How to Use AI for Pricing Decisions

Since posting our guide on how to price AI software, I've been inundated with founders looking to talk through pricing strategies for their startups. Unfortunately, most are skipping the critical first step.   They are spending lots of cycles iterating on "how" to charge (e.g., usage-based, outcome-based, hybrid, etc).   But they're neglecting the most foundational element: how much to charge. We're finding that with proper ROI frameworks, AI products are able to capture 25-50% of created value, which is significantly higher than traditional SaaS's 10-20%.   Here's how the best founders are achieving these pricing levels:   1️⃣ They bring pricing discussions into the sales conversation early   The worst thing is waiting until procurement to talk about pricing. The role of enterprise procurement departments is to minimize spend, not to assess value. They lack budget categories for 'AI that does the work of 3 people'—so they'll try to squeeze you into their existing software line items.   When prospects seem hesitant to discuss ROI upfront, don't push. Instead, propose a value audit session. Sit down with them after they've used your product for a few months and calculate ROI together based on real usage data.   I've seen founders use this brilliantly during negotiations: "I'll give you a discount, but in six months we need to do a value audit." It's a fair trade that shifts the conversation to outcomes.   Here's a bonus move: always offer outcome-based pricing even if customers don't choose it. Simply presenting it signals confidence and willingness to share risk. When positioned alongside a fixed fee, it makes the fixed fee look fair by comparison.   2️⃣ They calculate ROI holistically, not just hard savings   Most founders focus only on labor reduction or vendor spend cuts. But that leaves money on the table.   Factor in the opportunity cost of time efficiencies. Include potential implementation cost differences compared to traditional SaaS. In many cases, AI products deploy faster and cheaper, which should be reflected in your ROI calculations.   Work with buyers to agree on ROI inputs upfront. Once they've signed off on the framework, challenging the outputs becomes much harder.   3️⃣ They use the "acceptable, expensive, prohibitively expensive" technique   Rahul Vohra used this exact approach from Madhavan Ramanujam’s "Monetizing Innovation" to price Superhuman:   To gauge willingness to pay, ask three questions: 1. "What would be an acceptable price?" 2. "What would be an expensive price?" 3. "What would be a prohibitively expensive price?"   Willingness to pay typically lands near the "expensive" point.   --   I've watched too many brilliant AI founders build incredible products only to leave millions on the table by treating pricing level like an afterthought.   Don't be one of them.   P.S. The complete pricing guide (with the decision framework and tactical playbooks) is live on our website. Link is in comments.

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

Your AI agent just closed a deal, processed 40 claims, and rewrote a policy doc. Are you still going to charge per login? SaaS pricing made sense when software was a tool. But in the services-as-software world, the AI is actually doing the work, so how you charge needs to reflect that. Many leading companies like Harvey and Clay are rethinking traditional seat-based pricing. They’re finding ways to tie pricing more closely to the value delivered and work accomplished. Here’s the spectrum we’re seeing most AI companies fall on: ▶ Seat-based: Clean and predictable, but often disconnected from the gains the product delivers. ▶ Usage-based: Charges for tokens, minutes, or queries - transparent, but puts the burden on buyers to connect usage to ROI. ▶ Workflow-based: Priced per job done - docs processed, tickets closed, reports generated. This links revenue to actual work accomplished. ▶ Outcome-based: Tied to results - deals closed, hours saved, revenue unlocked. In theory, the cleanest alignment with value but hard to standardize in practice. Most AI startups aren’t ready for pure outcome pricing, and that’s okay. But breakout companies are designing pricing around what their product does and what customers would lose if it went away. 🧠 Harvey charges law firms roughly $1k per lawyer each year, but renewal talks are all about hours saved. ⚙️ Clay sells GTM automation, but equips its sales team with practitioners who actually do the work so the value starts accruing even before the contract is signed. In the end, AI buyers want results. If you’re building a services-as-software company, you’re doing the work and your pricing should reflect that.

