register here.

• Pricing Power Lunch (Boston): We have a couple seats left. If you are a SaaS operator in Boston working on AI pricing, we’d love to have you at the table. Reply directly if you’d like to join.

• The Hidden Cost of Hybrid Pricing: Next week, I’m joining a panel with Zona Zhang from Clay, and Jyotsana Saha from Nue.io to break down where AI-driven monetization breaks and how to fix it. Register here.

On to today’s post.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

This week we observed 100+ changes. The highlights:

▶ Cloudflare added client-side security tiers with malicious code detection [Link]

Timing is hard to ignore given the Mythos/Glasswing moment. When AI can find and exploit your vulnerabilities at scale, client-side security stops being a nice-to-have.

▶ Customer launched AI Agent tiers with LLM Actions and Daily Routines [Link]

Customer created a clear packaging ladder: agent skills and action credits that escalate with each plan. Premium adds Daily Routines (scheduled autonomous workflows). Great example of the different levers youn can pull to monetize agentic functionality.

▶ Linear introduced sub-team depth limits to Business plans [Link]

Previously, sub-teams didn’t have visible limits. Now Business supports 1 level, and Enterprise supports 5. This feels like an intuitive upgrade trigger for organizations that need more structure.

▶ Anthropic added a price change clause to its pricing footnote [Link]

Previously the footnote only covered usage limits and tax exclusions. A small legal change, but one that gives them runway for the pricing adjustments that Mythos-class capabilities will inevitably require.

▶ Twilio added a $35 per monthly user tier to Flex pricing lineup [Link]

This slots in between the existing $1/hour usage model and $150/named seat. A pricing model hedge that gives mid-market buyers a middle ground between pure consumption and full commitment.

Check out more updates on PricingSaaS →

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PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Notion — 28 searches

• Zendesk — 13 searches

• Slack — 12 searches

• Atlassian Jira — 12 searches

• Clay — 10 searches

🚨 Hot Topics

• Outcome-based pricing for AI — how to price around results rather than inputs

• Embedded payments SaaS monetization — turning payments into a revenue stream

• Term length discount trade-offs — when annual discounts help vs. hurt

• Per-seat vs flat-rate for SMB — which model wins for small team products

• Usage-based migration best practices — how to move from flat-rate to consumption pricing

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How to Optimize your Pricing Page for Agents

Buffer did something this week that people are just starting to talk about. They added a link for machine-readable pricing [link]:

The link sits under their main pricing grid. Click it, and you land on /pricing.md — a structured markdown file that serves Buffer’s full plan and pricing data in a format designed for AI agents to parse. Here’s what it looks like:

You’ve probably noticed growing interest in how AI agents interact with websites and products. Kyle Poyar wrote a great post a couple weeks back, and it was a focal point of his recent interview with Metronome CEO, Scott Woody.

Put simply, we’re already in a world where procurement tools, comparison engines, and AI assistants evaluate software on behalf of humans.

When someone asks Claude or ChatGPT “what’s the best social media scheduling tool under $20/month?” the model needs to get that pricing data from somewhere. Today, it requires scraping a pricing page, interpreting a screenshot, or relying on training data that might be months out of date.

Buffer’s move shortcuts all that, and gives agents an easy button to understand the granular details of their pricing and packaging quickly.

Buffer’s move doesn’t exist in a vacuum

It’s part of a broader shift that started with llms.txt — an emerging standard where companies publish a markdown file at their site root that tells AI crawlers what the site is about and where canonical content lives. Over 800 websites have adopted it, including Cloudflare, Vercel, and Coinbase.

Buffer took it a step further. While llms.txt is a general-purpose discovery layer, they published structured pricing data explicitly for agent consumption.

The implementation is a simple .md file. Which makes sense, since Markdown is the native language of LLMs, and it’s easy to maintain alongside a normal pricing page. They didn’t build some elaborate pricing API, they just translated their pricing in a format that machines read well, linked it in an easy to find place on the main page, and shipped it. The whole thing probably took an afternoon.

The barrier to doing this is near zero. Any SaaS company with a pricing page could publish a /pricing.md tomorrow to help AI agents get their pricing exactly right. No hallucinations or misinterpretations from a cached screenshot (we happen to know this struggle well at PricingSaaS 😅).

Here’s what I think gets interesting

Once pricing data is machine-readable, it becomes comparable at scale. An AI agent can pull /pricing.md from ten social media tools, normalize the data, and produce a comparison table in seconds — with accuracy that no human analyst could match for speed. That’s great for buyers. It’s terrifying if your pricing is confusing, your value prop is unclear, or your page is designed to obscure the real cost.

In other words: machine-readable pricing is a forcing function for pricing transparency and clarity.

If your pricing page requires a human to squint, click through three tabs, and do mental math to understand what they’re actually paying, an AI agent is going to struggle with it too. And unlike a human, the agent may just move on to the next option.

Buffer’s move is small in execution and big in implication. We’re entering a world where your pricing page has two audiences: humans who browse and agents who parse. The companies that recognize this early, and make their pricing legible to both, are going to win more of the deals they never even knew were happening.

A /pricing.md file is the lowest-effort, highest-signal thing you can ship this quarter. Buffer just showed everyone how.

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Thanks for reading! If you’re working on monetization and want to learn more about how we help, book time here.

Until next time,

Rob

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

Today, we’re covering the 3 most interesting pricing changes from the last week, breaking down moves from Slack, Canva, and Monte Carlo.

Let’s get to it.

Before we get there — we’re hosting an Office Hours session next Thursday (4/16) with David Reid of Teneo. David leads Teneo’s Global Private Equity Growth Practice and has helped tons of legacy SaaS companies rework their pricing and packaging for the AI era. If you’re feeling that anxiety right now, this is the one to join — RSVP here.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

Last week we observed 100+ changes. The highlights:

• Slack adds CRM features view

• Canva adds AI allowances, launches AI Pass add-on for power users — view

• Jobber hikes list prices — view

• Botpress raises Managed plan price 25% as introductory period ends — view

• Monte Carlo pivots to AI agent branding — view

• Pinecone lifts assistant document caps — view

• Mem0 caps Pro plan at 500K add requests — view

• Ahrefs doubles Lite plan credit limit — view

• Moz launches AI Visibility Dashboards and Prompts Tracked beta — view

• Otter adds user limits to Salesforce and HubSpot integrations — view

• Framer unlocks analytics history across all plans — view

• Mendix removes Customer Success Manager from Standard plan — view

Check out more updates on PricingSaaS →

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PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Notion — 29 searches

• Mixpanel — 15 searches

• Salesforce — 14 searches

• Deepgram — 13 searches

• Slack — 12 searches

🚨 Hot Topics

• Outcome-based pricing for AI

• AI add-on monetization bundling strategy

• AI pricing credits models

• AI agent customer service pricing (per resolution, conversation, token)

• AI pricing trends 2026

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PricingSaaS Receipts ▶

Breaking down 3 pricing and product updates worth discussing this week.

Slack officially added Slack CRM to the pricing page.

On the pricing page, Slack added a new category called CRM Capabilities, with four features. Notably, only one of the four requires a Salesforce subscription.

The data sync needs an account — but tracking leads and deals, managing support emails, and prepping for meetings all run natively inside Slack CRM, their new small-business CRM that lives entirely in Slack.

The CRM category is already being rewritten by AI-native players like Day AI, Attio, and Clarity — tools betting that in the age of AI, CRM looks different. They’re winning by pulling context from where deals actually happen: call transcripts, email, and Slack. The deal stage lives in the CRM — but the signal around every deal (rep and manager discussions, customer notes and research, follow-ups) overwhelmingly happens elsewhere. That’s the raw material these tools are mining.

Which makes Slack’s move notable. Day AI, Attio, and Clarity are going after startups and SMBs, the segment Salesforce has always struggled to serve without cannibalizing its enterprise brand. Slack CRM gives Salesforce a clean SMB entry point — no legacy baggage, that sits on top one of the richest context layers imaginable.

On the pricing front, Slack CRM is gated to Business Plus and Enterprise Plus. Free or Pro customers can pipe Salesforce data into Slack with an active Salesforce license, but need to upgrade to access Slack CRM. It’s a clean monetization fork: you’re either already paying Salesforce, or CRM becomes the hook to move you up a tier.

Canva introduced AI limits and a new AI add-on.

Canva made two moves worth paying attention to, both about one thing: figuring out how to price AI inside a plan structure that was never designed for it.

1️⃣ They added explicit AI allowances for each plan. Before, Canva used vague language around AI usage limits. Now every plan has a clearly defined limit, which is framed as an “allowance.” It’s a purposeful word choice. An allowance feels like a gift. A limit feels like a punishment.

2️⃣ They introduced the AI Pass add-on. They also introduced a new add-on called AI Pass at $100/month, which unlocks 40x more AI than Canva Pro (or 20x more than Canva Business) across Ultra, Premium, and Standard AI tools. Notably, AI Pass is gated to paid plans, creating two ways for Canva to grow accounts with AI usage:

1. The allowances drive paid conversion and tier-to-tier upgrades.

2. AI Pass offers additional expansion for power users.

The biggest question here is why not just use credits like everyone else? If the whole mechanic boils down to “how much AI did you use this month” credits are the cleanest expression of that.

Canva seems to be betting the simplicity of fixed allowances and a flat-rate add-on is preferable to credits for their SMB base, which would make sense if their AI features were simple. However, they’re already introducing complexity by categorizing their AI tools as Standard, Premium, or Ultra. Honestly, a simple credit model that charges more credits for more complex tasks would likely be easier to understand.

Monte Carlo rebranded the entire pricing page around agents.

In the top positioning move of the week, Monte Carlo rewrote its pricing page around AI agents. The headline changed from the very 2023-coded “Only pay for what you need” to “Products that make Agents smile.”

Why agents, and why now?

Monte Carlo has been openly building toward this. Last September, they launched Agent Observability to monitor the context, performance, behavior, and outputs of production AI agents end-to-end. The market data they cite explains the urgency:

• Nearly 80% of enterprises have already deployed AI agents

• More than 40% don’t trust their AI/ML model outputs

• 45% call data quality their top AI obstacle

• 53% already expect to significantly rebuild the agent systems they just shipped

Read between those numbers and the repositioning makes sense. Monte Carlo built its brand catching broken pipelines before they wrecked dashboards — targeting data engineers buying for BI teams.

Once enterprises pipe production data into agents that take autonomous actions, “the dashboard looks weird” turns into “the agent made the wrong decision a thousand times before anyone noticed.”

That’s a different risk category and a different buyer: the platform team standing up agent infrastructure, who needs a guarantee that the data and the agents on top of it are trustworthy in real time. This repositioning is their bid to reframe data observability as agent reliability infrastructure, in all likelihood a much bigger and more urgent budget line than BI hygiene.

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Thanks for reading! If you’re working on monetization and want to learn more about how we help, book time here.

Until next time,

Rob

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

Today, we’ve got a guest post from my friend Farhan Manjiyani. Farhan leads pricing at Grafana Labs, where’s he’s owned commercialization for a range of products, including Grafana’s agentic and AI-powered solutions.

In the process, Farhan has become an expert on fair use policies, which are becoming especially relevant in the age of AI. Below is his tactical guide including four real examples, how to create your own, and best practices for implementation.

