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

we wall want to be involved in the ideation stage of a product. where it's in alpha or beta and we have plenty of time and people to survey, research and model pricing. where pricing folks earn their check is when we have to price something in a crunch. all the pressure without any time / space to think wrote about how i approach in these situations: https://f13i.com/willingness-to-pay curious what you all do in crunch time

In an AMA I did a couple weeks ago, I was asked if pricing pages mattered - or how they mattered relative to AI features/capabilities/agents etc. While the full write-up is at https://forstarters.substack.com, the key point is: - Self-serve is not the business model, it's a marketing campaign for "Contact Sales"

I wonder what you think about this "new" pricing model promoted by Nue. It feels like it's supposed to solve a very real shift: - "Old Saas" sold entitlements: licenses, seats, subscriptions - "New SaaS" increasingly sells committed spend against flexible consumption. Simply put: As a vendor, how do I give my customers flexibility without destroying (the predictability of) my revenue? I like the direction, but I wonder where it might break in practice. A few things that can go wrong in my mind: - it's difficult for customers to understand, because it's complex. - AI usage volatility is still crushing margins. - Frequent disputes about overage and unexpected credit burn. - Prone to metering inconsistencies = even more disputes. - Revenue still unclear + accounting complexity (taxes, compliance). - Product behaviour drifts away from the commercial intent over time (the entitlements become a bit fuzzy). Curious what people here actually think of this model. Have you seen it succeed or fail?

I’ve been thinking about how pricing is evolving in the context of AI agents, and I’d really value your perspective. Historically, pricing has often been a bottleneck in the sales process—but over time, we’ve built systems (CPQ, billing, usage-based models) that make pricing more structured and scalable. With AI agents, it feels like we’re facing a new version of that challenge—not in pricing execution, but in defining and measuring value. While costs (tokens, infra, etc.) are relatively transparent, the value created by agents is much harder to quantify consistently. We’re currently exploring ways to decode this as part of our product journey. I’d love your thoughts on a few open questions: 1. Defining value: What key parameters or signals should be captured to quantify the value created by an AI agent (e.g., time saved, revenue impact, quality improvements)? 2. Data accessibility: In your experience, how willing are customers to share internal metrics required to measure value? What has worked (or failed) in getting this data? 3. Human-in-the-loop (HITL): How should we think about attributing value when humans are partially involved in the workflow? This seems like one of the hardest aspects to standardize. 4. Customer success criteria: How do you recommend aligning pricing with what “success” actually means to the customer, given that this can vary widely? 5. Product vs. sales responsibility: Should value measurement and tracking be embedded within the agent/product itself, or handled externally in the sales/pricing layer (e.g., CPQ, analytics tools)? If you’ve seen strong frameworks, patterns, or even failed approaches in this space, I’d be very interested to learn from them. Thanks in advance for your time—I’d really appreciate any guidance you can share.