Pricing Models for Productivity Software

Replit's gross margins went from 36% to negative 14% in two months. Same product. Same pricing. Same team. The only thing that changed: they launched a more autonomous AI agent that consumed more LLM resources than their pricing covered. Traditional SaaS has 70-80% gross margins because one more subscriber costs almost nothing. AI products pay for compute on every prompt. Your best users are your most expensive users. That single fact breaks every pricing model designed for the SaaS era. I mapped pricing across the top 50 AI startups by valuation with Moe Ali. Six patterns emerged. The scariest finding: in most AI products, the P90 user costs 10-40x more than the P50 user. Both pay the same subscription. You're subsidizing your heaviest users with revenue from your lightest ones. And that subsidy grows as power users discover more ways to use the product. Cursor learned this the hard way. They switched from flat 500 requests/month to a credit pool system. A developer burned the entire monthly allocation in a single day. $7,225 invoice. The CEO published a public apology on July 4th. The plan description quietly changed from "Unlimited" to "Extended" twelve days after launch. Anthropic took a different approach. Their $17/$100/$200 tiers map to genuinely different user personas. A casual user, a power user, and a developer replacing an IDE. Those are different products with different willingness to pay. Then weekly rate limits targeting less than 5% of subscribers to push the heaviest users toward the API, where per-token pricing covers actual compute. The pattern across all 50 companies: pure flat pricing is dying. Nearly half use two or three models simultaneously. Here's the full breakdown: 1. Complete AI pricing guide: https://lnkd.in/gdKaQSMk 2. Replit guide: https://lnkd.in/gmA_c_AG 3. AI product strategy: https://lnkd.in/egemMhMF 4. AI agents guide for PMs: https://lnkd.in/eeey5Cxr If you can't estimate your cost distribution across P10 to P90, you're not ready to set a price.

📦 JOBS-LED PRICING CANVAS™ A 10-step framework for transforming feature-led products into monetization-ready, jobs-based pricing models. Built on 4 stages: 1. Product (Discovery Layer) 2. Value (Logic Layer) 3. Customer (Preference Layer) 4. Pricing (Monetization Layer) 🔹 STAGE 1: PRODUCT [Discovery Layer] 🔹 Step 1: Feature Inventory What it is: ▪️ List every feature, tool, and function in the product
▪️ Include hidden, premium, or internal-use features Why it matters: ▪️ Creates a complete picture of what’s being delivered
▪️ Prevents missing monetizable elements 🔹 Step 2: Feature to Plan Mapping What it is: ▪️ Show how features are bundled into pricing plans today
▪️ Expose arbitrary or legacy packaging logic Why it matters: ▪️ Reveals pricing misalignment with value
▪️ Highlights over- or under-incentivized plans 🔹 Step 3: Feature Usage Mapping What it is: ▪️ Track actual customer usage of each feature
▪️ Look for engagement patterns by segment Why it matters: ▪️ Identifies “dead weight” vs “core value” features
▪️ Helps assess ROI per feature 🧠 STAGE 2: VALUE [Logic Layer] 🔹 Step 4: Feature Valuation What it is: ▪️ Qualitatively or quantitatively assign value to each feature
▪️ Use proxies: time saved, revenue unlocked, cost reduced Why it matters: ▪️ Establishes which features are worth monetizing
▪️ Anchors the price-to-value logic 🔹 Step 5: Jobs Identification What it is: ▪️ Identify core Jobs-To-Be-Done (JTBD) your product enables
▪️ Use user interviews, surveys, task analysis Why it matters: ▪️ Shifts the model from features to outcomes
▪️ Connects monetization to customer success 🔹 Step 6: Feature–Jobs Mapping What it is: ▪️ Map each feature to one or more customer Jobs
▪️ Create a logic layer: feature → outcome → value Why it matters: ▪️ Bridges product design with pricing strategy
▪️ Enables bundling and upsell opportunities around outcomes 🎯 STAGE 3: CUSTOMER [Preference Layer] 🔹 Step 7: Rank Jobs What it is: ▪️ Prioritize Jobs by importance and frequency
▪️ Use customer feedback and behavior data Why it matters: ▪️ Surfaces which outcomes matter most
▪️ Enables tiering or segmentation logic 🔹 Step 8: Value Jobs What it is: ▪️ Quantify perceived value of each Job
▪️ Use surveys, conjoint analysis, BWS, or proxies Why it matters: ▪️ Links value perception to potential willingness to pay
▪️ Avoids feature-based pricing traps 💰 STAGE 4: PRICING [Monetization Layer] 🔹 Step 9: Value Capture [%] Analysis What it is: ▪️ Decide what % of value created you can capture
▪️ Compare to industry benchmarks or strategic posture Why it matters: ▪️ Sets pricing defensibility
▪️ Avoids overcharging or leaving money on the table 🔹 Step 10: Pricing Metric / Model What it is: ▪️ Choose pricing metric: per seat, usage, credits, % of revenue, hybrid
▪️ Align it to how value is delivered + Jobs solved Why it matters: ▪️ Ensures pricing scales with value
▪️ Sets the business up for sustainable revenue growth #Pricing