Let’s get to it.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

Last week we observed 100+ changes. The highlights:

• Lovable shifted Enterprise to a platform fee model [Link]

• Base44 cut Starter Plan’s Annual Price 20% to $16/mo [Link]

• Docusign renamed a plan and raised its price [Link]

• Deepgram slashed Voice Agent API prices across all tiers [Link]

• Guesty tripled Lite plan pricing [Link]

• Framer dropped Advanced Hosting price 33% to $200 [Link]

• Gusto cut Plus plan pricing up to 14% across base and per-person fees [Link]

• Dynatrace launched telemetry pricing with plans starting at $0.15/100k [Link]

• LastPass ended its 30% promotional discount across all plans [Link]

• Pushwoosh overhauled pricing with a unified MAU model [Link]

Check out more updates on PricingSaaS →

Subscribe now

PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Clay — 20 searches

• Notion — 18 searches

• Wrike — 14 searches

• Anthropic — 10 searches

• Vercel — 9 searches

🚨 Hot Topics

• AI pricing complexity — agentic vs assistive, credit models, usage-based

• Developer tools unit economics — LLM costs, per-seat vs usage

• Free-to-paid transition strategy — beta pricing, change management

• Healthcare facility pricing — per-facility SaaS models

• PE/VC revenue growth — growth-stage packaging strategy

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Why Fair Use Policies Matter

Every software CFO has two priorities right now that directly contradict each other. Cut software costs. Increase AI usage. Fair use is how you hold both.

The reality is 5-10% of users are actually driving 90% of costs. A price increase for 100% of your base when 90% aren’t the problem is a tough sell (e.g. 1Password’s price increase pushback). A company’s cost increase is a company problem, not a customer one.

So the answer is to design a lever that only 10% will feel.

Take a simplified example:

You have 1,000 customers on a $50/month plan. Your AI feature costs you roughly $2 per customer per month in compute at median usage. That’s manageable — $2K in variable cost against $50K in revenue.

But the P95 customer isn’t using $2 worth of compute. They’re using $25. And the P99 customer? $80. That top 1% — just 10 accounts — might cost you more in compute than the bottom 500 combined.

You have two options.

1. Raise prices across all 1,000 customers to cover the tail.

2. Or set a fair use threshold at something generous — say the 95th percentile — and only trigger a conversation, overage, or throttle for the 50 customers above it.

Option one risks churn from 950 customers who were never a problem. Option two protects your margin without touching the experience for anyone using the product normally.

How Real Companies enforce Fair Use

One large customer can move product COGS by multiple percentage points without anyone noticing. I’ve used fair use to cut product COGS by 33% in six months. And this isn’t unique to one industry.

• Grafana Labs: Grafana Cloud Logs uses a 100x fair use query policy. You can query up to 100 times your ingested log volume each month at no extra cost. Go beyond that, and billing kicks in based on a formula comparing what you ingested versus what you queried. What I like about this approach -- it’s transparent, it’s generous, and most customers aren’t even close. But it gives the business a clear mechanism when someone’s query patterns are wildly out of band.

• OpenAI: ChatGPT’s approach across their plans uses rolling message caps by model. Hit your limit on GPT-5, and the system automatically drops you to a lighter model until your window resets. You’re not cut off. You’re not billed extra. You just get a less powerful version temporarily. The experience degrades instead of stopping. That’s a meaningful difference when the alternative is a hard cutoff.

• Atlassian Rovo: Atlassian bundles Rovo AI into paid Jira and Confluence subscriptions with a credit-based quota pooled across the organization. Right now, they’re not enforcing the limits. But they’ve published the quotas, committed to 90 days notice before enforcement begins, and will include dashboards for monitoring usage.

• ClickUp: ClickUp uses AI Super Credits with what they call “Super Fair Billing.” They pass on savings when AI model costs drop, subsidize spikes when costs rise, and provide grace periods if you burn through credits faster than expected. It’s fair use plus an explicit economic compact with the customer.

These are just a few examples to get the ideas flowing. Your own policy will depend on your product, customer usage patterns, and how hard you want to enforce it.

How to create your own

While every company is different, I’ve found the following 3 steps will consistently get you off to a good start:

Step 1: Inspect. Start with taking a deeper look at your usage patterns. If you have 1,000 teams that have adopted a product with an average of 10 users per product, pull up usage by user. If you see the 90/10 pattern then you have a great candidate for fair usage.

Step 2: Analyze. Take the P95 (pro tip - there is a percentile function in excel / sheets) of your usage. The 95th percentile = fair usage of customers. You can position this to customers as “95% of customers should be unaffected by this policy” and you can focus on negotiating with the 5% who fall outside these usage patterns.

Note: P95 makes sense for dev tools where there is a long-tail of usage. For infra or spiky usage, P90 may make more sense. For productivity tools, the fair usage may be closer to P99.

Step 3: Apply. My recommendation is to enforce fair usage in every single contract and apply it to your entire customer base even if it only affects 5%. Then depending on the product you can decide if you want it to be a hard limit (e.g. rate limit, stop usage, or automatically charge more when customers pass it) or soft limits (e.g. notice to customers but allow them to continue scaling). The benefit of the latter is you have the right to renegotiate the pricing without any disruption in customer usage/adoption. It’s a win-win.

The next step is implementing your policy, which comes with its own nuances.

How to roll it out

The biggest difference is how to approach new customers vs existing customers. Here’s what I recommend:

• New Customers: Net new customers are easy, just start applying the policy to all new contracts going forward.

  • Self-serve: For credit-card based customers, having fair usage clearly called out on the pricing page + examples in the documentation is enough. The key is to:

    • a) help them understand what it is and how to track it

    • b) show them how to optimize so they stay under.

  • Sales-Led: As part of your sales conversation it’s important to proactively flag the fair usage. I’ve actually used it as a competitive advantage; other companies may actually charge for dimensions like queries as an alternative to fair usage.

• Existing Customers: Roll out the policy to existing customers at their next renewal. All current contracts should be grandfathered in. In the renewal conversation make sure you have a clear way for customers to track how close they are to the threshold. Proactive alerting is a bonus. One note:

  • Expansion Trigger: Exceeding the fair use policy is a great signal that the customer is rapidly growing and it’s time for a renegotiation or misusing the product and needs support. Either way, introducing a fair use policy is a great excuse to start the conversation.

Final Thoughts

The irony of fair use is that it’s better for the customer its “enforced” on, too. That customer running 150x their expected usage? They’re either getting massive value and should be on a bigger plan, or they’re doing something wrong and need help. Either way, the conversation is worth having.

If you’re building a fair use policy and want a second set of eyes, reach out to set up some time here.

Thanks for reading! Along with his work at Grafana, Farhan helps SaaS operators craft better pricing and packaging strategies. Learn more and get in touch here.

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Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

This week, we published an exclusive report with our friends at Nue.io. The report highlights how companies are blending PLG and SLG through acquisition and expansion strategies, and is jam-packed with examples.

Grab the full report here →

Today, we’re sharing 5 learnings from our latest Office Hours session with Manny Medina, CEO of Paid. Paid is the monetization platform for AI agents, and Manny talks to 5-6 SaaS leaders a day, often helping them workshop their AI credit strategy.

Let’s get to it.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

Last week we observed 150+ changes. The highlights:

• OpenAI tripled GPT-5 mini pricing and launched a nano tier [Link]

• Datadog added Edge Device Monitoring [Link]

• Hubspot launched 3 beta features for the Enterprise suite [Link]

• MongoDB restructured Atlas tiers [Link]

• Zoom launched an Enhanced Media add-on [Link]

Check out more updates on PricingSaaS →

Subscribe now

PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Notion — 32 searches

• Asana — 18 searches

• Figma — 13 searches

• Linear — 12 searches

• Adobe — 10 searches

🚨 Hot Topics

• Annual contract discount benchmarks — how much to offer for annual commitments, and what the median looks like by ACV

• Agentic AI pricing models — how to price per outcome, per document, or per usage metric for AI-powered features

• Competitive pricing set mapping — side-by-side comparisons across direct competitors to find positioning gaps

• Credit-based pricing frameworks — how to structure token and credit monetization for AI features

• Per-seat vs usage-based tradeoffs — when to move away from seat pricing and what to replace it with

Start Using PricingSaaS Pulse for free

Last week, we hosted an Office Hours session with Manny Medina, CEO of Paid. This one was jam-packed with thoughful discussions around one of the hottest topics in SaaS — AI credit models.

Below, I’m breaking down the top 5 takeaways that you can steal for your own AI credit strategy.

1️⃣ Use a “gift first” approach to transition existing customers to credits.

This was Manny’s top advice for anyone introducing credit-based models alongside an existing seat-based model. Instead of asking customers to pay more, position the new capability as a feature that’s included in their seat — but powered by credits.

You’re essentially saying: “We normally charge for this on a credit basis, but because you’re a loyal customer, we’re going to bundle some credits for the next few months so you can see the value.”

Once those months are up and customers have seen value, the commercial conversation is natural. Manny framed this through Cialdini’s reciprocity principle — when you give someone a gift, they feel a natural pull to give something back when appropriate.

2️⃣ Create an abstraction layer between your internal rate card and customer-facing pricing.

Manny was emphatic that your internal rate card — where you track costs per API call, token consumption, model type — should never be what the customer sees.

The customer needs to see value in their language: tasks completed, reports generated, time saved, risk avoided.

Lisa from Pendo raised a great follow-up, asking how to manage constant pricing changes when the underlying models keep evolving. Manny’s advice: keep the rate card changes internal. When costs change, you frame it to the customer positively — “we have a new, more powerful capability, here are some free credits to try it” — rather than exposing the complexity underneath.

3️⃣ Monetize around workflows, not individual actions.

Riley from ClickUp asked how to deal with downward pressure on credit prices as foundation models get cheaper.

Manny’s answer was to move up the value chain. Instead of charging per LLM call or token (where you’re basically reselling commodity infrastructure with a thin margin), build monetization around complete workflows — composite sets of activities that result in a tangible task or output.

If it’s tied to something a human would have had to do, the value metric becomes “human equivalent work” and your margin stays healthy regardless of what happens to token prices.

He shared a real example from a Paid customer doing accounting reconciliation: they narrowed it down to four core workflows their customers execute, each mapping to work an accountant would otherwise do manually.

4️⃣ Credits are a bridge to outcome-based pricing. Start planning for outcomes now.

Manny laid out a multi-chapter roadmap for how AI monetization is evolving. Right now, we’re in the earliest chapter: transitioning from seats to credits.

Next comes standardization — credit models will become comparable across vendors, and “credit efficiency” (how much value you get per dollar of credits) will become a real metric.

After that, credits start getting tied to outcomes, and eventually you’re buying outcomes directly. He was candid that we’re probably two years away from that, but said for every five SaaS companies he talks to about this, four say they’re not ready. His advice: you don’t need to have it perfect. Just start the conversation about outcomes now.

5️⃣ The real unlock with credits isn’t monetization — it’s growth.

The smartest companies aren’t just using credits as a new way to bill customers — they’re using them as a growth engine.

Manny mentioned Cursor using credits to incentivize off-peak usage, Lovable pioneering free daily credits that hook new users, and how granting credits to your most engaged power users (not just low-usage customers) can drive adoption of new features and create viral expansion within organizations.

Manny views credits as a flexible tool to power creative growth strategies that wouldn’t be possible with license models. The big mindset shift: stop thinking about credits as a billing mechanism and start thinking about them as a lever for adoption, retention, and expansion.

Thanks again to Manny, and everyone who attended this session! Join the PricingSaaS Community on Skool to stay informed on the next one.

