Pricing Software Solutions

AI Agents Don’t Buy Seats—Why Your Pricing Should Follow Suit In the past 12 months, a clear pattern has emerged: as AI systems replace manual effort with automated intelligence, pricing structures tied to “seats” no longer reflect the value customers receive. Pricing models have surfaced as a hot topic with every portfolio company at Mosaic Ventures and is top-of-mind for nearly every founder building applied-AI products. When one person and an AI agent can outperform an entire legacy team, charging per user starts to feel arbitrary; what matters is how much business impact the product delivers. Founders are experimenting with three broad approaches: 1. Usage-metered plans that bill against tokens, API calls, or minutes of inference time. These create a direct bridge between consumption and margin and nudge teams to track cost from day one. 2. Outcome-based pricing that charges per lead booked, ticket resolved, or document drafted—tying revenue to measurable results. It’s the software analogue of value-based care. 3. Hybrid “starter bundle plus runway” tiers: a predictable monthly fee with a healthy allowance of AI credits, then pay-as-you-go beyond that. This balances budget certainty for customers with upside capture for the vendor. Across our portfolio, a few design principles keep showing up: 1. Anchor on a metric the customer already tracks. If your product shortens sales cycles, price per opportunity accelerated—not per login. 2. Bundle enough volume to eliminate credit anxiety. No one wants to ration prompts. 3. Expose real-time usage. Transparent dashboards prevent bill shock and build trust. 4. Instrument cost early. Metering and billing belong in the product backlog, not the finance queue. 5. Plan for non-linear jumps. When a model upgrade multiplies compute, re-grade tiers before your gross margin does it for you. AI’s promise is to shift human effort from repetitive execution to higher-order creativity. If our pricing still counts bodies instead of business results, we undermine that promise. The companies that map price to outcomes—while keeping the buying experience refreshingly simple—will capture the most upside. I’d love to hear how others are managing the move from seats to usage and outcomes. What’s working, what still feels messy, and where do you see the biggest opportunities to innovate on pricing? #appliedAI #pricing #startups

At the start of my career, pricing was often treated as an afterthought. Decisions were made based on instinct, outdated models, or by simply matching competitors. I witnessed how this approach consistently led to underperformance, weak positioning, and lost revenue opportunities. That experience shaped my belief that pricing is one of the most overlooked drivers of business growth. To solve this, we built the Predictive Sales Engine an AI-powered tool that brings clarity to pricing strategy. It analyzes actual market behavior to forecast revenue and sales volume at different price points. More importantly, it segments data to reveal how different audiences respond to pricing, allowing companies to set prices with precision and confidence. After working with hundreds of companies, the pattern is clear. When pricing aligns with how customers perceive value, businesses grow faster and more profitably. In a competitive market, using AI to guide pricing decisions is no longer a luxury. It’s a requirement for those aiming to lead rather than follow. #PricingStrategy #ArtificialIntelligence #PredictiveAnalytics #RevenueGrowth #ProductMarketing

For SaaS, think this way ▪️ Horizontal software : price on inputs ▪️ Vertical software : price on outputs. Horizontal: Let's take Azure, Asana, or QuickBooks : these can all be used across any industry and vertical. Any two customers can get wildly different value out of the same solution. But because these providers want to serve *all* of those verticals, they have to price based on common inputs: e.g. API calls or Users. Horizontal solutions (by definition) trade: Number of customers > Value per customer Vertical: Take #Benchling, #TrackUnit or #Slice - all companies that serve a very specific vertical (life science labs, construction and single location pizza places, respectively). Because they serve a very narrow vertical, the value is very consistent for same-size customers. This allows these (and other vertical SaaS vendors) to either: ▪️ Price based on output or a metric very specific to the vertical served (e.g. % of revenue, price per lab, price per motor powered asset) ▪️ Price an input (e.g. users) AS IF it was an output (=more expensive than comparable horizontal solution). Vertical solutions are simply easier to value price, because they integrate more deeply into the value chain of their customers and support the value creation all the way to the end. #PricingDesign #SaaS #B2BSaaS #SaaSPricing

𝗛𝗼𝘄 𝗔𝗜 𝗶𝘀 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗛𝗼𝘄 𝗪𝗲 𝗣𝗮𝘆 𝗳𝗼𝗿 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 Is $200B+ Salesforce abandoning the safety net of multi-year contracts for $2 per AI-driven conversation? Not just a new price tag. It might just be a rethink of the entire model. Salesforce is responding to customer demand for value that aligns directly with outcomes. But Salesforce isn’t the only one. Across AI, companies are finding new ways to charge that tie cost directly to value. 1️⃣ 𝗨𝘀𝗮𝗴𝗲-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗢𝗽𝗲𝗻𝗔𝗜 charges per input/output token. 𝗭𝗲𝗻𝗱𝗲𝘀𝗸 𝗔𝗜 bills per auto-resolution. 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 charges $4 per hour of AI usage. 𝗦𝗮𝗹𝗲𝘀𝗳𝗼𝗿𝗰𝗲 considers $2 per conversation. This model makes AI accessible to more companies. It’s pay-as-you-go, tied to use. 2️⃣ 𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗖𝗵𝗮𝗿𝗴𝗲𝗳𝗹𝗼𝘄 takes 25% per chargeback. 𝗔𝗳𝘁𝗲𝗿𝘀𝗵𝗼𝗼𝘁 offers unlimited AI photo edits. This approach aligns cost with direct results, so customers pay for outcomes, not promises. 3️⃣ 𝗙𝗿𝗲𝗲𝗺𝗶𝘂𝗺 𝗮𝗻𝗱 𝗖𝗿𝗲𝗱𝗶𝘁-𝗕𝗮𝘀𝗲𝗱 𝗠𝗼𝗱𝗲𝗹𝘀 𝗜𝗻𝘁𝗲𝗿𝗰𝗼𝗺 offers “10 free tickets monthly per agent.” 𝗖𝗼𝗽𝘆.𝗮𝗶 charges per workflow credit. These models lower the entry barrier, letting users try before they buy. 4️⃣ 𝗣𝗲𝗿-𝗧𝗮𝘀𝗸 𝗮𝗻𝗱 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝗥𝗲𝗹𝗮𝘆.𝗮𝗽𝗽 charges per workflow step. 𝗭𝗮𝗽𝗶𝗲𝗿 prices per task automated. This approach supports businesses that need specific automation without high upfront costs. Check out the infographic below for more on these top pricing models changing the AI landscape. ⬇️ These pricing models shift costs to match real value. No more paying for licenses that go unused. It’s a fairer system where businesses pay for what they get. A response to customer demand for value that aligns directly with outcomes. #AIInnovation #PricingStrategy #ProductPricing #BusinessModel #SaaS Follow me for weekly updates on the latest tools and trends in UX and productivity.

Pricing a niche SaaS is hard. Especially while you're still figuring out who loves it and why. You want to test different pricing strategies early. Here are 5 ladders to test: 1) Usage Trigger: events, runs, records. Show a live bar. Offer top-ups. 2) Seats Trigger: active team members. Invite → prompt upgrade. Gate a few pro features. 3) Credits Trigger: actions or generations. Low-credit alerts. 1‑click top-up. Clear cost per action. 4) Features Trigger: advanced capabilities. Lock tooltip explains value. Keep basics free. 5) Hybrid One headline metric. Small allowance. Clean overage or plan bump. Day-one moves: - Ship a tiny paid Starter. - 1‑click in‑app upgrade. - Trials 7–14 days, keep simple. Guardrails: - Use clear value metrics. - No freemium. - Nudge, don't punish.