Morning everyone, We've got Office Hours this Thursday with David Reid from Teneo. If you're not familiar, Teneo works with private equity-backed SaaS companies navigating some of the hardest pricing questions in the market right now — specifically how legacy platforms should think about pricing and packaging as AI reshapes their market. This one's going to be really relevant if you're dealing with any of the following: → You have a mature SaaS product and you're trying to figure out where AI fits in the pricing model → You're balancing AI adoption with protecting existing revenue streams → You're unsure whether to bundle AI features, gate them as add-ons, or restructure entirely → You want to understand how PE-backed companies are approaching this transition David's team have seen under the hood of hundreds of SaaS companies making these changes, and will share what they're seeing in the trenches. We'll collect questions in advance, then shift to open Q&A. 📅 Thursday, April 16 Small group, approval required (this isn't a webinar.) Register here: https://luma.com/lh57wykx Drop any questions you want covered in the comments and I'll make sure we get to them!

@Farhan Manjiyani Farhan Manjiyani guest-wrote last week's newsletter, and I loved his insights on fair use policies as a way to protect margins while creating a true AI story. The big idea: "unlimited" is dead, hard caps churn customers, and the companies winning right now are landing in the middle with fair use policies. A few examples he shared: 1️⃣ Grafana Cloud Logs lets you query up to 100x your ingested log volume for free. Go beyond that and billing kicks in on a formula. It protects margin without putting a scary number on the pricing page. 2️⃣ ChatGPT doesn't cut you off when you hit your GPT-5 cap — it silently drops you to a lighter model until your window resets. No overage, no upgrade wall, just graceful degradation. It's one of the most underrated pricing moves in AI and almost nobody's talking about it. 3️⃣ 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. Curious what others are seeing — are you using fair use policies, or seeing anything cool in the wild?

Curious to hear how other pricing strategists and operators are using AI in their workflows. Are you just getting started, or do you have a full flow built? What tools do you like vs. which tools are causing you the most trouble? I'm especially curious if anyone has developed a way to leverage AI to refine ICPs?? Or if anyone has found any good Claude skill files specific to pricing? I'll kick off! Some context: We're refining our sales-led pricing model at the moment and need to move quickly, so I don't have the support to run formal research. I'm also a team of one at a Series A company that just underwent a massive change in how we deliver our product 😅 I've been using Claude to refine new pricing structures and develop HTML pricing calculators for sales-led deals. I've been funneling context to Claude using our product's MCP server, knowledge files, and more, which have allowed me to develop and pressure-test various pricing strategies that I have then vetted with cross-functional leadership and sales as viable to test. My sales team now has a self-service hub they can rely on to run with these new models for certain deals. The hub provides guidance on choosing a model, the value story for the chosen model, pricing, and ROI calculations. Would love to hear what everyone else is doing!

Hey pricing people! We just published a new report with our friends at Nue.io. It's called PLG vs. SLG: What the Data Says About SaaS Growth in 2026. We analyzed 3,847 pricing, packaging, and product changes across 498 SaaS companies to figure out what's actually happening at the intersection of product-led and sales-led growth. Some of the most interesting findings: 1️⃣ Freemium strategy is bifurcating. Of the 40 companies that changed their free tier in 2025, roughly half tightened or eliminated it (Deputy, Plaid, Apollo GraphQL) and the other half expanded it (TravelPerk went fully free, Scratchpad loaded AI features into the free tier). There seems to be less interest in the middle. Companies are either going all-in on Freemium for activation, or pushing harder on monetization. 2️⃣ Trials are getting shorter. The median trial is heading from 30 days to 14. AI-native tools are already at 7. Voiceflow cut its trial in half while increasing AI tokens 150%. The bet: AI means users can hit value faster, so why give 30 days? 3️⃣ Credits are bridging the gap. 126% YoY growth in credit-based pricing. Monday, Figma, Miro, Notion, Hubspot - they've all implemented credit models. Credits are becoming the connective tissue between PLG and SLG — self-serve consumption that naturally creates sales conversations when pools run dry. Grab the full report here → We'd love your reactions. What matches what you're seeing? What surprises you? How are you thinking about a hybrid PLG + SLG motion right now? Drop thoughts and feedback in the thread 👇

Wanted to sanity check a view with you: MCP matters product-wise, but not as a standalone pricing unit. My take is SaaS companies shouldn’t monetize MCP itself. It’s a connectivity layer, and customers don’t buy “protocol access” - they buy the value it unlocks. So the monetization likely shows up in: - premium AI layers — paid add-ons / tiers with custom agents, integrations, context, admin controls, or workflow automation - usage-based pricing — credits, actions, outcomes - seat expansion / plan upgrades - pull-through to core product usage So my thesis is: don’t monetize the protocol; monetize the value created through it. Curious what others are seeing: - what usage patterns are real? - how are you packaging it? - what are customers actually paying for?