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• Community: Answers from real pricing experts. Apply for membership →

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• Advisory: Free pricing advice. Book a 30-minute call →

“Should we add a free tier?”

“Do we need a sales team on top of self-serve?”

“How do we price a product that serves both motions?”

We just released new research to answer those questions with data instead of opinions.

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PLG vs. SLG: What the Data Says about Growth in 2026

Our new report with Nue.io analyzes 3,847 pricing, packaging, and product changes across 498 SaaS companies from 2024 through 2026. The dataset spans 12 software categories, from AI & Machine Learning to CRM to Cybersecurity.

The big picture: the line between PLG and SLG has collapsed into something new. We’re calling it hybrid-led growth.

Here’s a taste of what’s inside:

▶ Freemium is bifurcating. Of the 40 companies that changed their freemium model last year, roughly half rolled it back (Deputy killed its free plan for a 31-day trial; Plaid replaced its free tier with a sandbox-only environment) while the other half doubled down (TravelPerk made its Starter plan completely free at $0/month while launching three paid add-ons as the real monetization layer).

▶ Trials are getting shorter. Trial reductions outnumbered extensions 3:1 across 2025. The median SaaS trial is moving from 30 days toward 14, and AI-native tools are already trending toward 7. Why? AI-powered onboarding means users hit value in minutes, not weeks. Voiceflow cut its trial from 14 to 7 days while increasing AI token allocations 150%. The logic: shorter window, richer product, faster conversion.

▶ Credits are the new expansion currency. Credit-based pricing adoption grew 126% year-over-year across our index. Monday.com added 500 AI credits to every plan simultaneously — Basic through Enterprise. HubSpot shifted from per-conversation credits to a flat 500-credit pool. Figma went from AI as a standalone add-on to free-tier credits in four quarters. Credits are the purest expression of hybrid growth: self-serve consumption with built-in sales triggers.

▶ AI is the forcing function behind all of it. 242 AI-related feature events in Q4 2025 alone. 30 new AI add-ons launched. And AI features are following a remarkably consistent three-stage monetization lifecycle: standalone add-on → embedded in premium tiers → core feature with usage limits. Each stage maps to a different growth motion.

▶ The expansion playbook has been rewritten. The old model (user hits limits → sales rep calls → tier upgrade) is fracturing into three distinct mechanisms: add-on layering (Salesforce Agentforce at $125–$550/user/month), credit-based expansion, and capacity-for-price swaps (Shorthand raised prices 119% while making members unlimited). Together they replace the single “upgrade your tier” motion with multi-path expansion where PLG and SLG coexist at every stage.

The full report includes detailed case studies on Deputy, TravelPerk, Freshworks, Tabnine, New Relic, Salesforce, Monday.com, HubSpot, Figma, and more — with before/after pricing comparisons and strategic analysis for each.

As Nue.io CEO Mark Walker told us:

“Very few companies reached massive scale on pure PLG. Eventually, either customers’ CFOs want control, or you start dealing with enterprise tiers that require security reviews and formal contracting. AI is accelerating this.”

If you’re evaluating your growth model, you’ll want to give it a look.

Download the full report →

If anything in here surprises you (or you’re putting your own spin on hybrid-led growth), reply to this email. I read every response and would love to hear your perspective.

Talk soon,

Rob

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

This week, we launched our first Pulse Skill. It’s called Market Map and allows you to do competitive research that used to take hours (or days) in minutes. If you’re new to MCPs and Skills, here’s how we’re thinking about it:

▶ PricingSaaS MCP is the intelligence layer — it connects your AI tool directly to our data in PricingSaaS Pulse, where we’re tracking thousands of SaaS companies.

▶ Pulse Skills are the action layer — they sit on top of the data and provide instructions for specific, executable workflows to make it immediately actionable.

Learn more here, and reach out if you run into any questions.

Today, we’re breaking down the backstory behind Clay’s new pricing model that took over the pricing world last week. Huge thanks to Zona, Clay’s monetization lead, for sharing how it all went down! Let’s get to it.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

Last week we observed 150+ changes. The highlights:

• Asana revealed prices for its Timesheets add-on at $5.99/user. Link →

• Dashlane unbundled Credential Protection at $4. Link →

• Optimizely consolidated experimentation products. Link →

• Anthropic API simplified pricing and removed volume-based tiers. Link →

• Conjointly raised prices for the Professional plan. Link →

• Airwallex shifted the Grow plan to $12/user pricing from flat $99/month. Link →

• BigCommerce payment processing fees. Link →

• Amplitude added AI Agents & MCP features across all plans. Link →

• Triple Whale removed monthly credit limits. Link →

• Together shuffled its model lineup. Link →

Check out more updates on PricingSaaS →

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PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Clay — 20 searches

• Notion — 18 searches

• Wrike — 14 searches

• Smartsheet — 11 searches

• Anthropic — 10 searches

🚨 Hot Topics

• Agentic AI monetization — credits vs hybrid license vs outcome-based pricing

• AI credits pricing models — structuring credit-based systems for AI features

• Developer tools unit economics — LLM costs, per-seat vs usage margins

• Annual discount benchmarks — contract terms, ACV sensitivity, SMB churn

• Free-to-paid transition strategy — beta pricing, change management playbooks

Start Using PricingSaaS Pulse for free

The Backstory behind Clay’s new Pricing Model

You’ve probably used Clay’s pricing page as a reference in the past couple years. Their credit-based model became the template of the AI era. Kyle Poyar called it out. Pricing consultants put it in decks. If you wanted to show a client “this is how credits should work,” you pulled up Clay.

So when Clay announced last week that they’re restructuring their entire pricing model — introducing a completely new “actions” metric alongside data credits and consolidating from five tiers to four — it raised a big question: why change something that’s working?

I sat down with Zona Zhang, who leads monetization at Clay, to get the full story.

I. Why it went down

Clay originated as a data enrichment tool, and the credit model fit perfectly. You buy credits, you spend them on data. Clean and simple. But over time, Clay’s customers started using the product for orchestration, which didn’t fit into their credit model.

“We saw a lot of users bringing their API keys and connecting all of their systems, which is great to see,” Zona said. “However, we only monetized on data. We knew we were going to continue to invest in higher scalability and better capabilities for people doing orchestration workflows, and the cost of the product would reflect that. But if we only charged for data, we would start to get feedback from the market like, ‘Clay is selling data at a high premium — it’s 2x what I’d get if I go straight to a provider.’ We don’t want to put ourselves in the position where we’re not monetizing the platform for people who come for the platform, but then overcharging people who are just here for the data.”

The more valuable Clay became as a platform, the less its pricing captured that value. And the data-only customers were subsidizing a platform premium they didn’t ask for. In other words, the model that powered Clay’s growth no longer aligned with where they were going.

II. How it went down

Phase One: The Memos

This wasn’t a snap decision. Zona said the first conversations that led to this change started over a year ago. What followed was three distinct phases of iteration.

Phase one was the longest and hardest — a small working group banded together to get the hypothesis right. Zona and Karan (Clay’s CFO) wrote the internal pricing memo four or five times. They talked to 30+ agencies. They interviewed self-serve customers and enterprise buyers from different segments, making sure each cohort felt pricing was fair.

“I doubted myself maybe 20 times,” Zona said. “I kept thinking, should we just ship something and see how people react? But in the end, I appreciated us being extremely critical with the process. Because later on, as we implemented all these details and tried to bring it to market, everybody started to question every small decision. And we had the documentation to explain why we made that choice.”

That documentation became their internal objection-handling playbook. When every stakeholder questioned a small decision, Zona could point back to past memos and speak to the reasoning behind each decision.

Phase Two: Implementation

The model Clay landed on draws inspiration from Snowflake’s pricing architecture — a platform component and a commodity component (e.g., compute vs storage). In Kyle Poyar’s post breaking down Clay’s update, he called this new model Platform + Tokens, where the platform can garner ~80% margins, and tokens garner ~20% margins. In Clay’s case, it’s actions (platform) and data credits (commodity).

Why actions and not a simple platform fee? Zona walked me through the logic:

“Everybody likes a platform fee — it’s non-controversial. But the issue is it doesn’t scale. We sell to both PLG and enterprise customers. Platform fees make perfect sense to self-serve customers. When customers expanding from self serve to enterprise, they have to suddenly predict their usage, but don’t have that habit from when they were on the self-serve plan. Now it feels like Clay is taking something away from them and it becomes unpredictable.”

Actions solve this by introducing the concept early, while making it invisible for most users. Clay set the initial action limits so that 90% of users won’t need to think about it — effectively functioning as a platform fee for them. But for the 10% who are power users? They’re already thinking in terms of actions, so the upgrade to the next tier feels natural rather than jarring.

The insight that locked the metric in was about how differently each record gets treated inside Clay:

“If you don’t care about the data quality, you’re doing a super large outbound campaign — you’re probably just spending half a credit on every email and another half a credit generating a very generic AI message. That’s cheap. And you don’t want to charge that one record the same amount as your ABM target list that you’re doing months of account research on, where you’re actually contacting only five people a month per rep. Our customers differentiate the value of every lead, and Clay is the right tool that can give you differentiated value per lead. That’s why we decided to use actions instead of a record or a row or an export.”

Notably, Clay expects to see a 10% drop in revenue in the short-term due to reduced data costs. Over time, however, Zona and the team expect customers to upgrade as their actions yield better and better results, creating another expansion lever beyond data credits.

Phase Three: The Launch

We see a lot of pricing changes at PricingSaaS. Often, they slip by quietly, under the radar. Clay did the exact opposite.

“The first version was, let’s just publish a memo,” Zona said. “Let’s say the honest things and tell them how much we’ve thought through this and show all the math. And then our marketing team really pushed back. They were like, no one wants to see all the math. You should do a video that talks to people like humans.”

So Varun and Karan recorded a long-form video walking through the reasoning, and posted it to LinkedIn. If you watch it, you can see Varun looks a little nervous, which Zona confirmed was genuine. Karan published the public memo with the math as a companion piece. Every executive posted on LinkedIn explaining the “why” in their own words. The FAQ section on the pricing page (which Clay has always used strategically) got its own revamp, with an especially helpful guide breaking down which features do and don’t consume actions.

And then there’s the grandfathering — where Clay went against conventional wisdom, allowing customers to stay on legacy plans, and still benefit from new functionality.

“We did something that every pricing expert told us not to do,” Zona said. “We grandfathered existing customers without a cutoff time There might be one day where we have to sunset this, but there’s no formal timeline. And the way we’re thinking going forward is — anything that’s a quality-of-life improvement, we should give it to legacy plans too. We want to show the community that we’re still servicing you, instead of saying we’re only going to build for enterprises now. “

The only exception is genuinely new use cases, like Clay’s new ads product, which is exclusive to modern plans because it serves a completely different persona (marketers, not rev ops). Zona believes there will eventually be a natural tipping point where it’s economically better for legacy customers to move to the new pricing. But they’re not forcing it. They’re letting the product make the case.

III. How it’s going

The early reception has been overwhelmingly positive — particularly on the transparency and communication of the launch. But the most interesting signal is what customers are asking for next.

“People are asking very good and thoughtful questions. A lot of people asked for detailed mapping of their historical usage, and they want to know how that informed new pricing. Currently, if we think you should move to the new plan, we’ll give you a recommendation based on your historical usage. But there’s an argument to give people more information and see what they do with it.”

The one area that’s still being iterated on: AI variable pricing. Because Clay charges for AI the way OpenAI or Anthropic does — based on actual compute consumed — users are pushing back.

