Thanks so everyone who attended Office Hours yesterday! It was a lively session that was jam-packed with insights from @Ulrik Lehrskov-Schmidt and @Emil Eriksson. Breaking down my 5 top takeaways below for those who couldn't make it: 1️⃣ Measure internally at maximum granularity, but expose a metric customers intuitively understand. Emil made the point clearly: internally you want usage data as granular as possible to understand costs, but what you surface to customers must map to an outcome that makes sense in their world. His example of Lovable charging fractional credits — where one prompt costs 15 credits and another costs half a credit — illustrates what happens when that translation layer is missing. The customer experience becomes a black box. 2️⃣ Build a usage baseline before you price anything. Emil's strongest practical advice was to run shadow pricing for a few months against real usage data, then sit down with friendly customers and say "here's what you would have paid." This prototype-testing approach — borrowed from product design — de-risks the model before launch and creates internal buy-in across finance, product, and sales. @Steve Blanck from M-Files distilled it well: the first ask of any internal stakeholder should simply be "get the data." 3️⃣ Credit systems beat pure metering for handling seasonality and revenue predictability — but only if they self-balance. Ulrik laid out a specific architecture: monthly credit subscriptions that roll over (so customers don't over-calculate), combined with an auto-rebalancing mechanism where the next contract period adjusts to match actual prior usage. This "transposed usage-based pricing" flattens revenue volatility, reduces renewal friction, and — in his experience — keeps balance-sheet carry-over under 5% of ARR. The key design details: monthly credits rot after 12 months, annual credits roll once then expire, and you recognize revenue at the average price-per-credit in the customer's balance.

Hey y,all, happy Friday! Wanted to share my key takeaways from our Office Hours session yesterday with Mark Walker and Tina Kung from Nue.io for those that couldn't make it. For context, Mark and Tina have spent the last two decades deep in the world of CRM, Billing, and ERPs. Tina has built out billing and CPQ products at Salesforce, Zuora, and Oracle. They're building Nue to be the revenue engine for the AI era and are powering companies like OpenAI, Superhuman, Glean, and Chili Piper. Here are my favorite learnings from the session: 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. 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. 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. 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. Takeaway #5: Don’t cancel-and-replace. Whether transitioning a customer between motions or introducing new pricing, preserve continuity. Friction kills expansion. Takeaway #6: Involve finance early. The CFO’s perspective on revenue recognition, SKU management, and reporting should shape packaging decisions from the start. Takeaway #7: System architecture matters. Disconnected billing, CPQ, and self-service systems are the bottleneck. Unified platforms are required to execute these strategies. Read the full recap with for color from Mark and Tina here: https://newsletter.pricingsaas.com/p/how-to-make-plg-slg-work-in-the-ai Otherwise, have a great weekend!

Hi guys We're shooting YouTube content Tuesday about all things SaaS Pricing. If you could choose, what topics / questions should I cover ? I like 10+ min long form stuff, so don't hold back on the complex, hairy stuff! (I'll link to the finished content here when we post it)

Howdy pricing people! As we wrap up 2025, I'm curious: What's one piece of pricing content that really landed for you this year? Could be a book, an article, a podcast, or even just an idea that you found and immediately had to share. Drop it in the comments. Would love to see what resonated across the community! I'll start: I love the concept of Value Literacy that I learned from @Mark Stiving. He defines Value literacy as the understanding of how buyers perceive value, evaluate tradeoffs, and decide what to pay. Highly recommend the full post to go deeper on the concept. I expect this to become even more important in 2026, especially as we enter the Credit Apocalypse. Look forward to seeing what you all recommend so I can load up my holiday reading list 🙂 Happy Holidays! Rob + John

Howdy Pricing People 👋🏼 There's a fundamental tension in SaaS I can't stop thinking about: Every SaaS company wants an AI story right now. To have a credible AI story, people need to be using your AI features. If people are using your AI features at scale, your margins will take a hit. Nobody wants margin erosion because we're still valuing SaaS companies on metrics built for the previous generation. The short-term playbook says protect your margins. The long-term playbook says invest in AI or get left behind. They don't reconcile. I'm genuinely curious how you're all thinking about this: - What should SaaS companies be doing right now? - Seemingly everyone is turning to credits as a hedge to both tell the AI story and maintain margin control. Are there other strategies SaaS companies should consider? - Does something fundamental need to change in how we evaluate these businesses? Drop your thoughts below. I'll be digging into this in this week's newsletter, and would love to share perspectives from this group. 🫡 Rob