I've been thinking about pricing in the context of AI-driven quoting which leads to highly dynamic pricing. Let's say we get really good at coming up with prices that maximize conversion (AI + sales). But what happens once the customer commits and we start charging on a regular basis? That's where I see a gap. In most setups I've seen, we lose the context behind the price very early on: - why is this customer paying $X instead of $Y? - was it an AI suggestion, a discount, or a manual override? - what inputs influenced the price point? I'm thinking about this problem from the perspective of plan migrations, where we often don't know how to handle certain (small) cohorts with non-standard billing setup. In your experience, is it a common practice to link pricing decisions (CRM / quoting + context) to the actual billing objects (subscriptions, customer records, etc.) in a structured and automated way?

I have a few thoughts on AI sprawl connected to learnings from SaaS. For this group ROI, unit costs, and monetization is one key pillar. What do you think? https://www.linkedin.com/posts/akshaypatel07_productmanagement-cxo-venturecapital-activity-7455947667186122752-B8_c?utm_source=share&utm_medium=member_ios&rcm=ACoAAABJjBEBGcaO8S8AHEITnczM9B_WTSKa6dc

Hi everyone — glad to be here. I'm Sowmya, a pricing systems PM and live in Austin. My background is in building the pricing logic and intelligence layer — building rules engines, ML-based rate models, and pricing APIs that sit between raw market data and what a customer actually gets charged. Recent work at Engine,Cox Automotive/vAuto and Dell. Mostly here to geek out on how we build pricing systems for the future. One question I'm actively thinking through: Agentic AI feels like it could pressure move to more dynamic pricing frameworks in SaaS context and I'm curious what that means for pricing decision and logic infrastructure. Would love to connect with others thinking about the same things.

Pricing is one of the hardest things to get right as a business owner. Most of us start too cheap.Some go too expensive too fast.Others realize later they were undercharging for years. Looking back, my biggest lesson with pricing has been how much it affects who you attract, how profitable you are, and how seriously customers take you. So I’m curious from other operators here: What pricing mistake taught you the biggest lesson in business? Could be: • Charging too little• Raising prices too late• Not charging for something you should have• Overpricing early 👇 Drop your experience in the comments.

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 👇