“People are so used to seeing a fixed-cost menu before you run everything,” Zona explained. “They have expectations. If you tell me you’re going to withhold five credits, that means five credits. You should not charge me six, you should not charge me four.”

The UX around setting expectations for variable pricing is actively being iterated on.

IV. The takeaway

Clay’s move is a signal for every SaaS company running a credit-based model: your pricing metric needs to evolve with your product. If your product started as one thing and became something bigger, your pricing probably hasn’t caught up.

But the deeper lesson is about process. Clay’s patience and internal documentation turned uncertainty into confidence. The memos. The objection handling. The transparency at launch. Every step compounded into a pricing change that landed well because it was battle-tested long before it went live.

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Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and share the ideas worth stealing.

We’ve seen a flurry of AI-related pricing changes over the past few weeks, so today curated some interesting examples for everyone out there navigating the options. For further research, check out PricingSaaS Pulse, which is currently in free beta.

Warning: this post went too long for email, so click ‘View in Browser’ to read the whole thing. Let’s get to it.

PS. Next week, we’re hosting an Office Hours session with Manny Medina, CEO of Paid on all things AI credit models. Learn more and register here.

PPS. My friend (and former boss 🫡) Jordan DiPietro put together an awesome guide to decoy pricing based on an experiment he ran at Motley Fool that generated $8M in annual revenue. Pricing psychology still works — especially for PLG/consumer products. Grab it here.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

We’ve ramped from 500 to 2,000 companies tracked, and predictably, change volume followed suit. Last week we observed 120+ changes. The highlights:

• Clay updated its pricing model [Link]

• Synthesia hiked the Starter plan 53% to $29/month [Link]

• Miro shifted Enterprise AI credits from 100/member to custom amounts [Link]

• Vercel launched Queues Beta with 1M free ops, $0.60 per 1M extra [Link]

• SimilarWeb launched AI Studio for natural language market insights [Link]

• Softr redenominated AI credits: 100 old credits equal 1 new credit [Link]

• Hootsuite removed Approval Workflows and Permissions from Advanced [Link]

• Dialpad replaced Connect with AI Agent conversation pricing [Link]

• HubSpot added User Management to Free Tools [Link]

• Supermetrics added Management API to Enterprise plan [Link]

Check out more updates on PricingSaaS →

Subscribe now

PricingSaaS Pulse Intelligence

Here’s what was top of mind in Pulse this week:

🔥 Hot Companies

• Klaviyo — 19 searches

• Zendesk — 16 searches

• Notion — 15 searches

• Intercom — 12 searches

• Stripe — 12 searches

🚨 Hot Topics

• Agent vs. Assistant pricing — OBP vs. UBP, complexity pitfalls

• AI monetization — credit-based pricing, bundling strategies

• Free beta → paid transition — customer backlash, change management

• Enterprise packaging — paid POC, proof of concept pricing, land & expand

• Commercial debt — price anchoring, free features, grandfathering

Start Using PricingSaaS Pulse for free

10 AI pricing moves you can steal this week

For the past year, everyone’s been asking the same question: how do you price AI?

I wish there was a silver bullet. The honest truth is it depends. It’s hard to draw hard trends across the wider SaaS landscape. We’re talking about thousands of companies that serve hundreds of different buyers at varying levels of AI proficiency.

In the last few weeks, we’ve seen a wide range of pricing, packaging, and product changes. Below we’ve curated a punch-list of tactics that are worth watching.

1️⃣ Monday.com’s Enterprise AI Bundle

Enterprise plans are usually pretty boring. But Monday.com just did something interesting to change their. They've bundled all AI features - Sidekick, AI credits, and Vibe - into an "Enterprise AI bundle" that only lives on the top-tier plan.

This could have an interesting impact on the upgrade conversation. The old enterprise motion was driven top-down. IT needed better permissions. Security needed compliance controls. The decision happened in a room full of people who were not using the product. This enables a bottom-up approach. [Link]

2️⃣ Notion’s Custom Agents Add-On

A couple weeks back, Notion launched Custom Agents as a paid add-on. It’s available only to Business and Enterprise customers, who can trial for free, then pay on a credit-based model ($10 per 1,000 credits).

The “s” in agents is the important here. Given Notion’s broad functionality, there’s a high likelihood heavy users will create multiple agents that do various jobs, making the add-on a strategy a no-brainer. [Link]

3️⃣ Miro’s Custom Enterprise AI Credits

Miro has a tiered AI credit system, and this week they changed Enterprise from 100/member to “custom amounts.” Removing the cap gives Enterprise prospects flexibility, and forces a conversation to determine the right allotment. [Link]

4️⃣ Pendo’s Flexible AI Add-On

Pendo restructured their entire pricing page — putting AI front and center. They offer baseline AI functionality across all plans, while allowing customers to add more AI modules as needed. This flexible approach aligns with their pricing philosophy which is anchored in All-In Access (no feature gates within a purchased module) and Customization. [Link]

5️⃣ GitLab’s Agent Integration

GitLab’s AI pricing move isn’t about their own models — it’s about becoming the platform layer for third-party AI agents. By adding Claude Code and Codex to their Duo Agent Platform, GitLab is positioning itself as the central hub for agentic development workflows. [Link]

6️⃣ SimilarWeb’s AI Studio Module

If you’re investigating the modular approach, SimilarWeb is another interesting example. They recently repackaged their AI features and launched a new module called AI Studio. This allows them to offer an AI-native layer on top of their existing dashboard-driven analytics, and capture additional revenue. Particularly useful given their sales-led approach for Business and Enterprise accounts. [Link]

7️⃣ Dialpad’s Conversation-Based Agent

Dialpad pulled from the Intercom playbook and introduced a new AI Agent tab to their pricing page, differentiating it from existing plans with ‘per conversation’ pricing. This gives them a seat at the table for OBP-based Customer Success products, and is a smart retention play to engage customers who are exploring OBP. [Link]

8️⃣ Rocketlane’s Enterprise Agent

Rocketlane introduced two agents, differentiated by credit volume, and the actual jobs the agent can do. This feels like a peek into the future, where more and more plans will be differentiated by agent capabilities, and “harder jobs” garner a higher price tag. [Link]

9️⃣ Softr’s AI Credit Reduction

Softr cut their free plan AI credits by 99% — from 500 to just 5. Expect more of these pullbacks as companies realize free AI credits are a line item, not a rounding error. The key is finding the limit where free users actually see enough value to upgrade. This is also why I’m bullish on MCP as a freemium strategy (it takes LLM costs off your books). [Link]

CORRECTION: Softr actually redenominated AI credits. 100 old credits equal 1 new credit. Customers get the same level of usage. More information here.

🔟 Hubspot’s Free Trial for Customer Agent

Hubspot launched a 28-day unlimited free trial for its Customer Agent, giving Professional and Enterprise users the ability to fold it into their workflows.

Free trials remain underrated assuming you have the CSM team to support it. It’s a smart move to convert early customers and gather learnings to make the product better long-term. [Link]

Looking Ahead

If there’s one takeaway from these moves, it’s that the “how do we price AI?” question is being answered in real time — through bundles, credits, modules, agents, and good old-fashioned free trials.

There’s no bulletproof strategy, only iteration. If you’re working on this and could use a sounding board, shoot me a note at rob@pricingsaas.com.

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Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and extract the ideas worth stealing.

Last week, we launched the PricingSaaS MCP. This week, we shared a Claude Skill we created called Report Builder that we’ve been using for deep dives on individual companies. Grab it here, test it, remix it, and use it for your own research 🫡

This week, we’re digging into Agents. We’re starting to see more of them pop up, and importantly, noticing different ways to price and package them.

Let’s get to it.

PS. Next week, we’re hosting an Office Hours session with Scott Woody, CEO of Metronome on the latest in AI pricing and packaging. Learn more and register here.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

Another busy week with 50+ changes detected. Here are the highlights:

• Notion launched a Custom Agents add-on [Link]

• Buffer slashed prices 75% but cut default channels to 1 [Link]

• Alfred hiked annual plan prices up to 68% across all tiers [Link]

• GitLab added Claude Code and Codex to its Duo Agent Platform [Link]

• Botpress cut prices on Team and Managed plans [Link]

• ChartMogul expanded the Pro plan ARR cap from $500k to $10M [Link]

• DigitalOcean raised GPU Droplet prices on multi-month contracts [Link]

• LexisNexis rebranded its AI legal assistant as Lexis+ with Protege [Link]

• Exa expanded its free tier to 1,000 monthly requests and revamped pricing [Link]

• Alchemy capped Enterprise webhooks at 500, ending unlimited access [Link]

Check out more updates on PricingSaaS →

Subscribe now

PricingSaaS Pulse Intelligence

Here’s what users searched for in Pulse this week:

🔥 Hot Companies

• Notion — 8.9% of searches

• OpenAI — 5.6% of searches

• HubSpot — 5.4% of searches

• Figma — 5.4% of searches

• Anthropic — 3.8% of searches

🚨 Hot Topics

• AI credit models: How to structure credit-based systems for AI features

• Credit-based pricing: General frameworks for token/credit monetization

• AI features packaging: Bundling vs. add-on decisions for AI capabilities

• Good/better/best tiering: Structuring plan hierarchies effectively

• Volume pricing mechanics: Tiers, brackets, and commitment models

Get started with PricingSaaS Pulse

How to Package an Agent

Two of the biggest names in SaaS, Notion and Hubspot, launched AI agents in the past couple weeks. Both are betting that autonomous agents — not just copilots or assistants — are the next packaging frontier. While their strategies share similarities, they also represent two distinct ways to go to market with agents.

Notion: Custom Agent Add-On

Notion launched Custom Agents as a paid add-on last week. It’s available only to Business and Enterprise customers, who can trial for free, then pay on a credit-based model ($10 per 1,000 credits).

Interestingly, Notion isn’t actually launching credits until May, so Business and Enterprise customers get the next couple months to trial Custom Agents and see how it fits into their workflow.

Importantly, when credits go live, the base price of the Business plan won’t change, and Custom Agents will be a pure add-on on top of that base price.

HubSpot: The Bundled Customer Agent

HubSpot took a different approach. Their Breeze Customer Agent is bundled directly into Service Professional and Enterprise plans — no separate purchase required.

But here’s where it gets interesting. They’re offering 28 days of completely unlimited, free access to Customer Agent when you buy at least one Pro or Enterprise seat. No restrictions on messages or features during the trial window. After the trial, Customer Agent runs on HubSpot Credits at 100 credits per conversation. Some subscriptions include monthly credits, and you can buy more as needed.

By removing all friction for a month, they’re betting that once support teams see 65%+ of conversations resolved automatically (their claimed stat), the credits-based cost becomes a no-brainer.

Looking Ahead

Both approaches share similarities that we’ll likely see in future launches:

• Credits as the underlying metric

• Gated access to Business users

• Generous access to early adopters

Beyond the similarities, Notion and Hubspot offer two different ways to package agents. On one side: the Notion model, where agents are a distinct, premium add-on with transparent per-unit pricing. On the other: the HubSpot model, where agents are bundled into existing tiers to drive upgrades and upsells, with usage-based economics kicking in after adoption.

Both make sense.

Notion’s agent is flexible, and customers can scale up or down as many agents as they need. For this, an add-on model makes perfect sense.

Hubspot’s Customer Agent is purpose-built to resolve support queries. Since it ladders up to the broader value prop of Service Hub, it makes sense to bundle it into their core plans to drive adoption from existing users.

