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.

Looking to chat with anyone who has experience monetizing on-prem products, specifically in the developer space. Some topics I'd like to think through: - Solution-based selling for on-prem offerings - How to upsell with minimal data insights & stonewall customers - The challenges (and benefits) of selling these products in a consumption-crazy/fatigued landscape Happy to provide more context. THANKS!

Introducing: https://www.revinci.ai/ Every pricing leader I've talked to in the last 12 months is wrestling with the same four questions: 1. How do I quantify the value my AI agent actually delivers — and turn that into a billable unit? 2. How do I price it when my COGS is a moving target on every API call? 3. How do I model 20+ pricing variants — flat, tiered, token, outcome, hybrid — without CPQ and billing drifting out of sync? 4. How do I see my margin in real time, not eight weeks later when finance closes? For 20 years, value-based pricing has been the holy grail no SaaS platform could operationalize, because SaaS value is invisible — you can't meter "the CRM helped close a deal." Agents are different. Every agent action is a measurable outcome event. Tickets resolved. Hours saved. Meetings booked. Bugs triaged. Documents drafted. For the first time, the value an AI delivers is instrumentable — and that means value-based pricing is finally executable. Revinci is built around exactly that shift. The three engines pricing pros should care about: Revinci runs on a unified Sell + Bill platform, and the heart of it is three engines that move together on every event: - Value Engine — model what each agent creates for the customer. Hours saved, tickets resolved, meetings booked, manual-labor cost displaced, time-to-resolution compressed. Every outcome becomes a first-class, meterable, billable unit. This is the engine that turns "we save you 40% of agent handle time" from a sales claim into a contract clause. - SmartCost — real-time cost-to-serve per agent, workflow, customer, and model. Tokens, compute, storage, API calls. COGS visible as revenue is created. - SmartMargin — live gross margin per deal, per customer, per agent. Guardrails auto-flag below-floor transactions. Leakage detection. Profitability scorecards. In pricing terms: value, cost, and margin are no longer three separate spreadsheets owned by three separate teams. They're one continuous signal, evaluated on every event, every quote, every invoice line.

Hi everyone, great to be here. I'm Tomas, an independent billing advisor focused on helping SaaS companies turn pricing ideas into repeatable billing systems. My work sits in the layer that comes after pricing strategy, and that's where things can get messy :-) - subscriptions + usage-based hybrids - plan changes, proration, and edge cases - global sales (MoR vs direct payments) - making sure the billing setup doesn't break product experience or reporting I've spent the last few years working heavily with Stripe and similar platforms, helping teams implement flexible pricing without locking themselves into brittle setups. More recently, I've been building Billing Atlas – a structured advisory service that helps SaaS teams make and execute billing decisions with clarity. I joined this community because pricing and billing are tightly connected, but often discussed separately. I'm particularly interested in how pricing decisions hold up once they hit real systems and customer expectations. Happy to share practical insights from the implementation side, compare notes, or help validate ideas when useful. Looking forward to learning from all of you.