Technology Contract Pricing Models

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.

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.

Fixed-Price contracts aren't protecting you... They're setting you up for failure! Most procurement teams think Fixed-Price = safety. Budget certainty. Risk transferred to the supplier. But here's what actually happens: → Your scope isn't as clear as you think → Requirements shift → The supplier protects themselves with change orders → You end up paying more, damaging the relationship AND... You have to spend time reopening/renegotiating contracts... I've watched this play out dozens of times. The real question isn't "which contract type is safest?" It's "which contract type matches my situation?" Here's how to actually decide: → 𝗪𝗵𝗲𝗻 𝘀𝗰𝗼𝗽𝗲 𝗶𝘀 𝗰𝗿𝘆𝘀𝘁𝗮𝗹 𝗰𝗹𝗲𝗮𝗿: Fixed-Price works. You get budget certainty and transfer delivery risk to the supplier. → 𝗪𝗵𝗲𝗻 𝘀𝗰𝗼𝗽𝗲 𝗶𝘀 𝗳𝘂𝘇𝘇𝘆 𝗼𝗿 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴: Time & Materials keeps you flexible. Add "Not-to-Exceed" caps to control costs. → 𝗪𝗵𝗲𝗻 𝘆𝗼𝘂 𝗰𝗮𝗻'𝘁 𝗲𝘃𝗲𝗻 𝗲𝘀𝘁𝗶𝗺𝗮𝘁𝗲 𝘁𝗵𝗲 𝗲𝗳𝗳𝗼𝗿𝘁: Cost-Plus gives transparency for R&D and innovation work. But it requires active oversight. → 𝗙𝗼𝗿 𝗼𝗻𝗴𝗼𝗶𝗻𝗴 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀: Master Service Agreements let you negotiate once, reuse forever while using Statements of Work (SoW) for specific work. Essential for strategic suppliers. → 𝗙𝗼𝗿 𝗿𝗲𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝗴𝗼𝗼𝗱𝘀: Supply Agreements lock in pricing and guarantee supply. → 𝗙𝗼𝗿 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲 𝗱𝗲𝗺𝗮𝗻𝗱 𝘄𝗶𝘁𝗵 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘀𝘂𝗽𝗽𝗹𝗶𝗲𝗿𝘀: Framework Agreements let you compete each project while maintaining pre-qualified vendors. Picking the right contract type is about correctly defining the rules of the game before you play it... But the rules also need to be adapted to the game! Otherwise, you're going to be bickering about the rules instead of creating value for both your organizations... Most contract failures happen because teams pick contract type based on comfort, not project fit. The visual below shows you exactly how to choose based on your situation. Would you add/change anything? Let me know in the comments 👇 _________________________ 𝗣.𝗦. I help companies choose and implement ProcureTech solutions for a living. If you're going to implement a CLM and/or an "AI Agent" to negotiate contracts, you're going to need to define your business rules for when to use which contract type in your business... Is that something you already have...? Every Sunday, I send out a free newsletter which shows you what you need to get results with technology. It's read by 10,000+ Procurement professionals (and counting...) Subscribe here for free: https://lnkd.in/eCeAcP3h

Seat-based pricing is dying faster than most CEOs realize. 12 months ago: 50% of B2B SaaS used flat or seat-based models Today: Down to 27% Projected 2027: <10% The shift? 41% of companies are now using hybrid pricing models. At SaaS Metrics Palooza, I walked through why this is happening, and the framework we use to build hybrid models that actually work. Here's the reality: AI changed who does the work. It's not just humans anymore. It's systems completing tasks, generating summaries, resolving tickets, approving requests, all autonomously. So how do you charge for that? By the person? The product? The outcome? Answer: It depends on who's doing more of the work. The BAM Framework - Base, Allowance, Meter These 3 layers create 5 different hybrid pricing models: 1/ Access Tiers (Base only) - Different AI complexity per tier, fair usage policy 2/ Flat + Unit (Base + Meter) - Platform fee + outcome-based hybrid pricing 3/ Flat + Limit + Unit (All 3 layers) - Most popular hybrid, includes volume + overages 4/ Credit-Based (Base + Allowance) - PLG favorite hybrid, but don't fall into cost-plus trap 5/ Pay-As-You-Go (Meter only) - Pure consumption, AI infrastructure The pattern from 400+ transformations: Companies using Flat + Limit + Unit (Model 3) are seeing the most traction. Why? It gives customers predictability (base fee + included volume) while capturing expansion value (usage after allowance). But, and this is critical, you need safety mechanisms. → Predict usage (dashboards, estimators) → Prevent surprises (match reset timing to contracts) → Protect customers (caps, true-up options, don't penalize usage) The companies winning with hybrid pricing aren't just picking a trendy model. They're designing the model that fits how their AI actually creates value. Based on Tremont's research: Hybrid models are capturing 3-4x more expansion revenue than traditional seat-based pricing. The question isn't "should we go hybrid?" It's "which hybrid model fuels OUR growth?" Which model fits your product? Person doing the work or product doing the work?