We’re going to see a lot more of these launches in the coming months. The playbook is forming in real time.

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Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and extract the ideas worth stealing.

This week, we’re digging into MCPs, and why companies are increasingly pushing MCP access into their free tiers. Since we just launched the PricingSaaS MCP, we wanted to dig into the different flavors of MCP, share how we’re thinking about it, and break down why we believe MCP access is a powerful way to drive free-to-paid conversion. First, the usual dose of pricing, packaging, and product news.

Let’s get to it.

PS. We’re hosting an Office Hours with Scott Woody, CEO of Metronome, on the latest best practices in AI pricing and packaging. Learn more and register here.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

• Hubspot offered a free trial for its Customer Agent [Link]

• Ahrefs created prompt packages for Radar AI [Link]

• Miro raised prices on the Business plan [Link]

• Monday created an Enterprise AI bundle [Link]

• Renderforest raised the price of the Business AI plan [Link]

• Acuity Scheduling tweaked calendar limits [Link]

• Pinecone launched a HIPAA compliance add-on [Link]

• Moz added AI visibility features [Link]

• Vendr restructured its plans [Link]

• FullStory launched a Guides and Surveys add-on [Link]

Check out more updates on PricingSaaS →

The MCP Freemium Strategy

When we first started seeing MCPs in the wild, it wasn’t immediately clear how companies would monetize them. Some companies gated them behind premium plans, others didn’t. But as adoption has accelerated, and AI-native workflows have become more normalized, it’s becoming clearer where MCPs fit in.

MCPs can function as a feature, a usage accelerant, or power agentic workflows, but perhaps most importantly, they’re shaping up to be a powerful distribution channel.

If you’re new to MCPs, the easiest starting point is APIs, but there’s an important difference.

• APIs allow developers to connect their product to other products, enabling their tools to work together. This makes both tools stickier by embedding each in the other’s workflow.

• MCPs allow you to expose your product to the LLMs, allowing Claude and ChatGPT users to embed your tools directly into their workflow. But they also allow the LLMs to call on your tool when a user is looking for help on a use case that you help solve. This makes MCPs a powerful discovery engine.

This post on Lenny’s Newsletter about ChatGPT apps really drove the discoverability point home for me. ChatGPT apps are built on the MCP standard, and to build an MCP, you need to equip it with tools.

For example, with the PricingSaaS MCP you can browse our pricing changes, company histories, and search our research and thought leadership to do pricing research:

The money quote from the Lenny article is that these “tools” are the new battleground for AEO. In SEO, you fought to rank on a page. In AEO, you fight to be the tool a model calls. It’s not a perfect analogy, but the “tools” in your MCP showcase your relevance in a similar way to keywords in traditional SEO.

Here’s the author of the post, :

Based on your tool’s name and description, ChatGPT can suggest your app to users to help solve their needs, like creating a slide deck or generating a financial model. Having accurate tool descriptions that uniquely identify your app will help ChatGPT users find and use your app in the correct contexts.

This distribution potential means it doesn’t really make sense to gate your MCP behind a paywall. At the very least, you should offer a free way to use your MCP to drive usage, then set guardrails to drive paid conversion.

This is exactly what we’re starting to see across some of the top players in SaaS, and we’ve observed a few different flavors.

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Flavor 1: MCP as a feature (Otter.ai)

Otter offers their MCP for free, allowing users to pipe call transcripts directly into Claude/ChatGPT.

While Otter’s MCP doesn’t directly act as a monetization lever, it enables free users to get more out of the product, which indirectly drives usage, making it more likely users trip other usage limits.

Flavor 2: MCP as a usage accelerant (Zapier)

Zapier’s MCP allows you to connect to 8,000+ apps, which is particularly useful if any of those apps don’t have an MCP themselves.

Zapier’s free plan is limited to 100 tasks per month, and each MCP tool call counts as 2 tasks towards that quota. It’s easy to see how that could drive free-to-paid conversion for users that increasingly automate workflows out of Claude.

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Flavor 3: MCP as part of an agentic workflow (Jam)

Jam.dev is like Loom for bug reporting, allowing developers to eliminate back and forth and debug faster.

Their MCP allows AI coding assistants to read complete, structured context from Jams to debug faster. Jam’s free plan is limited to 30 Jams, so similarly to Zapier, its easy to see a user blowing through that limit, especially if their team is using Claude code for debugging (a popular use case).

What we’re doing with PricingSaaS MCP

At PricingSaaS, we just launched our MCP this week, but we’ve been using it behind the scenes for the past month, and it has drastically changed how we work.

We’re using it to do rapid research projects for clients, curate weekly target account briefings for our partners, and to power our quarterly trend reports. Here’s a quick example, showing how you can use it to research AI credit models:

We’re still in beta, and don’t know exactly what pricing will look like, but we expect to keep MCP access free, and to follow a model similar to Zapier or Jam.

More broadly, there are two reasons I’d encourage any SaaS company to start experimenting with MCP:

1. An MCP helps power a credible AI story, especially if you become embedded in workflows for thousands of AI-native users.

2. As everyone scrambles to show up in LLMs, publishing an MCP (or ChatGPT app) will give you a leg up on the next big distribution channel.

We’re early — which means the opportunity is still wide open.

If you’re working on pricing and packaging research, give PricingSaaS MCP a shot and let us know what you think. If you have questions, shoot me a note at rob@pricingsaas.com.

Try PricingSaaS MCP

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

We just launched PricingSaaS MCP, giving you direct access to PricingSaaS data in Claude, ChatGPT, or Gemini. Quick video tour and how we’ve been using it below 👇

Here’s how we’ve been using it:

1️⃣ Pricing, Packaging & Product Research

Launching an AI product? Thinking through credits? Considering a price increase on your Business plan?

Tap into our data to see exactly what other companies have done — and get tailored feedback for your specific situation.

2️⃣ Competitive Intelligence

Losing track of what competitors are doing? Use the MCP to pull recent pricing changes from the companies you care about most. And if a competitor isn’t in our database yet, just let us know — we’ll add them.

3️⃣ Market Summaries

Pricing advisors and consultants are using this to provide instant benchmarks across pricing and packaging strategies for prospects and clients — a fast way to add credibility and value in any conversation.

Setup takes about 2 minutes. Follow the instructions here to connect to your LLM of choice:

👉 https://pricingsaas.com/mcp

We’d love to hear what you build with it.

And of course, if you run into any questions, just reply to this email or shoot me a note at rob@pricingsaas.com

Rob

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing, packaging, and product moves from SaaS leaders and extract the ideas worth stealing.

This week, we’re comparing two positioning updates that shift the value conversation from inputs to outcomes. One is an iterative approach from a legacy SaaS staple (Constant Contact), the other a radical approach from an up-and-coming AI player (Lindy). First, the usual dose of pricing, packaging, and product news. Let’s get to it.

PS. Next week we’re hosting an Office Hours session on Intelligent Usage Metrics from both the strategic and technical lens. Learn more and register here.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

• Stripe formally introduced its Merchant of Record product

• Auth0 made Machine-to-Machine (M2M) auth available in its Essentials plan

• Vercel launched Flags functionality in beta.

• Fireworks revamped its AI model lineup.

• Employment Hero bundled an AI Recruitment Agent in all plans.

• Supermetrics added Data Residency controls to Enterprise.

• SignalHire revamped prices across all plans.

• Jam added MCP access to all plans.

• Colossyan split video minutes across NEO 1 and NEO 2 models.

• Vanta removed the “add on” label for several Enterprise features.

Check out more updates on PricingSaaS →

Outcome Based Positioning: Choose your Speed

Historically, SaaS companies have used a familiar group of value ladders to differentiate plans and drive upgrades:

• Capabilities: Pay more as tools become more powerful and flexible.

• Volume: Pay more for higher usage and throughput.

• Risk/Trust: Pay more as risk decreases.

• Support/Service: Pay more for hands-on help.

• Automation: Pay more for less manual effort.

These still work, but agentic AI has introduced a new value ladder that is far more compelling:

• Outcomes: Pay more as work is completed for you.

While outcome-based pricing is still in its infancy, companies are starting to shift their positioning from “enabling work” to doing work for you. As Scott Galloway has said:

“The story always outpaces the fundamentals.”

This week, we saw two positioning updates at different ends of the “outcome-based” spectrum, and wanted to highlight each to show both an iterative and radical approach to product positioning in the age of AI.

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Example 1: Constant Contact

Shifted to action-oriented plan copy (Link →)

Constant Contact’s shift is a great example of the conservative approach to outcome-based positioning. Previously, their plan copy was pretty boilerplate. Each plan had “best for” language with a short description of features included in each plan.

The new positioning creates an intuitive value ladder:

• Lite: Essential Tools (Do it yourself)

• Standard: Adds pre-built automations (Do it with us)

• Premium: Adds custom automations and recommendations (Done for you)

It would be a stretch to call Constant Contact’s product agentic, but this language shifts the value ladder from pure capabilities to increased out-of-the-box actions that take more off the customer’s plate.

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Example 2: Lindy

Added a fake Human Assistant plan (Link →)

Then there’s Lindy. They added a fake Human Assistant plan to compare to their AI Work Assistant. Previously, they had a similar value ladder to Constant Contact, where plans moved increasingly closer to outcomes.

Now the outcome-based positioning is inherent in the comparison, and the actions the AI Assistant will do for you (email management, scheduling, note-taking, etc.) are listed in the Pro plan.

This is obviously aggressive, but it’s also a smart way for Lindy to communicate the value of their product. I wouldn’t be surprised to see other agentic products start openly comparing the cost of their product to the human worker they’re replacing. Sales reps at these companies are definitely already making the comparison on calls.

Choose Your Speed

The gap between enabling a task and owning a task is the most valuable real estate in SaaS right now.

Whether you take the iterative path like Constant Contact — reframing your existing tiers around service levels, or the radical path like Lindy — anchoring your price against a human salary, the goal is the same:

Stop selling the shovel and start selling the hole.

Even if outcome-based pricing is out of reach, you can start moving your story toward outcomes today. If your positioning is still stuck in the “input” era (number of seats, number of emails sent), you’re already playing from behind.

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS leaders and extract the ideas worth stealing.

This week we hosted an office hours session on one of the thorniest questions in SaaS: how to actually make PLG and SLG work together in the AI era. Below, I shared 7 key takeaways, plus the usual dose of pricing, packaging, and product changes.

Let’s get to it.

🔌 PricingSaaS Partners power the next era of SaaS pricing

This Week in Pricing, Packaging, and Product

• Zoom raised the price of AI Companion.

• Anthropic released Opus 4.6.

• Jam reduced AI Generation limits.

• Algolia added new generative features.

• Vidyard reduced free video limits.

• Triple Whale introduced AI Credits.

Check out more moves from Vercel, Frontegg, and Snyk on PricingSaaS →

This week, we hosted a live Office Hours session with Mark Walker and Tina Kung, CEO and CTO of Nue.io to answer one of the thorniest questions in SaaS right now:

How do you make PLG and SLG work together in the age of AI?

In the AI era, it often feels like we’re watching a giant game of musical chairs. Users are signing up for everything, and committing to nothing. The companies that can convert self-serve curiosity into contracted, committed revenue are the ones that will still be here five years from now.

Mark and Tina aren’t armchair theorists on this, they’ve spent decades in the trenches of subscription and revenue operations.