We just set our first pricing model, and it's not the advice you've been hearing on LinkedIn. Everyone is saying you need outcome-based pricing and that seat-based models are dead. We looked at both paths and chose something different. Here's what we considered: Option 1: Traditional Seat Based Seat-based pricing with AI delivering more value per dollar. Safe, predictable, but doesn't match how AI actually works, and how the world is changing. Option 2: Usage-based/Credits Pass through AI costs with markup. Transparent but creates two problems: people hate budgeting (unpredictable) usage, and Pricing 101, day one, first lesson: never do cost-plus pricing if you can avoid it. Cost-plus binds your costs and revenue together in a spreadsheet with some multiplier—you lose the ability to create situations where customers feel they're getting amazing value while you make the math work on the backend. Option 3: Outcome-based Take a percentage of revenue or other business outcomes. Charge for "true work completed". Sounds great until you realize the gap between using a CRM and generating dollars is too big to claim ownership over. Option 4: Something else We chose what we're calling "ergonomic pricing." We took the ideal user experience and are making it our problem to make the math work. Here's how it works: We don't charge for human users. We don't charge for pure usage or outcomes. We charge for "Assistants." When you sign up, we sync and pre-process your team's email history automatically. You can access all of this CRM data for free - the best view-only seat of all time. Assistants add a powerful search, instruction, and tool-calling layer on top of that data platform. Each human can have no assistant, one assistant, or multiple assistants. Different assistant tiers offer different combinations of models, tools, and automation capabilities. This means you can buy "software" from Day.ai (a CRM assistant) and you can also buy digital labor (think a sales engineer with all your company knowledge who can join web meetings and help progress deals). The model gives teams a major wow moment at how much value is offered for free, with a clear path to connecting your human and digital team on a shared data platform. We're not trying to maximize software margins while AI costs are still high. We built 100% transparent pricing from day one. Monthly gets you list price. Annual gets you 20% off. Team growth gets you volume discounts that deepen as you scale. All visible upfront, no negotiation required. This is something I've always wanted to do - complete transparency in how we price and discount. The honest truth is that pricing AI products is impossibly difficult. We've seen the smartest people in SaaS struggle with this. We landed somewhere that feels right: predictable for customers, sustainable for us, aligned with how people actually want to buy and use AI tools. Early response suggests we're getting it right.

