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.

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.

I just added this to a comment so sharing for visibility: The SaaS Monetization Tech Stack Billing has Stripe. Analytics has Amplitude. Feature flags have LaunchDarkly. Etc. But the system that decides when to upsell a user, what usage limit to enforce, or which paywall variant to show? That usually lives in a tangle of product code, feature flags, and duct tape... A few things that have came up: - The fragmentation problem is worse than most teams realize. Entitlement logic ends up duplicated across billing, product code, and feature flags. A simple pricing test that should take a day takes weeks because three systems need to stay in sync. My CTO (ex-Atlassian, Dropbox) is really good at explaining the risks/headaches from this "brittleness" to technical folks! - Mobile solved this years ago. RevenueCat and Superwall own the monetization layer for iOS/Android apps. Web SaaS has no equivalent — most teams are still stitching it together manually. The above content is really resonating with an ex-Revenue Cat guy who is now Head of Product at a web SaaS business. - The main benefit of getting it right (in-house or via tooling) is experiment velocity

Hey pricing people! We just published a new report with our friends at Nue.io. It's called PLG vs. SLG: What the Data Says About SaaS Growth in 2026. We analyzed 3,847 pricing, packaging, and product changes across 498 SaaS companies to figure out what's actually happening at the intersection of product-led and sales-led growth. Some of the most interesting findings: 1️⃣ Freemium strategy is bifurcating. Of the 40 companies that changed their free tier in 2025, roughly half tightened or eliminated it (Deputy, Plaid, Apollo GraphQL) and the other half expanded it (TravelPerk went fully free, Scratchpad loaded AI features into the free tier). There seems to be less interest in the middle. Companies are either going all-in on Freemium for activation, or pushing harder on monetization. 2️⃣ Trials are getting shorter. The median trial is heading from 30 days to 14. AI-native tools are already at 7. Voiceflow cut its trial in half while increasing AI tokens 150%. The bet: AI means users can hit value faster, so why give 30 days? 3️⃣ Credits are bridging the gap. 126% YoY growth in credit-based pricing. Monday, Figma, Miro, Notion, Hubspot - they've all implemented credit models. Credits are becoming the connective tissue between PLG and SLG — self-serve consumption that naturally creates sales conversations when pools run dry. Grab the full report here → We'd love your reactions. What matches what you're seeing? What surprises you? How are you thinking about a hybrid PLG + SLG motion right now? Drop thoughts and feedback in the thread 👇

One of the hardest things to sort out in pricing seems to be wrangling the product catalog. Sales need it in CRM/CPQ systems. Finance need it in Billing/ERP systems. Product/Pricing need it in ... where? When products have usage-based pricing or entitlements, many ERPs can't handle it, and the product catalog spreads into a third system that handles usage, credits, entitlements, etc. We see how this crosses organization boundaries, lacking a single clear owner, and keeping everything in sync becomes super important - and very difficult to keep 100% correct over time. And most likely, someone in your organization is using Excel in some part of this process. Curious to hear how others split the product catalog, both horror stories and success stories.