Mark is a serial entrepreneur who’s spent 20+ years building and scaling SaaS companies, with a particular obsession around CRM and ERP systems. He’s seen what happens when pricing and billing systems break under pressure.

Tina is the architect behind Nue’s technology. She’s built four generations of CPQ and billing systems across Oracle, Salesforce (Steelbrick), and Zuora, and led product and engineering for one of the largest CRMs in China.

Together, they’re building Nue to solve a problem they kept running into: the tools built for subscription businesses were never designed for the messy reality of PLG and SLG coexisting in the same company. Nue is now powering these motions at top players in SaaS and AI including Superhuman, Glean, OpenAI, and Chili Piper.

The Nue team recently released their Guide to Commit Burndown, which is quickly becoming the revenue model powering the AI era. I highly recommend bookmarking it for later.

The questions from our community didn’t disappoint. We had submissions from leaders at Monday.com, Zoom, and Typeform. Here’s what we learned 👇

Takeaway #1: The PLG/SLG binary is false.

Most companies will operate in both motions simultaneously, and the customer experience should flow seamlessly between them.

Both Mark and Tina argue that the framing of PLG vs. SLG as two separate worlds breaks in practice. Their advice? Design for fluid movement between motions. Mark went further, breaking down why the distinction doesn’t hold on either end.

The distinction between PLG and SLG was never real anyway. Very few companies reached massive scale on pure PLG. Eventually, either customers’ CFOs want control, or you start dealing with enterprise tiers that require security reviews and formal contracting. AI is accelerating this. Data concerns and auditing tools will force SLG conversations. On the other end, the PLG/SLG boundary breaks down post-sale as well. Enterprise customers, once contracted, generally don’t want to talk to a salesperson again for routine expansion. They want self-serve interfaces for adding volume or understanding consumption.

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Takeaway #2: Flexibility is the meta-strategy.

In the AI era, product roadmaps, pricing models, and customer needs change too fast for rigid packaging. Build for adaptability.

Mark pointed to the leading AI companies as proof that flexibility in packaging is the defining strategic priority. Nobody knows what future pricing will look like, so the infrastructure must be designed for constant change.

Companies shouldn’t pretend they know what future pricing will look like. The AI leaders: OpenAI, Anthropic, Cursor, Harvey, Anaconda — all adopted flexible systems precisely because they don’t know what the pricing and packaging mix will be in the future. That humility is the strategy. Build packages and contracts structured to deliver increasing value without requiring re-contracting. The message to customers should be: we’re going to be dedicated to bringing more value to you every month, every quarter, and this contract is structured so you can grow as fast as possible without going through a re-contracting process.

Takeaway #3: Credits and committed spend are the bridge.

They preserve PLG simplicity while enabling SLG complexity, and let customers gradually adopt new services without re-contracting.

Credit-based models were a focus of the session, with good reason — PricingSaaS data showed 126% YoY growth in credit models in the past year. Tina gave historical context for credit models in SaaS, and how AI companies are giving it a new spin.

Snowflake’s journey is the canonical example. They innovated the credit burn-down model to create a common unit across compute, storage, and API usage. In the modern AI era, the evolution is toward committed spend — you commit an amount and pay as you go rather than prepaying a lump sum. This lets customers gradually adapt while giving sellers predictable ARR. The committed spend can continue from PLG subscriptions and gradually incorporate more services: subscriptions, AI tokens, professional services, FDE (Forward Deployed Engineers), with no big contract required upfront. Customers commit more or less depending on service needs, with pricing that appears simple on the surface.

Takeaway #4: Professional services are a growth lever, not a cost center.

Embed them in committed-spend contracts, make them self-serviceable, and think of them as usage products.

Mark and Tina share a contrarian perspective on professional services. Instead of a gated implementation requirement, they view professional services as a flexible, revenue-generating product.

Think of professional services as a usage product. It has a price, and can be structured in hour blocks. An emerging pattern is the “go-to-market engineering” function, where professional services is the sales team. They go in, configure and deliver the solution, and that installed solution then becomes the PLG expansion vector. You need professional services to get the product installed and change business processes before self-serve expansion can take hold. Major AI companies are embedding professional services inside committed-spend contracts. Customers don’t have to re-contract, they just raise their hand and get billed against the existing agreement.

Tina elaborated — highlighting why professional services are becoming a necessary complement to rapidly evolving products.

In the AI era, selling a solution is not a one-time event — it’s ongoing. Products update constantly, and managed services or FDE motions need to be self-serviceable. Customers should be able to add professional services hours on demand for new product rollouts or consultative support, without renegotiating. Ideally, they are bundled alongside subscriptions and consumption — creating a unified revenue model that includes one-time, milestone-based, and recurring billing — rather than being a separate gated engagement.

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Takeaway #5: Don’t cancel-and-replace.

Whether transitioning a customer between motions or introducing new pricing, preserve continuity. Friction kills expansion.

Tina argues that preserving the customer’s existing subscription and experience is tablestakes to a frictionless transition between PLG and SLG.

PLG customers are accustomed to transparent, simple pricing. When moving to SLG, pricing gets more complex — multi-year contracts, discounts, professional services add-ons — but the challenge is maintaining that sense of transparency and simplicity even as complexity grows underneath. From the customer’s perspective, they shouldn’t feel like they’re canceling one subscription and replacing it with a completely different one. And because there may be a motion to move back, the back-and-forth should be very frictionless.

Mark went even further, highlighting the risks of a poor pricing model or billing process when converting PLG customers to higher plans.

Customer dissatisfaction with the pricing model or billing process — not the software itself — is one of the main drivers of churn. Offering different value-aligned pricing options, rather than forcing a switch, addresses this directly. There’s a key difference between the absolutist approach (stay on old pricing or switch entirely to new) versus the incentive approach (old model has limited features at higher cost, new model offers more value with better economics). The second approach creates a re-engagement opportunity rather than a disruptive migration.

Takeaway #6: Involve finance early.

The CFO’s perspective on revenue recognition, SKU management, and reporting should shape packaging decisions from the start.

Revenue recognition, reporting, and billing complexity all need to be factored in before new models ship. If not, Mark says, you’ll end up with a laundry list of SKUs that need to be normalized later in reporting.

Talk to your CFO. A big issue is whether these are actually different products or different pricing plans on the same product. You want the same product in different offerings, maybe with a completely different pricing model, but with revenue going to the same place. Otherwise, you’ll configure your financial system backwards and create more complexity.

Takeaway #7: System architecture matters.

Disconnected billing, CPQ, and self-service systems are the bottleneck. Unified platforms are required to execute these strategies.

The final takeaway is that none of the preceding strategies work without the right system architecture. Tina argues the PLG-to-SLG problem is fundamentally an infrastructure problem.

Revenue recognition naturally starts from how you price and assign revenue models for your products. It starts from products and pricing, flows through quoting, billing, and rev rec — these must be connected and unified. PLG and SLG have very different metrics (PLG tracks conversion and usage; SLG tracks ARR and ACV), and the analytics layer must unify these across channels. When PLG and SLG have different billing systems (e.g., Stripe for PLG, another for SLG), the entire ecosystem needs unified traceability and analytics. Without this, you lose clarity on what’s driving revenue across your channels.

Mark elaborated further on the pitfalls of disconnected systems:

Land-and-expand fails when billing, self-service, and CPQ systems are disconnected. You can’t easily offer “included-but-measured” features when three separate systems need to agree on the rules. Companies are switching to unified platforms because they literally couldn’t execute their pricing strategy with disconnected tools.

That’s all we’ve got for today. Thanks again to Mark and Tina for sharing their time and insights. For further reading, check out Nue’s Guide to Commit Burndown.

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• Community: Answers from real pricing experts. Apply for membership →

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• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS & AI leaders and extract the ideas worth stealing. This week was a busy one. We observed ~100 updates across pricing, packaging, and product.

Keep reading to go deeper on 10 of our favorites, with concrete examples and commentary.

Our Partners power the next era of SaaS pricing

We’re lucky to work with exceptional companies powering the next era of SaaS/AI pricing infrastructure and strategy. Visit our partner portal to learn more.

Next Thursday, we’re hosting Office Hours with Mark Walker and Tina Kung, CEO and CTO of Nue.io to answer questions on balancing PLG and SLG in the AI era.

Register to join

What happened in pricing and product this week?

In short — quite a bit. Across 100+ pricing, packaging, and product updates, the key trends we observed were:

• Price increases (Canva, Hootsuite, FreshBooks)

• Promotional Cycle Cleanup (Aircall, Gusto, Quicken)

• Product Additions (Datadog, Fivetran, Stripe)

• Plan Restructuring (Dovetail, Unbounce, Imgix)

• Capacity Increases (Synthesia, Renderforest, Zoho)

Highlighting 10 favorites below. Check out the rest at PricingSaaS.com.

1. DataDog introduced Feature Flags (Link →)

Feature flags seems like a natural fit alongside the rest of Datadog’s products (they have an extensive product catalog), and gets them closer to offering a true end-to-end DevOps platform.

From a monetization standpoint, it feels like a solid expansion play and gives them an even stronger consolidation narrative. They’re letting customers use up to 1M flag configuration requests per month for free too, making it easy to give it a trial run.

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2. Lovable added 6 new features to Business and Enterprise (Link →)

Lovable is building the scaffolding to support Enterprise customers. Security and Role-based access with Admin controls are tablestakes for Enterprise buyers who need assurance around security and compliance. From a monetization lens, it’s clear they’re going after higher contract values and bigger deals.

This is no surprise, as they’ve been ramping GTM hiring, and have massive upside to capture what should be a PLG → SLG expansion story for the ages.

3. Claude continued to democratize feature access (Link →)

Anthropic is on a warpath to give away as much Claude functionality for free as they possible can. Every week, it seems like they’re moving new features down to lower tiers to provide more access to users. The more they can eliminate friction on coding tools and integrations with legacy tools like Excel, the stickier Claude becomes.

They’re also on a warpath against ChatGPT Ads. If you haven’t seen the commercials, they’re very funny.

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4. Zapier clarified Copilot limits for Free vs Paid (Link →)

Zapier was already offering Copilot access to Free users, but this week they made it clear how they’re differentiating Free vs Paid.

Free users get daily message limits (similar to Lovable’s daily credit limit for Free users), while paid users get unlimited messages. It’s a smart way to give access (without giving away too much) in what’s becoming a very competitive market for AI-native and agentic workflows.

5. Dovetail consolidated plans (Link →)

We’ve seen a few consolidations like this lately, where a company has removed a middle tier. My guess is that the Professional plan never really fit Dovetail’s ICP, and getting rid of it provides clarity and focus.

With the move, they’re pushing smaller teams to self-serve free/lower tiers, while funneling serious buyers into sales-assisted Enterprise conversations where deal sizes and expansion potential are higher. This will likely help them drive higher ACVs, and make GTM/Customer Success teams more efficient.

6. DigitalOcean introduced a Managed Hosting add-on (Link →)

DigitalOcean acquired Cloudways in 2022, and with the move they’re officially welcoming Cloudways into the DigitalOcean family.

Integrating Cloudways makes managed hosting a native experience, simplifying the product journey for DigitalOcean customers. Revenue-wise, it’s a move to double-down on simplicity for SMBs.

7. Canva raised prices on Pro and Business (Link →)

Canva wasn’t the only establish player to raise prices this week, we also observed price increases from Hootsuite, Freshbooks, and Bitwarden.