92.4% of AI agent companies have figured out something most enterprise software vendors haven't. They've abandoned traditional SaaS pricing entirely. Our latest Global AI Forum research analyzed 60+ Agentic AI companies serving enterprises. The findings will change how you think about AI monetization: The Death of Flat-Rate Pricing → Every AI interaction costs real compute dollars → A power user can cost 100x more to serve than a light user → Yet traditional SaaS treats them identically This is why pure subscription pricing is dying in enterprise AI. What's Actually Working (The Data) ↳ 92.4% use hybrid pricing models ↳ 85.2% pair SaaS with usage-based components ↳ Only 4.5% charge for outcomes ↳ 12.1% run multiple pricing models simultaneously The dominant combination? Subscription + Usage-Based + Freemium + Tiers This isn't experimentation. It's convergence. The Outcome-Based Opportunity Here's where it gets interesting. Intercom Fin ai → $0.99 per resolution (only when customer confirms solved) Zendesk AI → $1.50-2.00 per resolution Salesforce Agentforce → $0.10 per action These companies are betting that value alignment beats predictability. And they're winning. ↳ Intercom reports 66% average resolution rates ↳ ROI is instantly calculable ↳ Buyers pay for results, not access Yet only 4.5% of companies have made this shift. That's a massive whitespace. The Hidden Complexity What enterprise buyers miss: → Cursor's $20/month plan has a credit pool that depletes based on model costs → Windsurf charges flat-rate for their model, token-based for Claude/GPT → Fireflies.ai' "unlimited" transcription has AI credit limits that cost $5-600 extra → Salesforce Agentforce implementations run $50-150k before you pay per action The advertised price is never the real price. What This Means For AI vendors: ↳ Hybrid is table stakes, not differentiation ↳ Outcome-based is the next frontier ↳ First movers will own the narrative For enterprise buyers: ↳ Model total cost of ownership, not sticker price ↳ Push vendors toward outcome alignment ↳ Negotiate usage caps before you sign The Strategic Imperative The companies who figure out outcome-based pricing first will have a meaningful edge. Everyone else will be competing on features while leaders compete on value delivered. Scroll through the full report below Who needs to see this? Tag a founder building AI agents. Tag a CIO evaluating AI vendors. Tag anyone who's been surprised by their AI bill. ♻️ Repost if this changed how you think about AI pricing.

How do you position and price a new AI product when you know users might be skeptical? OpenStore had created OpenDesk - an AI-powered customer support tool designed for small eCommerce brands. But they anticipated challenges: overcoming merchants' natural resistance to AI and making their value proposition immediately clear. So they asked Irrational Labs to help position and price OpenDesk for success. Through our behavioral science approach, we transformed OpenDesk from "just another support tool" into a compelling investment for eCommerce merchants. What behavioral barriers did we need to overcome? ⚠️ AI Aversion: Small business owners hesitated to trust AI with complex customer issues. ⚠️ Mental Accounting: Support tools were viewed as expenses, not investments. ⚠️ Status Quo Bias: Switching from established workflows felt risky. Our 3-step Behavioral Design process helped us address these challenges: 1️⃣ Behavioral diagnosis: We reviewed OpenDesk's prototype, analyzed competitor pricing, and conducted behaviorally-informed interviews with merchants. 2️⃣ Psychological mapping: We identified how to reframe customer support from a cost center to a revenue driver. 3️⃣ Strategic redesign: We created: 📊 A positioning strategy that emphasized customer retention over just solving support tickets 🎨 A landing page design that instantly communicated value 💰 Three transparent pricing models tailored to merchant psychology For the pricing strategy, we explored multiple pricing models and built behaviorally optimized pricing pages to play out how consumers may react and how to mitigate the pain of paying: 💲 Hybrid Pricing Model: A mix of monthly subscription fee and per-ticket charge 🔢 Usage-Based Pricing Model: A simple pay-per-ticket structure 👥 Per-Seat Pricing Model: A flat fee per user per month, offering straightforward costs that made budgeting easier Our recommendations helped OpenDesk successfully launch in a crowded market with clear positioning and a pricing structure that felt fair to merchants. Shoutout to our core team on this project Katie Dove Karl Purcell Pauline Kabitsis Lydia Trupe and also to Gigi Melrose and Eamon Davis at @OpenStore for their partnership 💪 Want to know exactly how we reframed AI tools, which pricing model worked best, and the specific techniques we used to build trust? Check out the full case study in the comments! Want help positioning or pricing your AI product? Hit me up: kristen@irrationallabs.com   #BehavioralDesign #AIStrategy #ProductPricing

