How to Build a Modern SaaS Pricing Org: Lessons from Sam Lee (HubSpot, Snowflake, ServiceNow)
Plus: Updates from Wistia, Dashlane, Deepbrain
Welcome back to Good Better Best!
Between Sam Lee and Ulrik, this issue is jam-packed with pricing wisdom. Before we get there, a quick bit of housekeeping.
We’re partnering with Ulrik and the Willingness To Pay team to create an accessible pricing service for B2B SaaS teams that won’t break the bank. We’re actively gathering feedback. If you’re interested in learning more, reply to this email directly or shoot me a note on LinkedIn.
On to this week’s post.
📰 Pricing News & Updates
Wistia reorganized plans and added features.
Dashlane launched an Enterprise platform.
Deepbrain added features and usage thresholds.
The proven playbook for shifting to usage-based pricing
Today’s post is brought to you by Metronome. Metronome is the world’s leading usage-based billing platform, trusted by companies like OpenAI, Anthropic, and Confluent to launch new products and pricing faster.
Thinking about moving to usage-based pricing? This playbook breaks down the proven steps to make the shift—from aligning your org to avoiding common pitfalls. Learn how companies like OpenAI and Databricks made the transition, and get tactical steps to set your team up for success.
Lessons from a Pricing Leader: Sam Lee, VP Pricing and Product Ops at HubSpot
This week, the PricingSaaS Community was lucky enough to host Sam Lee, VP of Pricing Strategy and Product Ops at HubSpot. Sam is one of the most thoughtful people I’ve met in the SaaS world, and is incredibly fun to jam with on pricing.
The first time we chatted live, I was blown away by how holistically he thinks about pricing strategy, including its impact on hiring, compensation, and technical resourcing. He was kind enough to jump into the community and answer wide-ranging questions about pricing org structure, credit models, AI-agent pricing, and more.
Check out the highlights below.
Q: Credit models are having a moment. Curious if you have any advice for implementing a credit model given your experience at Snowflake?
I like to start with the question of understanding what you’re solving for with credits, and focus on the value metric and understand how value accrues to customers, and how that ultimately can/should be measured, and charged.
The framework I’ve used and am using at HubSpot is:
Identify the Value Metric: Features drives customer usage, customer usage creates customer value → what is the value metric that best represents customer value?
Choose the right Usage Metrics: Decide which usage metrics are the best proxy and representative of the value metric - ideally the usage metric (i.e. what you charge) should scale with value and scale with COGs.
Define the Credit Model: Layer on the credit model and the pricing mechanics that are associated with the credit model as a pricing / abstraction and GTM enabler to help drive certain outcomes or solve for the sales motion.
Q: How do you predict “AI employees” will be priced? Will it be comparable to paying someone a salary, or more outcome-based?
This is a really hot topic right now. Here are some talking (thinking) points on where my head is at (in no particular order)…
The key to value capture in “AI employee” is to capture labor or HC budget. HC budgets are orders of magnitude more than software at every company so we need to make it easy for customers to understand that tradeoff and also for sellers to articulate that value.
Treating AI employees like an FTE is not possible in a traditional sense - there is no time or even speed constraint on AI employees - an AI customer agent can work 24/7 and (in theory) only constrain the speed in which the end user can interact with it. So if we’re talking about pricing an AI employee, we’re not talking 1:1 to human employee, but some kind of proxy for “FTE equivalent” - which I think will at the end of the day be made up of different jobs and tasks.
The question of how it will be priced is almost like a packaging decision - how do you “bundle” a bunch of jobs and tasks capacity into something that resembles, or can be discussed with customers, that lend the conversation to go after the larger HC budget?
I’m not entirely sure where it goes. I can see (and I think some companies are experimenting now) some kind of big flat subscription fee (hire fee) for an AI employee that is based on capacity to do certain amounts of work. On the usage front you can easily tag on “overtime” as a concept for usage beyond the subscribed capacity.
But yeah, I think performance, outcome, output, and capability will all play a role in pricing AI employees. Just like today you would pay an entry level analyst differently than a staff analyst. Similar types of price discrimination will be at play with AI employees.
Q: When building a pricing function inside a scaling SaaS company, how do you recommend structuring the pricing team within the broader org?
