For the past month, a handful of users have been beta testing PricingSaaS Pulse. The feedback has completely reshaped how I think about the Pulse product. When @John Kotowski and I started PricingSaaS, the goal was to make pricing expertise accessible to the average product team. We'd both witnessed our own versions of pricing paralysis. As a consultant, I watched clients struggle to drive consensus. As a Chief Product Officer, John lived the pain firsthand. Often, when teams are staring down a new launch, they freeze. Not because the team isn't smart. Because there are just so many options, and its hard to cut through the noise and figure out what makes the most sense. Pulse is solving that. The clearest pattern from beta: Pulse works as a pricing intelligence layer that plugs in alongside a team's own internal context. The combination of internal context, and our structured market data is the unlock. On one side: Their documentation, their data, their customer transcripts. On the other: A clean, objective view of what companies are actually doing. The most interesting thing we're seeing: a handful of beta users have started using Pulse to build their own pricing agents. The recommendations those agents are producing are sharp. Specific. Grounded in what's actually shipping. It's the closest thing to a real pricing copilot I've seen. We soft launched last week. If you're a SaaS operator or pricing consultant and you want to take it for a spin, comment here or shoot me a DM and I'll get you squared away.

Hey pricing people! We have a great Office Hours session in store next week. I'll be joined by @Fynn Glover, CEO of Schematic to discuss how companies can build a modern pricing architecture. Fynn is my go-to person to send people when they're running into pricing tech bottlenecks. And every person I have sent his way has thanked me profusely afterwards. In this Office Hours, Fynn and I will dig into: - Why your billing tool isn't the bottleneck (and what actually is) - The five pillars of a modern pricing architecture: unified product catalog, decoupled entitlements, real-time metering, company profiles, and a GTM control plane - How companies like Plotly ship and monetize AI products two quarters faster than their peers - What it takes for non-engineers to iterate on monetization without filing a ticket - Why, in the AI era, pricing agility is revenue agility If you've ever had a "creative pricing conversation" die because someone said "our systems can't do that" — this one's for you. Bring your questions. We'll workshop them live. Details below: Date: Thursday, June 4th Time: 12pm EST Registration Link: https://luma.com/7okgi8rk Hope to see you there! Rob

Big thanks to everyone who joined Office Hours last week. This was one of our most popular sessions ever, and for good reason. @Ulrik Lehrskov-Schmidt brought the heat with an absolute clinic on how to price, protect margins, and not lose your shirt as AI eats into cost structures. Breaking down my 5 top takeaways below for those who couldn't make it: 1️⃣ The "double hockey stick" is the real AI pricing trap We're all used to the idea that value is unevenly distributed across customers — some get massive value, most get medium, a few get none. What Ulrik nailed in this session is that AI introduces a SECOND hockey stick: cost distribution. He shared a real example from a client with a couple hundred million in ARR where 75% of their entire GenAI cost was driven by just 0.1% of users. Thousands of users driving three-quarters of all consumption. The critical insight: there's no natural law that says your value hockey stick is proportional to your cost hockey stick. You can build a beautiful AI product that turns dollars into dimes. Or you can be like @Steven Meyer at Placer.ai, serving customers ~$50K/week of value at $150 in cost. Most of us land somewhere in the middle, but you have to KNOW where, and you only learn by putting product in the market and measuring. 2️⃣ Define fair use in dollars, not actions This was the most tactical part of the session. Most companies set fair use policies as "more than 500 API calls per month" or some action count. Ulrik's argument: that metric will be obsolete the second your model changes or your prompt structures shift. Track REAL COST per user in real time, and trigger fair use at dollar thresholds. The framework: bucket your users (malicious vs non-malicious, new vs tenured, monthly vs annual, by vertical, whatever matters). Build your fair use policy along a dollar cost line. Then run different policies per bucket and tune to the margin you want. He has customers where they literally use fair use policies to "decide the margin of the product" — turn the dial, get the gross margin you want.

Came across this HubSpot piece on credit-based pricing. The article was published yesterday - suspiciously timely after last night's conversation on credits and transparency :-) @Rob Litterst , were you behind this one? https://blog.hubspot.com/website/why-ai-usage-based-pricing Found it quite insightful. I particularly liked the screenshots showing in-product cost labels at the point of action. HubSpot uses a credit badge on the data agent, whereas Airtable gives you a breakdown of what exactly your credits buy you. It's the difference between getting a surprise bill at the end of the cycle and watching the meter tick as you are using the product (so that you know when to stop spending). Great for building trust.

Happy Monday pricing people! We've got an awesome Office Hours topic this week. On Thursday, I'll be hosting a session with @Ulrik Lehrskov-Schmidt about managing AI costs. As most of you know, this is one of the gnarliest problems in SaaS right now. So we're talking about it. Here's what we're planning to cover: ▶ Designing fair use policies that protect margin without alienating customers ▶ When (and how) to use throttling, queuing, and rate limits ▶ Structuring cost-to-serve into your pricing model from day one ▶ What the high-consumption companies are learning the hard way We're collecting questions in advance, and will leave room for Q&A at the end. It's Thursday at 10am EST (we will record it). Grab your seat here: https://luma.com/8q7djgto