As an executive, I’ve participated in extensive discussions about pricing models at companies such as ZoomInfo, Apollo and RB2B. After 15 years I’ve come to the conclusion there are only TWO pricing models that can help you win: Model 1: Pricing Leader: Can you foresee having so much market power in your product category that you become the pricing leader ? All the other vendors use your prices as a reference benchmark. When you change your prices, you influence the entire market. Market power like this is a dream for any company as it allows them to make a lot of money from a handful of high margin deals. Generally premium, enterprise products that cost the most but have a significant moat fall into this category. Model 2: Pricing Disruptor Are you a fast, agile, more modern startup that can deliver similar value at $99/mo where your competitors are charging $30K/year? It is possible to make a lot of money by selling at low margins but to a lot of people. Do you have the ability to create a reasonable brand while disrupting the price and cannibalizing competitor revenues? Pricing disruptors do not need a moat to grow. However, low end pricing advantages are often short-lived and cause a race to the bottom - as somebody new can come in and deliver the same solution for $49/mo. --- There’s also a combination where you build a brand by being a pricing disruptor but find a way to switch to premium products once you have an established brand. I’ve never seen an example of a hybrid approach executed well, but it should work and at RB2B we will certainly take a stab at this : Model 3: First Pricing Disruptor → Then Pricing Leader: One can start as a pricing disruptor and grow into a pricing leader delivering premium solutions on the back of a strong brand. If I were starting a company in 2025, I would disrupt the space with low prices, deliver high quality products, build a strong community, quickly develop a brand and capture instant attention. I would then leverage the brand heat to develop and distribute "additional" products to the same buyer. However, the next set of products can be priced at premium while retaining low prices for the original products. While difficult to execute, it can be total market coup and possibly an efficient way to grow. ––– You need to be intentional about your pricing strategy. Doesn’t matter if you are doing $1M ARR or $20M ARR. If you are not leading or disrupting the prices then you are simply following somebody; stuck in the middle and at the mercy of market forces to grow. Followers struggle for survival and don’t control their own destiny. You need to break free and lead from either end. Find a way to unstuck yourself and take charge of your pricing. Dominate or Resist. In 2025, you have to decide - are you going to be a pricing leader, disruptor or a follower? At RB2B, at the very least, we are going to be a pricing disruptor.

If your company still charges per seat, you need a new pricing model. The math is already working against you. I've watched three pricing revolutions in enterprise software from close range. Perpetual licensing to subscription. Subscription to consumption. What's happening now is the fourth, and it's moving faster than the others. Here's what I'm seeing from the buyer side. Customers are consolidating vendors. Terminating point solutions. Using AI agents to fill the gaps that platforms don't cover. Fewer seats. Lower spend. Same or better output. As a buyer, I love it. As a vendor watching your seat count compress, it should be a wake-up call. Two models are emerging to replace seat-based. Consumption-based. Not new, but accelerating. You pay for what you actually use. Clean, defensible, scales with the value delivered. Outcomes-based. Genuinely new. The pitch is simple: "We automate ten headcounts of work. You save ~$1M. You pay us $250K." Hard to argue with the math. Much harder to operationalize the proof at scale. I'll be honest: I don't think anyone has fully figured this out yet (except potentially Manny Medina?). But the direction is clear even if the mechanics aren't. Here's the underlying problem. Seat-based pricing assumed software was the scarce resource. Your buyer is now using AI to do for $10 a month what your $1,000 seat license used to be the only way to accomplish. That's not a negotiation. That's a structural problem. If you're still charging per seat, what's your plan B? #ai #leadership #gtm #transformation