Given the public market negativity around SaaS this week, it’s refreshing to see companies raise prices with confidence.

8. Unbounce added 2 new plans (Link →)

Unbounce added a Starter plan for beginners, and a higher tier called Concierge that includes implementation services, and a dedicated CSM.

They now offer 6 plans, which feels like a lot. It’s unclear how necessary the 3 middle tiers are (Build, Experiment, Optimize), but I like these two additions because they have clear jobs. Starter keeps the funnel wide at the bottom, while Concierge maximizes value at the top.

9. Synthesia boosted usage limits (Link →)

Synthesia boosted usage limits on both ends. On the free side, they tripled the credit limit, which is an interesting competitive move in the wake of ElevenLabs tightening usage limits across the board. On the higher end, they added value by beefing up stock AI avatars and language options.

10. Fivetran added a new data activations product (Link →)

Fivetran introduced a new product called Activations. Fivetran already brings data into warehouses, and with Activations, customers can now move that data back into the tools their teams use. This allows them to pitch a true platform play where they own the full data movement stack (in and out), and makes them much sticker. It’s also a compelling upsell for existing customers who are already using Fivetran to bring data in.

From a pricing and packaging lens, Activations is priced based on destinations and monthly active rows, which gives Fivetran new usage metrics to incorporate into their existing plans.

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS leaders and extract the ideas worth stealing. This week, we observed five ways to reframe your pricing and packaging.

TLDR:

• Klaviyo reframed its pricing page to drive acquisition

• Lindy reframed the Enterprise plan around its AI Assistant

• Eden AI reframed Professional Services as an Advanced AI Platform

• DeepBrain reframe the default commit from monthly to annual

• Hugging Face reframed the value of Data Storage

Keep reading to go deeper on each idea, with concrete examples and commentary.

PS. We also released our latest Trends Report. We tracked 500 pricing pages for two years, and unpacked our biggest findings. Get the full report →

🔌 PricingSaaS Partners power the next era of SaaS pricing

Need a sounding board? We’ve helped hundreds of teams figure out their pricing strategy and stack. Book a free Pricing Review →

What happened in pricing and product this week?

1. Klaviyo reframed its pricing page to drive acquisition (Link→)

Klaviyo updated the pricing page to deprioritize the pricing calculator, and highlight the free plan. This suggests they’re making a push to drive acquisition, and use a PLG/SLG combo to help free customers get the most out of the product, before expanding the account.

What you can steal: This goes both ways, whether you’re focused on adoption or monetization, the layout of your pricing page can support either. Previously, the free plan was an afterthought for Klaviyo. That clearly changed, and this update should push more visitors to get started for free.

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2. Lindy reframed the Enterprise plan around its AI Assistant (Link→)

Lindy launched a personalized AI Assistant, and quickly made it the centerpiece of the Enterprise plan. They did this by adding the AI Assistant as the first Enterprise feature in the main pricing menu, and by updating the banner on the Enterprise plan from “Unlimited Phone Calls” to “AI Assistant for Teams.”

What you can steal: Volume has always been a key differentiator for Enterprise plans, but “work completed” is an even more compelling value prop. This reframe creates a new value ladder for Lindy where the more you pay, the more work the product does for you, shifting the value for Enterprise customers to outcomes instead of more inputs.

3. Eden AI reframed its Professional Services offering (Link→)

Eden AI previously positioned its higher tier strictly as Professional Services. They’ve since reframed it as an Advanced AI Platform, with Professional Services being an important ingredient.

What you can Steal: The lines between services and software are blurring. Eden was already building and maintaining a more complex environment for these customers, clearly going beyond pure services. Historically, similar services have been billed as a one-time implementation or onboarding fee. Because of that, I wonder if Eden ran into friction for recurring professional services payments. This reframe is a way to position a true combo of SaaS + Services, drive recurring revenue, and ensure they can charge a premium for any human-intensive solutions they’re providing.

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4. DeepBrain reframed the default commit to Annual (Link→)

We’ve been talking about credits a lot lately, and its clear buyers are starting to get tired of credit complexity. That complexity can include vague definitions, unclear expectations on how credits are drawn down, and ops around rollovers and top-ups.

What you can steal: Making Annual the default does a few things. It anchors visitors to the annual credit allotment (a bigger number), it sets the expectation that annual is the most common approach, and it gives the sales team a talk track around credit flexibility by granting all annual credits upfront. If you’re shifting to a credit model, keep this in mind.

5. Hugging Face added a new Data Storage add-on (Link→)

Hugging Face prioritized the placement of the Data Storage add-on just below Hugging Face Hub, making it clear that it’s a focus. This makes sense, as Data Storage can be a bottleneck for AI/ML workflows. Adding Data Storage also makes Hugging Face stickier, and gives them another recurring revenue layer.

What you can steal: Similar to Klaviyo’s move, pricing page order and flow show a visitor your priorities. If you have a new product you want to prioritize, give it prime real estate.

Elsewhere…

• Joist expanded payment options.

• Jotform raised form view limits.

• Imgix expanded bandwidth and storage limits.

• Fiddler added a Developer plan.

• ExpressVPN reduced discounts on the Alcove router.

Check out more moves from Mailchimp, Gusto, and Checkr on PricingSaaS →

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

We just released our latest Trends Report, breaking down the top patterns we saw in 2025. The biggest takeaway? SaaS leaders are increasingly focused on packaging.

Here are a few highlights:

• Plan additions were the most popular packaging event

• Credit models surged 126% YoY

• AI feature share of the product roadmap is up 4x from Q1 2024

Inside you’ll find concrete examples from Anthropic, Atlassian, Figma, HubSpot, and more.

Get the Report

A big thanks to our partners for making this research free for the community 🙏🏻

John + Rob

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS leaders and extract the ideas worth stealing.

In a recent Office Hours session, Metronome CEO Scott Woody called this one of the most customer-friendly times in SaaS history. We’ve been keeping an eye out for this trend, and wanted to share 5 examples you can borrow for your own roadmap.

TLDR:

• Meet customers where they already are.

• Take the pricing objection off the table.

• Help customers offer flexibility to their customers.

• Dramatically increase usage limits.

• Make Enterprise pricing more transparent.

Keep reading to go deeper on each idea, with concrete examples and commentary.

PS. We’re at almost 1,000 members in the PricingSaaS Skool Community. If you haven’t joined yet, it’s totally free, and you’ll get access to regular events with pricing experts and operators. Join the fun here 👉🏼

🔌 PricingSaaS Partners power the next era of SaaS pricing

Need a sounding board? We’ve helped hundreds of teams figure out their pricing strategy and stack. Book a free Pricing Review →

5 ways to be customer-friendly

1. Meet Customers where they are (Pylon).

By adding connectors for WhatsApp and Telegram, Pylon is evolving from “Slack-native support” into a customer communication platform optimized for modern, global, community-driven companies. By offering it in their Professional plan, they’re making it a core part of their offering, not gating it behind Enterprise.

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2. Take the pricing objection off the table (Gusto).

Last week, Gusto introduced a banner at the top of their website offering qualified switchers a price match. This is definitely a tactic that happens behind the scenes in sales all the time, but making it a formal offer is rare. When we have seen it, it’s usually for lower ACV SaaS products (e.g., beehiiv).

3. Help customers offer flexibility to their customers (Freshbooks)

Freshbooks added new options for payment across all plans, including Apple Pay, Google Pay, Pre-Authorized Debit, and Buy-Now Pay-Later.

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4. Dramatically increase usage limits (Ahrefs)

Ahrefs increased social media feature limits to a comical degree. It’s hard to image a customer having more than 1,000 social channels or tripping 10,000 posts per month. Social Media Management isn’t the core use case for Ahrefs, and doesn’t make much sense as a threshold to drive monetization. This adjustment ensures customers can comfortably test their social media management features without running into limits.

5. Make Enterprise pricing more transparent (Zuplo)

This is a move we’ve advocated for before. Revealing Enterprise pricing isn’t binary. Using a “Starts at” price gives prospects a ballpark, while preserving customization for your Sales team based on what the customer needs.

Elsewhere…

• Zapier rebranded Interfaces as Forms.

• Wrike adjusted messaging across all plans.

• Grafana launched an AI copilot.

• Skyvia added data integration thresholds across all tiers.

• Checkr removed its self-serve option.

Check out more moves from StreamYard, HubSpot, and GitGuardian on PricingSaaS →

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS leaders and extract the ideas worth stealing. Today we have 5 fresh pricing changes:

• Slack introduced an AI Agent

• Anthropic pulled its Memory in Conversations feature into Pro

• Auth0 simplified Enterprise connections

• ElevenLabs adjusted limits and raised prices

• AiSDR lowered meeting output expectations

Links to those changes below. Before we get there, I wanted to share notes from a recent read that I highly recommed: You’re Leaving Money on the Table, by Schematic CEO Fynn Glover.

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Over the past year, there’s one person I’ve sent more SaaS leaders to than anyone else when they have questions about their monetization stack:

Fynn Glover, the CEO of Schematic.

Fynn is a founder and operator himself. He’s seen the impact of pricing from multiple angles. At Schematic, he’s building something that solves one of the most pressing problems in SaaS: enabling companies to rapidly iterate on pricing and packaging.

There’s a lot of talk about the right pricing strategy. But if you don’t have the right pricing architecture, your strategy is irrelevant.

We’re entering a season in SaaS where rapid pricing iteration is becoming a core competency. Last year, we saw 1,800 pricing changes across the 500 companies in the PricingSaaS index, and the fastest growing companies (e.g., OpenAI, Lovable) are updating pricing multiple times per month.

If you aren’t used to iterating on pricing, that likely feels daunting. Lucky for you, Fynn just wrote a book. It’s called You’re Leaving Money on the Table.

I read it earlier this week. It’s a quick, concise, and actionable read that breaks down how to make pricing work for you. It should be required reading for SaaS founding teams.

The book is full of gold, but my favorite takeaway is the idea that how you package is how you sell, and I want to unpack that with 3 core ideas from the book below.

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Idea 1: PLG and SLG don’t compete. They complete each other.

The best SaaS companies today run both motions: PLG to acquire users, SLG to monetize and retain them. The two aren’t in tension; they’re complementary.

But making them work together is one of the biggest challenges in pricing and packaging design — it gets at what people mean when they say they want a pricing model that scales. Fynn’s perspective is that this is inherently a systems problem:

For PLG and SLG to work together, your systems must connect product behavior to pricing behavior. To do this, you need deep telemetry: data that shows who’s using what, how often, and how that usage correlates with revenue.

Historically, companies have started with one or the other. Maybe they started with PLG (e.g., Dropbox, Canva) and layered Sales on later. Or they started Sales-Led (e.g., HubSpot, Adobe) and layered a Product-Led motion later. Either way, if you only start with one, nailing the other is hard.

That’s why you should start with both. At the core of getting it right is your pricing metrics. First, you need to choose the right ones, then you need to set thoughtful limits.

Idea 2: Pricing metrics must pass two tests.

When choosing what to charge for, Fynn lays out two important tests:

1. The Customer test: if the chosen value metric scales, would the customer feel that you have earned an increasing share of their wallet?

2. The Founder Test: Can a new sales rep explain the metric in 15 seconds without slides? If not, it will create friction at checkout and in procurement.

As an Account Executive, I’ve lived both sides of this. At HubSpot, we had a pricing metric that sales could explain and customers could understand (Contacts). This was incredibly powerful for sales and allowed us to tell a clear story. Importantly, we had features differentiating each plan that aligned with the sophistication needed at different contact levels.

