An Operator's Guide to Fair Use Policies
Plus: Updates from Lovable, base44, Docusign, Framer, and Gusto.
Welcome back to Good Better Best.
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.
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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 →
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
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.
Raise prices across all 1,000 customers to cover the tail.
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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