Per-seat is no longer the atomic unit of software. Consider customer support software Zendesk: companies currently pay per support agent ($115/month/seat), but when AI can handle ticket resolution, the natural pricing metric becomes successful outcomes. If AI can handle a sizable proportion of customer support, companies will need far fewer human support agents, and therefore fewer Zendesk software seats. This forces software companies to fundamentally rethink their pricing models to align with the outcome they deliver rather than the number of humans that access their software. If you are increasing the productivity of labor or usurping it, how should you price this? If every action your customer takes incurs a corresponding cost through an API call, how should you factor that in? How will buyers react to pricing models they’ve not seen before? There’s a lot to consider. However, AI-native companies are leaning into this shift. For instance, Decagon, an AI customer support platform whose AI agents autonomously resolve customer service tickets, offers per-conversation (usage-based) and per-resolution (outcome-based) pricing models to their customers. Both models scale with the amount of work completed (i.e. value delivered) vs. labor (software seats). Read more on Emerging AI Pricing Models in the a16z Enterprise Newsletter with Ivan Makarov and Equals 👇

"Seat-based pricing is dead." I keep hearing this at every SaaS conference, in every blog, on every LinkedIn "hot take". But you know, most have an angle. So I decided to look at the data and form my own opinion. I analyzed 25+ enterprise B2B companies across AI, CRM, support, productivity, and L&D to see what's actually happening with pricing in the AI era. Here's what I found: Credit-based models grew 126% YoY in 2025 (35 to 79 companies in the PricingSaaS 500 Index). That's real momentum. But when you look at how the AI companies themselves price their enterprise products, it tells a very different story. Anthropic (Claude): $25-60/seat/month OpenAI (ChatGPT): $25-30/seat/month Glean: $45-50/seat/month Microsoft Copilot: $30/seat/month Harvey (AI legal, $11B valuation): $1,200/seat/month Hebbia (AI finance, $700M valuation): $3K-10K/seat/year Every single one sells seats to enterprises. If the companies building AI can't find a better model than seats for their own products, that's the strongest signal the market offers. Does outcome pricing work? Yes, but only in specific verticals. Customer support (Sierra, Zendesk, Intercom, Ada) works because the task is binary, the causal chain is short, and it directly replaces headcount. Developer tools (Cursor, Replit) use credits because inference costs are real and variable. But for platforms serving persistent human users with complex, long-causal-chain workflows? Seats persist. Not because companies are behind. Because the economics demand it. The real finding: your vertical determines your pricing model, not whether you use AI. Three things CFOs keep telling us: 1. Predictability is the #1 friction point when buying AI tools (McKinsey & Company) 2. 87% rank AI as critical to operations, but they want it in a budget they can forecast (Deloitte) 3. Credits suppress adoption; when every interaction has a cost, users self-censor I put together a full analysis covering all five pricing buckets, the AI-native wildcards (Sierra, Harvey, Cursor, EvenUp), and what this means for enterprise SaaS strategy. Carousel attached with the key insights. What pricing model is your company betting on? I'd love to hear what you're seeing in the market. Carousel is a summary. The full analysis available, comment or DM me to have access to it. #SaaS #AI #Pricing #Enterprise #B2B #ProductStrategy

Back when the AI boom first kicked off, most startups defaulted to usage-based pricing: charging per token, message, or API call. Simple, familiar (like AWS), easy to ship. But as inference costs plummet this approach is becoming a dangerous race to the bottom. The reality is customers care about outcomes and business value. How you charge is becoming as important as what you build. We’re seeing 4 distinct pricing models as companies move away from pure consumption-based approaches: 1 - Activity-based pricing (pay per use): The default approach we've all seen, charging by tokens or compute usage. It mirrors cloud services but ultimately treats AI as a commodity. 2 - Workflow-based pricing (pay per workflow): Instead of raw usage, you price the completion of structured tasks. An AI drafting and sending an email might cost $0.10 regardless of tokens used. 3 - Outcome-based pricing (pay per result): Customers pay only when a desired outcome is delivered. Companies like Intercom and Zendesk are pioneering this with per-resolution pricing. 4 - Per-agent pricing (pay per "AI employee"): Bill an AI agent like a SaaS seat or virtual hire with a flat monthly fee. This brilliantly taps into headcount budgets, much larger pool than IT budgets (see Joanne’s “Software-as-a-Service”). The further you move from consumption-based pricing toward value-based models, the stickier your product becomes. Pricing strategy IS product strategy. Build it in early, not as a bolt-on later.