After leaving HubSpot, I worked at a company that sold email testing software. We went from a flat fee to reverse-engineering a value metric to unlock usage-based expansion. I saw firsthand how brutal it is to sell a value metric that didn’t previously exist, especially to existing customers. Without much feature differentiation, customers would simply reduce their usage to fit within the lowest tier.

In my experience, people make pricing metrics harder than they have to be. Answer the two questions above with a “yes” and you’re in good shape. Importantly, this should apply to not just your primary pricing metric, but secondary usage metrics as well. Secondary metrics are some of the most effective ways to drive upgrades.

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Idea 3: Limits are the connective tissue of GTM.

Handled well, limits create a seamless bridge from self-serve to enterprise. Here’s Fynn again:

A limit is the invisible boundary between free and paid, between product and sales, between usage and upgrade. When a customer approaches a limit, you have a signal. When they surpass it, you have an opportunity.

Take Zoom and Slack. Both charge for Users as the primary metric, but in the free plan, they limit meeting duration and message history respectively. The limits on these metrics connect the dots between PLG and SLG.

With the right systems, limits power important actions across product and sales. Some examples:

• When users near a usage limit, the product automatically notifies them.

• If they hit the limit, perhaps the product triggers a paywall.

• In parallel, the sales team gets an alert: “This account just maxed out their limit.”

In this way, usage-based pricing isn’t just a strategy decision. It’s a technical capability. The two are inseparable. Think about the recent rise of credit models. Offering a credit model without visibility to usage would lead to uncertainty, and likely reduce usage as customers worry they might trip their credit limit. By improving your pricing architecture, you not only empower your sales team, you create a better experience for customers.

Wrapping Up

Patrick Campbell used to say that most companies spend more time choosing the toilet paper for their office bathroom than they spend on pricing. That’s still true for a lot of teams, but it’s no longer viable. AI has fundamentally transformed SaaS pricing dynamics. While there’s no perfect pricing model, the only certainty is that you need to be able to evolve with your product and market.

The only way to do that is to get comfortable iterating on pricing. Fynn’s book is the best starting place I know to get the ball rolling.

Read it here: You’re Leaving Money on the Table

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Elsewhere…

• Slack launched Slackbot, a personal AI agent on Business and above.

• Anthropic added Memory across Conversations to the Claude Pro plan.

• Auth0 introduced Express Configuration for Enterprise Connections.

• ElevenLabs cut included minutes and raised the price of additional minutes.

• AiSDR reduced the expected output of the Grow plan by 27%

Check out more updates from Ahrefs, Amplitude, and Superhuman on PricingSaaS →

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing and packaging moves from SaaS leaders and extract the ideas worth stealing. Below are 5 pricing and packaging ideas from the past week you can borrow for your own roadmap.

TLDR:

• Use billing cycles to filter for commitment when time-to-value takes work

• Shift limits from users to usage metrics that map closer to value

• Offer flexible add-ons

• Default to self-serve onboarding and monetize human support

• Raise prices across the board, but raise the entry tier more to drive upgrades

Keep reading to go deeper on each idea, with concrete examples and commentary.

PS. We’re hosting Office Hours with pricing expert Ulrik Lehrskov-Schmidt next Friday to talk all things Enterprise pricing.

👉🏼 Sign up here and drop your questions in the thread.

🔌 PricingSaaS Partners power the next era of SaaS pricing

Need a sounding board? We’ve helped hundreds of teams figure out their pricing strategy and stack. Book a free Pricing Review →

5 Pricing Ideas to Steal This Week

1. Test Quarterly Billing (AiSDR)

AiSDR shifted from monthly to quarterly billing. On the surface, it runs against a broader trend we’re seeing toward optionality for customers, but in this case (and for agentic products generally) it makes a lot of sense.

AiSDR’s value isn’t instant. There’s a learning curve, upfront setup, and hands-on help from their team to get customers ramped. Front-loading all that work only to have a customer churn after a month is brutal.

The shift to quarterly billing acts as a filter to ensure there’s a level of commitment from the customer, and gives AiSDR more time to deliver value.

👉🏼 Steal this: If you offer an agentic product with FDEs or hands-on support, monthly billing probably doesn’t make sense.

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2. Remove Users + Tighten Usage Limits (Arize)

Arize removed user limits on its Pro plan, while tightening limits across more relevant usage metrics. User limits were never a great fit for Arize. Observability tools are inherently collaborative and capping users disincentives adoption.

By removing user limits, Arize made it easier for teams to add stakeholders to the product. More users leads to more traces, higher ingestion, and faster paths to hitting real usage thresholds. It’s a move to drive both activation and monetization.

👉 Steal this: This is one of those cases where seat-based pricing doesn’t make sense. If the value of your product lies closer to other metrics, consider removing seat-based caps, and adding friction in places that are closer to value.

3. Introduce a flexible add-on (DeepL)

DeepL bundled its Write ProAI add-on into the Business plan, but didn’t fully kill the add-on. Individual and Team customers can still purchase Write Pro AI separately.

This is a unique application of the optionality trend we’re seeing, and also shows how DeepL and others see AI fitting into their product (Notion and Loom also bundled AI add ons into Business tiers).

For individuals and small teams, the add-on remains a low-commitment way to test AI-powered writing and understand where it fits into daily workflows. For Business customers, AI is standard.

👉 Steal this: Consider offering a flexible add-on that comes standard with higher tiers, but gives users on lower tiers a chance to discover value.

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4. Rethink Default Onboarding (Deputy)

Deputy changed default onboarding from personalized onboarding to self-serve. At the same time, it introduced paid onboarding as an option for annual customers.

Couple things at work here:

1. AI is increasingly making product-led onboarding a more viable option

2. This ensures high-touch resources are spent with customers who’ve shown commitment.

👉 Steal this: Default to self-serve onboarding, and leverage human support to drive deeper commitment (and incremental monetization).

5. Move the whole ladder (Freshworks)

Freshworks raised prices across all three plans, but not evenly. The Growth plan went up ~27%, while Pro and Enterprise increased closer to ~12%.

In our Pricing Change Log, we found the average same-plan price increase fell around 20% in both 2024 and 2025, and these changes are equidistant on each side of that benchmark. Freshworks clearly believes the full product is worthy of a higher price, but seems to want to push more customers towards Pro.

👉 Steal this: When raising prices, move the whole ladder. Use larger increases at the entry tier to drive upgrades, but don’t freeze your higher tiers if your best customers can pay more.

Elsewhere…

• Ahrefs upgraded Project Boost Max with unlimited AI URL detection

• Browserstack expanded Enterprise idle timeout from 10 to 45 minutes

• Datadog introduced dataset and connection limits to Pro and Enterprise tiers

• Intercom added its 90% startup discount with free year of Fin AI agent to page

• Klaviyo extended introductory pricing.

Check out more updates from JazzHR, HubSpot, and Dashlane on PricingSaaS →

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →

Each week, we break down real pricing moves from SaaS leaders and extract the ideas worth stealing. Below are 5 pricing and packaging ideas from the past week you can borrow for your own roadmap.

TLDR:

• Use AI limits to quietly reshuffle plan value

• Revisit your plan mix to see if you can simplify

• Test a value-specific banner for your most popular plan

• Monetize seasonality instead of fighting it

• Discount with usage, not price

Keep reading to go deeper on each idea with real examples.

PS. The PricingSaaS Community is a free space for founders, PMs, and pricing leaders to:

• Pressure-test pricing changes before rolling them out

• Learn from real examples shared by other SaaS teams

• Join live roundtables, AMAs, and weekly Office Hours

It’s practical, opinionated, and focused on real decisions, not theory.

Join the free PricingSaaS Community

🔌 PricingSaaS Partners power the next era of SaaS pricing

Need a sounding board? We’ve helped hundreds of teams figure out their pricing strategy and stack. Book a free Pricing Review →

5 Pricing Ideas to Steal This Week

1. Adjust AI Limits (Renderforest)

Renderforest made a series of moves to differentiate the Pro plan from the Pro AI plan. Combined, these moves suggest they’re looking to drive conversion to Pro and change how they differentiate AI across plans. Here’s what they did:

• First, they reduced the discount on Pro AI from 50% to 20% effectively raising the monthly price from $19 to $30. Notably, they kept the discount for the Pro plan at 50% — making it look more appealing.

• Second, they doubled limits on the Pro plan for AI Credits, which gives users more access to AI videos and Images. By holding the limits on the Pro AI plan the same, they further increased the appeal of the Pro plan.

• Lastly, they doubled the limits on AI Text-to-Speech on the Pro AI plan, creating a new differentiator between Pro and Pro AI.

These changes signal a push towards adoption for the Pro plan, and a push towards monetization on the Pro AI plan. I’d argue they should take this further, boosting the AI usage limits for the Business plan and raising its price to make Pro AI look more compelling as well.

👉🏼 Steal this: Use AI limits and prices to quietly prioritize your plans and reshape where customers land.

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2. Consolidate Plans (Clarifai)

Clarifai reduced its plan mix, consolidating from 5 plans down to 2. They replaced the Community, Developer, Essential, and Professional plans with a single Pay-as-you-Go plan. The update removes all usage thresholds and monthly commitment requirements, and drastically simplifies the decision-making process for new customers.

Along with being more customer-friendly, the new model is a better fit for Clarifai’s position as an AI infrastructure platform.

👉🏼 Steal this: Review your plan mix to see if there’s an opportunity to simplify and make it more customer-friendly.

3. Add a Value-Driven Banner (NordVPN)

The “Most Popular” or “Recommended” banner is a staple of SaaS pricing page design, but NordVPN added an interesting spin. The banner for the Plus plan changed from “Most Popular” to “Anti-Malware & VPN.”

This is a thoughtful way to use every inch of pricing page real estate purposefully, and will be interesting to see if it drives higher conversion for the Plus plan.

👉🏼 Steal this: Test a new spin on your banner to clearly position a plan around a specific value prop.

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4. Offer Seasonal Seats (Taxdome)

Taxdome offers practice management for Tax and Accounting firms, who need flexibility with seats as they staff up during busy season.

On the Pro plan, customers could already add temporary staff at $100/user/mo. Now Business plan customers can add Seasonal Seats for $500/user for 4-months.

I’ve talked to several operators in seasonal industries (e.g., landscaping, home services) looking for creative ways to offer flexibility to customers. Both options from Taxdome are worth a look.

👉🏼 Steal this: Offer flexible seat options to serve customers that don’t need to pay for full seats year-round.

5. Get Creative with Promotions (Anthropic)

Anthropic boosted usage limits for paid users through the holidays. This functions as a discount for existing customers, and is a smart way to drive goodwill and retention.

👉🏼 Steal this: Look for creative opportunities to add value to existing customers without impacting the price on their contract.

Elsewhere…

• Anthropic added Claude in Excel to the Max Plan (Link →)

• Wrike added a Whiteboard feature for $15/mo (Link →)

• Unbounce raised prices on the Build plan by 90% (Link →)

• Salesforce shifted Marketing Intelligence to per-org pricing (Link →)

• Monday.com removed the Basic plan (Link →)

Check out more updates from DeepL, HubSpot, and Groq on PricingSaaS →

Thanks for reading! When you’re ready, here’s how we can help:

• Community: Answers from real pricing experts. Apply for membership →

• Data: Access to news, research, and analysis. Sign up for free →

• Advisory: Free pricing advice. Book a 30-minute call →