What to Know About Pricing Models

If you’re still pricing #GenAI like SaaS, you’re not “innovating” — you’re gambling with your margins. AI-enabled business models are just emerging, but a recent article from Bessemer Venture Partners, "AI Pricing & Monetization Playbook" (in the first comment) nails the core shift: AI doesn’t monetize access; it monetizes outcomes — in a world where every token (and human-in-the-loop) has a real COGS line item. Practically speaking, start with the business model you’re really building: Copilot vs. Agent vs. AI-enabled Service → different economics, different charge metrics. Then pick a charge metric as a strategic choice (consumption → workflow → outcome): tighter value alignment means you’re taking on more cost risk. Next, use hybrid pricing (base + usage/outcome tiers) to balance predictability with upside. Finally, test value-first, then “find the price through friction” (if it’s an instant yes, it’s probably too low). Most importantly, treat pricing as your operating model: it shapes sales motions, CS incentives, what you measure, and how you scale from 10 to 1,000 customers. This resonates strongly with what I’ve been seeing in my research and in the classroom at The Wharton School: in AI-enabled business models, pricing isn’t a “packaging” decision—it’s where strategy, unit economics, and organizational design meet. #AI #GenAI #Pricing #Monetization #BusinessModels #UnitEconomics #GoToMarket #SaaS #Wharton

The question I hear most from founders during Sequoia Capital's Arc program is about #pricing. Pricing is one of the most underutilized levers for startups. Why does it matter so much? It has the most direct impact on revenue, and the moment you establish your pricing, you determine your TAM. Getting the pricing metric right is, by far, the most important one. The key is to imagine the future: when you are a large and successful company, how have you changed the world, and what metric correlates best with your success? Hitch your financial wagon to that metric! If you are Figma, success is all designers using the app; therefore, the pricing metrics is per designer seat. If you are VMware, success is all workloads run in virtual machines; therefore, the right pricing metric would have been a virtual machine. A pricing metric is like the genie in a bottle: once you get it out, it is tough to rein it back or change it. The pricing model is about when and how frequently you charge. Recurrent subscriptions are the predominant model for SaaS apps, and usage-based pricing is the model for infrastructure solutions. Usage-based pricing creates a beautiful alignment of incentives but is less predictable. Upfront credit purchases and commitments are efforts to make usage-based practice more aligned with the rigid corporate budgeting processes. You can be the premium solution or the affordable one. Both are legitimate approaches. But your pricing needs to be consistent with the rest of your strategy: with your product and distribution channels.  You can’t have an affordable solution distributed through an expensive enterprise sales force. In this case, you need to sell either online or through inside sales—the product better be simple and the sales cycle quick. Many technical founders are shy about asking for a lot of money for their product. Don’t be. If customers like the product and it delivers value, they will gladly pay for it. Unless you hear customer complaints that you are expensive, then for sure you are underpricing. Calculate the ROI of your product, and take 20% of that value as your price point. How much it costs you to build the solution should not guide your pricing. But you should do a sanity check that you have a decent gross margin. Most companies start by selling a single package. Over time, they realize that different customer segments have different maturity levels and willingness to pay. To price discriminate between these segments, you need to introduce multiple packages.  Start by creating a customer maturity curve to inform your decisions on how many packages you need. The trick is to have the smallest number of packages to cover the broadest range of customer needs. Your packages will change and evolve quickly as your product matures. 

