Strategies for Scaling Vendor Pricing Models

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 $10M+ ARR, You are losing money. Not because of bad product, But because of bad pricing. Why pricing? → Competitor pricing weakens positioning → Pricing doesn’t match customer value → Customers stay on the cheapest plan → No upsells, no expansion revenue → Too few users on annual plans → Enterprise deals lack flexibility → Pricing is never tested Lack of pricing strategy directly affects your revenue. Here are 7 steps to fix it. 1. Audit pricing by revenue segment → Where is pricing suppressing upgrades? 2. Reposition pricing against competitors → Own a category, not just a price point. 3. Expand revenue streams → Upsells, add-ons, usage-based models for high-value users. 4. Charge based on value, not just cost → Align pricing with impact and willingness to pay. 5. Move customers to annual → Build ACV and retention with incentive-based annual pricing. 6. Enable enterprise flexibility → Custom contracts, volume discounts, and deal-based pricing. 7. A/B test pricing regularly → At this scale, small price shifts = millions in ARR gains. At $10M+, pricing isn’t just a strategy, it’s a competitive advantage. P.S. How often are you testing your pricing strategy? ♻️ If you find value, let others benefit too. __________________________________________ Ready for more SaaS pricing insights? Follow me, Marcos Rivera🔔

Pricing is a hot topic. At KPMG High Growth Ventures, we are fielding a high volume of enquiries on how to price here and when our clients launch internationally. It's clear that pricing strategies for #startups are evolving rapidly due to shifting market dynamics, customer expectations, and unpredictable macroeconomic conditions. The below is what I've been discussing in the last 2 weeks alone. 💹 Usage-Based & Value-Driven Pricing Startups, especially in SaaS, are moving away from fixed subscription models and adopting usage-based pricing (UBP), where customers pay based on consumption (e.g., API calls, storage, or active users). Why? It aligns revenue with customer success, making it easier to land and expand within accounts. 💹 AI-Driven Dynamic Pricing AI-powered pricing models are enabling real-time price adjustments based on demand, customer behavior, and competitor benchmarking. Example: E-commerce and B2B platforms are using AI to optimize discounting strategies based on customer lifetime value (LTV) predictions. 💹 Freemium + Premium Hybrid Models The traditional freemium model is evolving, with startups integrating premium feature unlocks, AI-assisted functionalities, or paywalled analytics to increase conversion rates. Example: Companies like Notion and OpenAI offer free tiers but monetise advanced capabilities. 💹 Localisation & Regional Price Sensitivity Startups are implementing geo-based pricing to maximize revenue in different markets, using regional purchasing power to justify tiered pricing. Example: Companies like Spotify and Netflix price their services differently in India vs. the U.S. 💹 Transparent & Ethical Pricing Customers demand pricing clarity—startups that eliminate hidden fees and offer straightforward pricing gain trust. Trend: More "cost-plus" models, where pricing is based on production costs + a margin, are emerging in sectors like direct-to-consumer (DTC) and fintech. 💹 Financial Engineering in Pricing Founders are leveraging payment flexibility—offering pay-over-time options, revenue-sharing models, and financing plans to improve accessibility. Example: B2B startups using monthly vs. annual prepayment toggles to balance cash flow and customer acquisition. 💹 AI & Data Monetisation as a Revenue Lever Startups are increasingly monetising data insights, analytics dashboards, and AI-powered recommendations as add-ons. Example: Companies selling anonymised, aggregated customer data insights as a separate revenue stream. ⚠️ Key Takeaway ⚠️ Pricing is no longer static and one size doesn't fit—startups must adopt flexible, data-driven, and customer-aligned pricing models to stay relevant and competitive.