𝗕𝗲𝘆𝗼𝗻𝗱 𝗦𝘂𝗯𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻𝘀: 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗘𝗰𝗼𝗻𝗼𝗺𝘆 🚀 At #GoogleCloudNext25, we unveiled tools like ADK and A2A Protocol to fuel AI innovation: 🔹 𝗔𝗴𝗲𝗻𝘁 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗞𝗶𝘁 (𝗔𝗗𝗞): Simplifies building advanced AI agents.  🔹 𝗔𝗴𝗲𝗻𝘁𝟮𝗔𝗴𝗲𝗻𝘁 (𝗔𝟮𝗔) 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹: Enables cross-platform compatibility.  🔹 𝗔𝗴𝗲𝗻𝘁 𝗘𝗻𝗴𝗶𝗻𝗲 & 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗠𝗮𝗿𝗸𝗲𝘁𝗽𝗹𝗮𝗰𝗲: Ensures reliable deployment and broad reach. These innovations make enterprise-ready AI agents more accessible than ever. But the question dominating my conversations with Google cloud partners over the last 48 hours? 𝙃𝙤𝙬 𝙙𝙤 𝙬𝙚 𝙢𝙤𝙣𝙚𝙩𝙞𝙯𝙚 𝙩𝙝𝙚𝙢? 💡 Traditional SaaS subscriptions are a start, but they often miss the full value AI agents deliver. The future lies in pricing tied to actions and results. Here are three models gaining traction: 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 ⚙️  • Charge per task for a transparent value exchange.  • Example: A travel AI charges $5 per itinerary booked, verified by customer confirmation.  • Challenge: Requires robust tracking to ensure trust. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗕𝗮𝘀𝗲𝗱 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 🎯  • Earn payment only when measurable goals are met, aligning incentives.  • Example: A logistics AI for a retailer earns $0.50 per mile saved on delivery routes, tracked via GPS data.  • Challenge: Customers need clear proof the agent drove the result. 𝗛𝘆𝗯𝗿𝗶𝗱 𝗣𝗿𝗶𝗰𝗶𝗻𝗴 🔄  • Blend subscriptions with variable fees for flexibility.  • Example: A weather platform charges $500/month for data access, plus $50 per hyper-local forecast delivered by an AI.  • Challenge: Balancing fixed and variable fees can complicate pricing. 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? The best path depends on your agent’s role, audience, and measurable impact. As we refine tools for building and deploying agents, monetization will evolve too. I’m excited to see how we innovate in this space. Which option would you test first—execution-based, outcome-based, or hybrid? Share your thoughts below or DM me to swap ROI ideas! 💬 #AI #Monetization #AIAgents #Innovation #DigitalTransformation #enterprise #GoogleCloudNext25

Missed RiseUp summit’s talk about #Precision_Pricing using #AI? Here are the key take aways: When you manually set the price points of your products, you will have the challenge of navigating your way out of two extremes: 🔻1. If you set your price too high, you will lose sales, which will negatively affect your total profit 🔻2. If you set your price too low you will indeed increase your sales volume but your final profit will also be negatively impacted So where is this optimal price point? 🚀 #Artificial_Intelligence is here to help you maximise your profit by predicting this exact sweet price spot But how exactly is that solution built? ⭐️ #Data: We collect and consolidate data about your historical sales, prices, competitors information, inventory, product characteristics, marketing campaigns and much more to feed the AI models ⭐️ #SalesForecast: We train a #MachineLearning model that forecasts your sales volume given all of the different factors such as seasonality, trends, advertisements, and even weather forecast ⭐️ #ElastictyModel: Taking into account the historical discounts data and the historical product sales, we train the model to predict how would your sales volume change at all of the different price points ⭐️ #Simulation: We then give the user the option to self-test different pricing scenarios to see the effect on the sales volume and on the final profit ⭐️#Optimization: We then build an optimisation engine on top that smartly selects the best price points that optimises the final profit 🚀Just imagine your #AI engine doing this automatically for the thousands of products you have on daily basis to set your pricing and promotions.. Ain’t mind-blowing enough? Let’s go even one level deeper where this AI engine can also: 👨👩👧 Understand the different kind of #customer_segments that you are having and personalise the promotions based on their expected behaviour. After all, this AI engine can understand who are your discount-optimisers and who are your quality-hunters, and how would each segment react to the price change or the timely promotion 🙎♂️If you have enough data about the #individual_customers (such as in online retail or telecommunication sectors) you can scale those models to predict how each individual use will react to a price change, and send individualised promotions for each and every customer Ain’t mind-blowing enough? 🚀 How about using #GenAI to personalise the messaging and the marketing of the personalised offers?! 🤩 Mind blowing enough? #ThePowerOfPrecisionPricing —- PS: Pricing and promotions have been used interchangeably to simplify concepts

From Fixed Pricing to Adaptive Pricing: The AI Shift That Changes Everything For decades, pricing was static. One price. Set by humans. Reviewed monthly. Applied to everyone. Today, AI has changed that model entirely. Adaptive pricing systems now: • Analyze demand, behavior, competitors, time, and events in real time • Update prices in milliseconds • Tailor pricing to segments or users • Capture high-demand moments automatically • Scale across thousands of products This is not just about higher revenue. It is about intelligent commerce. The real shift is from: Manual pricing decisions → Autonomous pricing systems Historical data → Real-time signals Periodic updates → Continuous optimization In fintech, SaaS, e-commerce, and payments ecosystems, this capability is becoming core infrastructure. The question is no longer “Should we use dynamic pricing?” The question is: How intelligent is your pricing engine?

𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗶𝘀 𝗯𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 𝗽𝗹𝗮𝘆𝗯𝗼𝗼𝗸. The SaaS seat based pricing construct was built for software that waits for humans to use it. Agentic AI is forcing a fundamental redesign of how enterprise software gets priced, sold, and evaluated. My colleagues Maria Bodiu, CFA, Max R. Mailman, and Marsha Sugana and I explored this question with founders building in the space. Three models are competing for dominance. 𝗢𝘂𝘁𝗰𝗼𝗺𝗲-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 is the most structurally sound. Enterprises pay per resolved ticket, completed workflow, or measurable result. The vendor carries performance risk; the ROI is immediate and legible. PolyAI CEO Nikola Mrkšić made the point well: pricing has to be built from the customer's cost basis, not the vendor's product metrics. That requires a level of commercial rigor most software companies aren't used to. 𝗖𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 remains the dominant near-term reality — token usage, API calls, compute. It's operationally simple and aligns vendor costs with revenue, but it measures activity, not value delivered. Customers who find success with it quickly want off it. Skan AI's Avinash Misra noted that enterprises are already pushing to run agentic workloads on local hardware to avoid long-term vendor dependency. Predictability and control are becoming procurement requirements, not afterthoughts. 𝗦𝗲𝗮𝘁-𝗯𝗮𝘀𝗲𝗱 𝗽𝗿𝗶𝗰𝗶𝗻𝗴 is being retrofitted into the agentic era, framed as a productivity multiplier. It offers CIOs budget predictability and familiar procurement pathways — but it systematically underprices exponential value. A per-seat license doesn't reflect the economics when one agent replaces the output of an entire team. The deeper issue runs beyond mechanics. Pricing is increasingly how strategic identity gets communicated. Whether a company is perceived as infrastructure, enterprise software, or labor replacement is often determined more by how it charges than by what its product narrative claims. There is no single right answer on agentic pricing. But the companies that fail to align their commercial model with customer economics will either leave significant value on the table or lose deals to those who have. Full perspective linked in the comments

One man is responsible for pricing many of your favorite products. $50 billion of market cap worth of products. Madhavan Ramanujam believes the winners in AI will be forced to execute on pricing from day 1. At Gamma, we live by his framework. His pricing playbook for AI-native startups: 1. Price from Day One - or Stay at Zero Madhavan advised 400+ companies. Here's what kills them: they wait to charge. Once users experience value for free, their willingness to pay anchors at zero. You've trained customers to believe your product is worthless. In AI this is especially dangerous - every user action has a cost. 2. You Need Market Share × Wallet Share After studying 50+ unicorns, Madhavan found this formula: Market Share × Wallet Share = Enduring Value. Most founders chase one. High users but no revenue = you're a feature. High revenue but few users = you're a consultancy. Winners optimize both from day one. 3. Your Unit Economics Are Fundamentally Different Traditional SaaS hits 70-85% gross margins at scale. AI companies? Every API call costs money. Here's the paradox: AI can capture MORE value in pricing than traditional SaaS (25-50% vs 10-20%). But only if you price strategically from day one. Unlike SaaS, your infrastructure costs scale with usage. "Grow now, monetize later" in AI means bankruptcy. 4. The Move That Separates Winners from Commodities Madhavan shares how one founder 4x'd their deal overnight: stop anchoring on usage, anchor on outcomes. His 2×2 framework reveals why: plot your AI on Autonomy (how independent) × Attribution (connection to outcomes). Most AI startups are stuck bottom left - low autonomy, weak attribution. They're building features, not businesses. Winners move top right - high autonomy, clear outcome attribution. That's when you can charge for business impact instead of API calls. Example: Intercom's Fin charges $0.99 per resolved conversation. Not per query. Per outcome. When you charge per seat or query, buyers negotiate you down. Charge based on business outcomes - revenue generated, costs saved, hours eliminated - and pricing discussions flip entirely. The ideal is outcome-based pricing that captures 4x the value. Some categories start with seats to build momentum (we do at Gamma), but the key is knowing your path to value-based pricing - and having the metrics to prove it. — Price from day one if you're building AI. Don't bet on costs coming down. User expectations will only rise. Madhavan's principles come from studying 400+ companies and 50 unicorns. Your product got you users. Your pricing will build you a business. h/t Lenny's Newsletter for the deep dive

Exploring the Possibilities: AI Agents as Pricing Analysts AI’s potential in business is rapidly expanding... with new capabilities emerging from platforms like OpenAI. One fascinating example from OpenAI is the development of a Personal AI Pricing Analyst for retail businesses. Let’s be clear: not every organization needs a pricing agent. But this use case demonstrates just how customizable and powerful today’s AI tools have become. Imagine you own a yoga attire shop. With these new AI capabilities, it’s now possible to build an AI agent that can: - Collect real-time competitor pricing data (think Alo Yoga, Uniqlo, and others) - Analyze your store’s sales to surface high-performing products and revenue drivers - Automatically flag mispriced items outside a set market range - Generate plain-language reports (no technical jargon, just actionable insights) What once required heavy resources and months of development is now achievable in a fraction of the time using solutions like OpenAI’s responses API, Code Interpreter, and web integrations. While your business might not need this exact solution, the broader point is clear: AI agents are opening new doors for custom automation and decision support. Curious about other innovative use cases or want to see how these tools could work for you? Let’s discuss in the comments below, I’d love to hear your thoughts.