Your pricing page is the second most viewed page on your website. Yet, most pages fail to convince users to buy. I’ve spent 100s of hours running price experiments… Here are the 5 principles to make your pricing page so irresistible that it sells itself: — 𝗢𝗡𝗘 - 𝗖𝗼𝗻𝘃𝗲𝘆 𝗬𝗼𝘂𝗿 𝗠𝗼𝗱𝗲𝗹 Ask yourself: → What’s the pricing structure? → Who’s the right audience for each plan? → Why should someone choose this plan? If your users can’t answer these questions immediately, you’re losing them. → Talk to your users. Find out what’s confusing. Fix it. → Make your plans make sense because a confused mind never buys. — 𝗧𝗪𝗢 - 𝗪𝗵𝗮𝘁 𝗪𝗼𝗿𝗸𝘀 𝗙𝗼𝗿 𝗢𝘁𝗵𝗲𝗿𝘀 𝗠𝗮𝘆 𝗡𝗼𝘁 𝗪𝗼𝗿𝗸 𝗙𝗼𝗿 𝗬𝗼𝘂 Copying your competitor’s pricing page might seem tempting. But it’s a shortcut to failure. Here’s what you should do: → Dig into your user research. Prioritize experiments that solve your audience’s specific pain points. → Skip the “growth hacks” that pile up downstream problems for sales or support. Your users are unique. Treat them that way, and your results will be too. — 𝗧𝗛𝗥𝗘𝗘 - 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗧𝗵𝗲 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗼𝗳 𝗕𝗲𝗵𝗮𝘃𝗶𝗼𝗿𝗮𝗹 𝗣𝘀𝘆𝗰𝗵𝗼𝗹𝗼𝗴𝘆 Your pricing page isn’t about what you’re selling. It’s about how you’re selling it. Use psychology to guide decision-making: → Offer three plans: good, better, best. → Highlight the one you want them to choose. → Include a free option; it’s a no-brainer for undecided users. → Use the decoy effect: make your premium option shine by comparison. These aren’t just tricks. They’re time-tested ways to make decisions easier for your users. — 𝗙𝗢𝗨𝗥 - 𝗦𝗶𝗺𝗽𝗹𝗶𝗳𝘆 𝗙𝗼𝗿 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗻𝗱 𝗔𝗱𝗱 𝗠𝗼𝗿𝗲 𝗜𝗻𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗘𝗹𝘀𝗲𝘄𝗵𝗲𝗿𝗲 Your pricing page doesn’t need to say everything. And don’t make users “work” to understand your pricing. → Start clean: clear plans, clear benefits, and add depth where it counts. → Use FAQs and deeper sections for additional details further down. → Think Apple: clean, focused, and easy to understand, with details available when needed. — 𝗙𝗜𝗩𝗘 - 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗙𝗼𝗿 𝗨𝘀𝗲𝗿 𝗦𝘁𝗮𝘁𝗲 Your users are in different stages of their journey. So your pricing pages should tailor to their experience with your pricing page. Here’s what to do: → New visitors? Show them why you’re the best choice. → Returning users? Highlight what’s new or offer a discount. → Existing customers? Nudge them toward upgrades tailored to their usage. Also, a little personalization will go a long way: → Use their language, their currency, their context, etc. — Want to dive deeper with 6 best pricing page breakdowns and top experiments of my career? Go here: https://lnkd.in/dvBxfY_q

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

Who Pays for Data Access? The debate that about to define the next decade of Digital Finance as it impacts Open Finance, Emerging Business Models among banks like BaaS and the level of embedded finance adoption Following the recent news that JPMorganChase has struck deals with major data aggregators — including Plaid Inc., Envestnet | Yodlee, ByAllAccounts (part of Morningstar, Inc.), and Akoya — to receive paid access for granting third‑party fintechs access to customer account data, there’s one conversation that should be getting far more attention across GCC, it’s this: “What does a fair, sustainable, pro-innovation model for data access actually look like?” Because the answer decides who builds, who scales, and who gets left behind. Across GCC, regulators are racing forward with open banking, open finance, and broader open data frameworks. Banks are carrying real costs around security, uptime, fraud management, and liability exposure. Fintechs and platforms are trying to build products with predictable unit economics. Consumers want control and transparency. Yet… the pricing models for data access is still unclear❗️ Globally, we are seeing different models emerging - some markets are leaning toward free access, others lean toward cost-recovery. Some push for market-driven pricing and others are going further and embedding it into regulation. And the truth is, each model shapes the ecosystem in very different ways. Three questions we should be debating far more openly. While these questions are partially addressed in principle they are not fully “answered away” 1️⃣ What should be the “public-good” layer of financial data? Which API endpoints should be low-cost or zero-cost because the entire ecosystem benefits? 2️⃣ Where should risk-adjusted pricing apply? If a provider takes on liability for fraud, disputes, or verified identity claims, should pricing vary based on the risk assumed? 3️⃣ How do we avoid entrenching incumbents or suffocating early-stage fintechs? Pricing can quietly shape market structure for a generation. That’s not an exaggeration, it’s basic market design. Why GCC is the perfect regions to lead this debate - not just for our own region but globally 🫡 The GCC is building financial infrastructure at speed. We have regulators willing to experiment. This is the ecosystems where banks, fintechs, and Big Tech players are converging and we all understand that interoperability without affordability is meaningless If we don’t get the incentives right, the next wave of open finance, tokenised finance, embedded services, and cross-border flows will struggle to reach their potential. The question isn’t whether data should be open, it’s how to price openness in a way that helps the whole ecosystem scale GCC has an opportunity to set the global blueprint but we need to have the hard conversation now #openbanking #openfinance #baas #embeddedfinance