I’ve always structured pricing strategy team under a “tripod” model:
Product Monetization - This is the “bread and butter” of pricing, and the team that works with the product managers to set price, bundles, packaging, etc. I sometimes call this “inbound pricing” because this team is really focusing on the product value and value capture of products and services
Commercial Pricing Strategy - This would encompass pricing programs (ex. ELA, partner pricing, etc.), discounting, and other commercial terms that impacts how customers interact with the “price” - I sometimes call this “outbound pricing” as its focus is on how price lands in the different market segments, with partners, sellers, etc.
Governance, Analytics, Operations - This is like the back office function - which includes the governance and policies management of how the company makes pricing decisions. tracking, forecasting and readout of relevant KPIs and business performance as it relates to pricing. And finally, the way the company operationalizes pricing decisions - how it flows into different teams, systems, etc.
In terms of prioritization, Monetization (1) is usually the top priority. Followed by the Governance / Ops side of things. Early on the pricing leader will probably do most of the work directly on governance / ops etc, but one amazing analyst that can span across different areas can give you tremendous leverage.
Commercial strategy is typically the last to be layered in and not a priority until a company reaches sufficient scale and have a more complex business portfolio. Also often the deals desk, sales ops and legal would pick up the slack until they can’t anymore.
The pricing team can report to many functions. In my career I’ve reported into Product, Finance, Sales, Marketing, and Strategy/Operations. It really depends on how a company defines the scope of different functions. Where a pricing team sits is less important than who the top leaders are and if the C-suite has sufficient understanding of pricing and what their vision of the function is.
There are pros and cons to having pricing report into every function. Generally speaking I feel like the best place to have pricing is either 1) Product, 2) Strategy/Operations, 3) Finance (if there is a strategic finance function).
Pricing sits at the intersection of Product, Finance, and Sales. So there’s no “perfect” place for it. Everyone needs to understand what the team’s goal is and what they’re charged with solving for. Sponsorship and air cover from the very top is key to maintaining autonomy and objectivity.
Our next AMA will feature Justin Farris, VP of Product at GitLab. Join the PricingSaaS Community to attend live, ask questions, and stay up on all of our events.
🎯 Expert Insight
After diving into more than a hundred B2B SaaS pricing projects over the years, I've seen the same mistakes play out time and again.
Let me share the 5 critical pitfalls I've watched teams stumble into – and how you can sidestep them in your own pricing journey.
1. Too Many Cooks in the Kitchen
Pricing decisions should function as a benevolent dictatorship, not a democracy. When you invite the entire organization to weigh in, you'll end up with a compromise that serves no one. Trust me on this – I've seen the meeting rooms filled with stakeholders, each with their own agenda, and watched as brilliant pricing strategies dissolved into mediocrity.
2. The Perfectionism Trap
There's a particular moment I've witnessed in almost every pricing project – when someone starts pushing for a model that captures every last dollar of potential value. I call this the "over-engineering" phase, and it's where good pricing goes to die.
Simple, effective pricing that captures a 'good' portion of the value is what works in the real world. Aiming for 80% is enough. I've found that leaving some money on the table actually builds goodwill that pays dividends later.
3. Over-reliance on Data
The truth I've come to embrace is that pricing is fundamentally a creative exercise with unlimited options. Most pricing changes trigger new customer behaviors that are impossible to predict or estimate from historical data alone. Some of my most successful pricing structures came from intuitive leaps that no spreadsheet could have suggested.
4. Slow Decision-making and Execution
A client once confided in me that he estimated he had to make about 400 decisions during our pricing project. This sounds about right — I've found that you should be able to design, validate, and launch new pricing in 4-6 months, not the 18-24 months that some companies allow.
Some leaders want 1-2 days for every decision, but that timeline is simply unsustainable. I coach my clients to give themselves 5 minutes for 95% of decisions and a full day only for the most critical ones. Moving with purpose doesn't mean being reckless – it means understanding which decisions truly merit deep reflection.
5. Being afraid of customers
I've seen great pricing get butchered in the execution because the team ran for cover whenever the customer gave the slightest pushback. What you need is a good solution for dealing with the risk of implementation.
I typically advise clients to start with new sales first, then progressively roll out to existing customers, beginning with those most likely to understand the value and ending with the challenging ones.
These lessons weren't easy to come by. Each represents countless hours of work and difficult conversations. But the companies that avoided these pitfalls have consistently achieved the pricing transformations they sought – and the revenue growth that followed.
Thanks for tuning in and see you next week!
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