Howdy Pricing People! AI credits are everywhere right now, but almost nobody agrees on how to run them. On Wednesday, October 28 at 12 PM ET, we're hosting a Monetization Roundtable with three operators who have actually rolled out credits and are actively working on their models: - Rita Sherman, Sr. Director of Monetization at Gong - Jeff Curran, Head of Pricing and Packaging at Miro - Manu Mehra, Strategic Pricing Leader at Databricks We'll cover: - Why they moved to credits instead of seats or charging per outcome - How they set the price of a credit and decided what uses one up - Allowances, rollovers, and top-ups - What it took inside the company to launch, across billing, finance, sales, and support - What customers like and what they push back on If you're pricing an AI product, or about to, come with questions. Save your spot: https://tinyurl.com/2xmt6gao Hope to see you there! Rob

Great session today with @Ulrik Lehrskov-Schmidt and @Emil Eriksson on taming AI margins. You can find the full recording and transcript in Google Drive: https://drive.google.com/drive/folders/1hBZnQ6HWlzPwNgB9rw8ZrnXA9Uk0Ba5t?usp=drive_link If you're working on this and could use a sounding board, shoot me a DM! Otherwise, hope to see you at the next one. Rob

Howdy pricing people! We've got a great one coming up this Thursday. @Ulrik Lehrskov-Schmidt , CEO of Willingness To Pay, is joining us to answer your questions on pricing and packaging AI usage. Ulrik works with B2B software teams on exactly the problem everyone's wrestling with right now: how to monetize AI features when usage is unpredictable and costs can run away from you. If you've been staring at your AI margins wondering how to package this stuff, his perspective is hard to get anywhere else. A few things we're going to dig into: - How to price and package MCP access - Whether unlimited plans are a growth lever or a margin trap - How to actually write a fair use policy that holds up Details below: Thursday, June 18th @ 12:30pm EST Register here 👉 https://luma.com/y70r7esb Hope to see you there! Rob

Howdy pricing people! On October 8th we've got two special guests: Ulrik Lehrskov-Schmidt, CEO of Willingness to Pay, and Emil Eriksson, CPO of DigitalRoute. We're going to talk about AI margins, and how to stop them from slipping away as usage grows. The pairing gives both sides: - Ulrik works on strategy: credits, packaging and how to capture value. - Emil works on the technical side: how usage actually gets metered and turned into revenue. We're increasingly seeing a gap in communication between these two groups, and believe tighter collaboration is key for the next phase of AI monetization. A few things we're going to dig into: - How to set credit burn rates when your model costs keep moving - When sending work to cheaper models makes sense, and when it just makes the product worse - What your usage setup needs to track now so you can change pricing later without a rebuild Details below: Thursday, October 8th @ 11am ET Register here 👉 https://luma.com/qdlmss76 Hope to see you there! Rob

Last week we had David Reid, Samuel Little and Pooja Nair from Teneo on PricingSaaS Office Hours. It was a great session, with tons of practical advice on implementing credit models. Here are 5 things from the session I'd want to know if I was rolling out a credit model right now: 1️⃣ Unpredictability loses more deals than price does. In Teneo's work, unpredictability comes up as a loss reason far more often than total price. When customers push back on credits, it's usually about control, not credits themselves. The fix: t-shirt-size bands, forgiving overage in year one, and enterprise contracts that lock in what credits cost at 1M, 10M and 100M. 2️⃣ Get customers used to the counting before you charge. David recommends a 3-month free pilot with monthly value statements: "You used 1,000 credits and saved 500 hours." Charge a small services fee for onboarding, not ARR. That way it never becomes a big, scary approval. 3️⃣ Don't create a new SKU for every AI feature. If a feature doesn't cost much to run, put it in the base platform with a credit allowance. Size that allowance so customers use it up early in the year. Top-up packs are where expansion comes from. (And no one is paying extra for AI summaries anymore.) 4️⃣ 5 to 8 credit burn categories is the sweet spot. Map out the jobs your AI does and you'll find about 50 ways to charge. Put them into 5 to 8 buckets. Keep the subscription and the credit estimate simple. The rate card itself can be more detailed. 5️⃣ Your margins should improve over the life of the contract. Credit models usually target 70 to 85% margin on COGS. Send each task to the cheapest model that does it well and your cost per task keeps falling. With fixed-price credits you keep those savings. With cost-plus pricing you end up passing them on. Dropping the link to the recording and transcript here: https://drive.google.com/drive/folders/158hWJshwYM3-8pgu2B0bFR4F5a5hxym9?usp=drive_link