Saas pricing is evolving. Many companies try figure out their pricing by copying competitor pricing or debating between conventional options. This misses a couple of key points. Your pricing needs to reflect two critical factors: F͟r͟e͟q͟u͟e͟n͟c͟y͟ Daily active users aren't the same as monthly active users. Why would you price them the same? Accounting software used by your CFO every 2 weeks vs design software used by a junior designer every day meets a different need. B͟u͟s͟i͟n͟e͟s͟s͟ ͟I͟m͟p͟a͟c͟t͟ A bug in your CFO's accounting software is potentially catastrophic. A glitch in your designer's tool? More of an inconvenience. Your pricing should reflect this risk and impact difference, not your opinion of "value". With AI and no-code tools making it easier to build interfaces (I whipped up an API integration in 20 mins the other day), we're seeing a fundamental shift in pricing models. The data backs this up: • 46% of SaaS companies now use hybrid models (subscription + usage) • Only 15% are purely usage-based This isn't all roses though: • 66.5% of IT leaders report unexpected charges from AI/usage-based pricing • Companies underestimate their SaaS spend by 304% So there's certainly hiccups in the transition period. So evolution is moving from seat-based to: • API-based pricing (like OpenAI's token model) • Usage-based pricing (like Snowflake's compute credits) • Task completion pricing (like Intercom's resolution-based pricing) Get this wrong and two things happen: • Churn - customers move to solutions that better match their needs Your pricing needs to align with how customers extract value. • Lost revenue - you're leaving money on the table If your pricing doesn't reflect real value delivered, you're missing out. Because it better reflects the varying value different customers get from the platform. Make sure to run qual & quant research to understand your users and the market rather than speculate. The future isn't about seats - it's about value delivered. As AI capabilities expand, expect even more granular and output-driven pricing strategies.

Sharing this from years of working across Quant, Risk, and Model Development 📊 📊 One thing people outside the industry rarely realize is that different companies use completely different pricing models for the same product. There is no universal model. Each firm chooses a modeling framework that fits its business, liquidity needs, risk appetite, and computational limits. For example: High-frequency trading firms lean toward models that are extremely fast. They prefer closed-form formulas, simplified volatility surfaces, fast-converging lattices, or approximate PDE solvers. Accuracy matters, but speed and stability matter even more, because they reprice thousands of instruments per second. Investment banks rely heavily on models that are robust under stress, not just fast. Their goal is consistency across trading, risk, and regulatory reporting. Buy-side firms such as hedge funds or asset managers can afford more complexity. Their priority is modeling realism because a small improvement in accuracy can materially change PnL. Clearing houses and exchanges focus on transparency and reproducibility. They prefer lattice models, finite-difference PDEs, and well-controlled Monte Carlo frameworks that can be audited and validated easily. Everything must be explainable, stable, and fully documented. There is no single perfect model. There is only the model that fits the purpose, the horizon, the liquidity, and the risk profile of the firm. That’s the beauty of this field. Two companies can price the same option using entirely different engines, and both can be right; as long as the assumptions, calibrations, and risk frameworks are consistent.