Orb 𝗷𝘂𝘀𝘁 𝗿𝗲𝗹𝗲𝗮𝘀𝗲𝗱 𝘁𝗵𝗲𝗶𝗿 “𝟮𝟬𝟮𝟱 𝗦𝘁𝗮𝘁𝗲 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗣𝗿𝗶𝗰𝗶𝗻𝗴” 𝗿𝗲𝗽𝗼𝗿𝘁 — 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗼𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝘆𝗲𝘁 𝗼𝗻 𝗛𝗢𝗪 𝘁𝗼 𝗱𝗲𝘀𝗶𝗴𝗻 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗳𝗼𝗿 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀. ⬇️ The report analyzes the pricing strategies of 66 companies offering AI agent products. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲 𝗴𝘂𝗶𝗱𝗲 𝗰𝗼𝘃𝗲𝗿𝘀: ⬇️ 1. Orb identified 8 foundational pricing components → This are the pricing core models currently emerging in the market: • Subscription – Flat recurring fee for access, usually monthly or annually. • Per user or seat – Charged based on the number of individual users. • Usage-based – Scales with consumption (e.g. tokens, API calls, generations). • Outcome-based – Pricing tied to results (e.g. leads closed, tickets resolved). • Freemium or free trial – Free limited access to drive adoption and conversion. • Tiered – Pricing packages with increasing features or usage limits. • Add-ons – Paid upgrades for advanced features or premium support. • Hybrid – A mix of models to balance predictability, flexibility, and value capture. 2. Hybrid pricing is the default  → 92.4% of companies now combine multiple pricing components — most commonly subscription + usage + freemium + tiered access. Understanding these levers is now table stakes for anyone pricing agents. 3. SaaS-only pricing will kill your margins  → Flat rates break under AI’s compute load. 85% of SaaS-based offerings now layer in usage pricing to avoid margin collapse. 4. Outcome-based pricing is a wide open lane  → Only 4.5% of companies tie price to actual business results. But the strategic upside is enormous — especially for agents replacing human work. 5. Parallel pricing = segmentation superpower  → 12% of vendors now offer distinct models for different audiences (e.g., flat-rate for individuals, per-seat for teams). This flexibility fuels learning and market fit. 6. Billing infra is now a moat  → Hybrid pricing adds complexity fast. If your billing stack can’t handle dynamic usage, add-ons, or outcome tracking — you’re flying blind. Pricing isn’t a table in a Google Sheet. It’s your growth mechanic. It’s part of the product — and it’s one of your strongest levers for growth. Full report below. ⬇️ Enjoy. 𝗣.𝗦. 𝗜 𝗿𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗹𝗮𝘂𝗻𝗰𝗵𝗲𝗱 𝗮 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘄𝗵𝗲𝗿𝗲 𝗜 𝘄𝗿𝗶𝘁𝗲 𝗮𝗯𝗼𝘂𝘁 𝗲𝘅𝗮𝗰𝘁𝗹𝘆 𝘁𝗵𝗲𝘀𝗲 𝘀𝗵𝗶𝗳𝘁𝘀 𝗲𝘃𝗲𝗿𝘆 𝘄𝗲𝗲𝗸 — 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝘀𝘁𝗮𝘆 𝗮𝗵𝗲𝗮𝗱 𝘄𝗵𝗶𝗹𝗲 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗮𝘁𝗰𝗵 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝘀𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀. 𝗜𝘁’𝘀 𝗳𝗿𝗲𝗲, 𝗮𝗻𝗱 𝘆𝗼𝘂 𝗰𝗮𝗻 𝘀𝘂𝗯𝘀𝗰𝗿𝗶𝗯𝗲 𝗵𝗲𝗿𝗲: https://lnkd.in/dbf74Y9E

How organizations prefer to pay for AI agents is at odds with how vendors can sustainably monetize them.  While the majority prefer consumption-based pricing, what stands out in Capgemini’s latest agentic AI report is how many organizations still prefer subscriptions or per-seat pricing. CFOs want predictability. Vendors need pricing that scales with usage and preserves margins. So what’s the move? Hybrid pricing offers a practical middle ground by blending fixed fees with variable costs. It lets vendors meet buyers where they are and grow revenue alongside usage. If you aren’t combining different pricing structures, now’s the time to experiment with hybrid models. Use a pricing simulator (like Orb Simulations) to forecast the impact of different approaches, and iterate quickly to find the right model that balances customer needs with long-term sustainability: https://lnkd.in/g4WjAe4Y  Get Capgemini’s report here: https://lnkd.in/g6AcpTAW 

One of the least-discussed challenges in AI adoption today is pricing. Everyone talks about model performance, benchmarks, or features. But for enterprises, the real sticking point often shows up when the bill discussion starts. The problem: current pricing models don’t align with how enterprises budget and buy. Usage-based pricing makes perfect sense for vendors, but it feels like a blank cheque for buyers. If adoption succeeds, the bill grows in unpredictable ways. No CFO wants to be surprised by a doubling in costs because usage spiked. Flat subscriptions feel safer for buyers, but they put vendors at risk. The underlying compute costs fluctuate, and a heavy customer can easily push margins underwater. Hybrid models try to balance the two, to put in predictability for buyers’ forecast, and vendors try to to defend and improve profitability. This mismatch slows progress. Solution: a new generation of pricing models. Simple enough to understand, predictable enough to budget for, but still sustainable for vendors. It could also mean having periodic reviews instead of fixed term pricing for multi year deals. That could mean outcome-based contracts, tiered usage bands with hard caps, or bundled services that absorb variability in spikes. Until AI economics are solved, adoption will remain slower than the technology itself.