Your pricing model is a positioning statement Your pricing model is the first thing buyers read about you. Most of them never make it to the deck. Founders spend weeks on the home page and minutes on the pricing page. Then wonder why conversion won't move. Pricing tells the buyer four things before they've read a feature. Who you think your customer is. The cheapest tier reveals the floor of who you want. What you think you're worth. Anchored against whoever they last bought from in your category. How confident you are in the value. Per-seat versus usage-based versus flat. Each signals a different conviction. How long you've been doing this. Round numbers and clean tiers signal a company still guessing. Off numbers and asymmetric tiers signal one calibrated by real deals. A pricing page does silent work the deck can't reach. The buyer arrives with a question, and the price either confirms what they expected or breaks the frame and makes them think about the company differently. Positioning is doing that work underneath the surface, not the math on the page. I've watched founders rebuild their pricing once a year and stay flat. The teams that actually move conversion treat the pricing page the way they treat the pitch. They rebuild it every time someone new shows them what they're buying for.

The “token cost” panic sounds familiar. We’ve heard it before. In the early days of cloud computing, finance teams were shocked by AWS bills. Compute costs felt unpredictable, invisible, and out of control — until organizations learned to architect for efficiency: right-sizing instances, separating workloads, using reserved capacity for predictable loads and spot instances for the rest. We’re in that same moment with AI. The headlines about token costs ballooning are real — but the framing is wrong. Token spend is a proxy metric. The real question is cost per business outcome. A few principles worth internalizing: → Not every task needs a frontier model. Overusing large LLMs for work that rules or smaller models could handle is the cloud “over-provisioning” mistake all over again. → Workflow design matters as much as model selection. Knowing when not to invoke an LLM is a core AI design skill — and one most organizations are still developing. Many tasks being routed through AI agents today are better handled by deterministic, rules-based workflow automation: approval routing, data validation, status updates, notification logic. These aren’t “dumb” alternatives to AI; they’re the right tool for predictable, structured work. The cost savings are significant, and the reliability is higher. The most efficient AI architectures are hybrid by design — traditional workflow automation handling the deterministic layer, AI handling the judgment layer. → Pricing model architecture matters. When costs scale directly with tokens, context windows, and document volume, complexity gets punished. Action-based models — where you pay for the business task performed, not the tokens consumed — change that calculus. → Observability is non-negotiable. You can’t optimize what you can’t see. Usage analytics, consumption breakdowns by agent and workflow, and model comparison tools are table stakes for responsible AI deployment. The organizations that thrived in cloud didn’t abandon it because of a surprise bill. They learned to architect for it. Same discipline applies here. #AIStrategy #EnterpriseAI #AgenticAI #CostOptimization #DigitalTransformation #Creatio #CreatioAI

The companies building the most interesting AI products right now are all inventing the pricing playbook in real time. There's no obvious right answer on how to structure a credit model, whether to layer in seats or licenses on top of usage, how to use rate limits as a commercial lever rather than just an engineering guardrail, or what the right unit of value even is for a product that didn't exist two years ago. What we've learned from working with some of the leading AI companies is that the question isn't "what should my price be." It's whether you have a system that can help you find the answer — and then keep finding it as the market moves. Something powerful enough to represent genuinely complex models, but usable enough that running an experiment doesn't require filing engineering tickets every time. We've been doing this alongside some of the biggest names in AI, and across that work, the same questions tend to surface — the ones that actually drive the decision to work with Orb. The first thing companies ask us: can you model my pricing? The structures companies are working through right now are genuinely complex. How do you build a credit model that maps cleanly to how customers consume value? When does it make sense to add a seat or license component on top of usage, and when does that create friction you don't want? How do you use rate limits to protect margin without punishing your best customers? These aren't questions with universal answers — and they require a billing system that can represent whatever you land on, not one that forces you to approximate. The second question is: can we change it? Pricing at this stage of the market is an experiment. Companies need the data to understand what's working, and the ability to act on what they learn without a three-month engineering sprint. The pricing model you ship in Q1 probably won't be the one that's right in Q3, and that should be fine. The third question — the one that tends to surface once a company is actually scaling — is: can this hold up? Usage-based billing breaks things downstream. Finance workflows, revenue recognition, enterprise procurement processes — these weren't designed for the kind of dynamic pricing models AI companies are building. We've invested heavily in this layer because we've seen it become the ceiling for companies that got the first two things right.