I used to think “EPC” (Engineering, Procurement, and Construction) was just about pricing: fixed vs. cost-plus. Then I began to see how many flavors of contract there really are. Pick the wrong one for a FOAK project, and that so-called ‘turnkey’ delivery might just leave the owner locked out. 🔐 In nuclear projects, we spend enormous energy debating technology risk, licensing risk, and supply-chain risk. Yet the delivery and contracting model often decides whether those risks are surfaced early, shared intentionally, or weaponized later through claims and rework. Lump Sum Turnkey (LSTK) EPC frequently remains the default ask. It promises cost and schedule certainty. That promise only holds when design maturity is truly FEL-3 (or CD-2) ready. FOAK nuclear projects rarely meet that bar before contracts are signed. Here is the uncomfortable reality: • LSTK transfers risk on paper, then recaptures it through assumptions, exclusions, and change mechanisms • Cost-Plus enables learning, but only with disciplined owner governance • Target Price or GMP works when scope is real, not aspirational • Progressive or Two-Stage EPC (and CMAR) allow learning before commitment • EPCM and Alliance models reward transparency, but demand capable ownership Across FOAK projects, the most reliable outcomes I have seen follow a consistent pattern: →  Phase 1 focuses on design maturity, licensing risk retirement, credible estimates, defined risk positions, and long-lead procurement alignment →  Phase 2 locks price only after a meaningful amount of uncertainty has been retired Execution model selection is a technical decision, not a commercial afterthought. Choose a delivery structure that matches design maturity, regulatory exposure, and owner capability. You cannot contract away uncertainty. But you can choose when you want to confront it. Risk is either retired during design or monetized during construction. The execution model determines which path you take. 👇 Below are the various types of EPC contracts and some key differences. (If it's hard to read on your phone, let me know below and I'll msg you the high-res PDF or excel file).

The telecom industry has a pricing problem in Agentic AI. And every supplier I speak to has a different story. Operators have already put $3–5B cost programs on the record with AI as a contributor. This is not experimentation. It’s a P&L commitment to their investors. Yet many suppliers are still pricing AI like this..... • Per seat • Per token • Per query None of these map to how a CSP runs its business. A VP of Network Operations does not manage tokens. A CFO does not approve budgets based on API calls. They manage: → MTTR → P1/P2 incident rates → Change failure rates → Cost per subscriber If your pricing model cannot be translated directly into those metrics, it fails the most basic test. “What does this deliver to my P&L?” The reality is straightforward. AI is replacing work, not adding seats Consumption pricing without predictability breaks telco budgets Passing through compute cost is not value-based pricing The industry needs to move to: → Outcome-based pricing (incidents resolved, outages avoided) → Predictable commercial models aligned to annual budgets → Domain-specific OSS metrics as the unit of value Stop pricing the Agent. Start pricing the outcome. We’ve laid this out in detail — including how operators are actually evaluating AI contracts and what pricing models are gaining traction. If you’re selling Agentic AI or OSS into telecom and want to align your pricing with how operators buy, reach out. Happy to share the full analysis and discuss where the market is really going. Link to the Appledore Research report in the comments.

Seat based software pricing is nearing its end. AI agents are stepping in. We are entering a new era where software does not just support human work, it replaces it. As AI agents begin taking on tasks that once required entire teams, the old pricing model based on seats or licenses starts to break. If an AI can resolve a thousand support tickets without a single person involved, who exactly are you charging per seat? This is why the traditional cost plus pricing model is reaching its limit. It was designed for a world where people did the work. But as AI becomes the worker, the only metric that matters is the outcome. The shift is already underway. Companies like Intercom charge per successful AI resolution. Riskified only bills when a fraud decision is correct. These models tie cost directly to impact, not access. Token or compute pricing moves in the right direction, but it still misses the mark. You can waste tokens without ever achieving results. But if you only pay when something valuable happens, the alignment is perfect. Outcome based pricing builds trust. It forces quality, speed, and results from day one. It creates a flywheel where success for the customer is success for the provider. Many have not realized it yet, but AI is not just reshaping technology. It is rewriting the economics of software. #AI #PricingStrategy #OutcomeEconomy #DigitalTransformation #FutureOfWork #SaaS #TechLeadership #InnovationMindset #BusinessModelInnovation #CustomerSuccess

Outcome-based pricing used to be a non-starter. 𝗡𝗼𝘄 𝗶𝘁’𝘀 𝘀𝘁𝗮𝗿𝘁𝗶𝗻𝗴 𝘁𝗼 𝘄𝗶𝗻. This Gartner chart from ~2 years ago ranks the most common pricing models for IT services across flexibility, transparency, predictability, and risk. Look at the Business Outcome row: 🟡 Just “OK” on flexibility and transparency 🟡 Predictability? Also “OK” 🔴 Provider risk? Brutal. This model looked rough. Too much ambiguity. Too much risk. Too hard to measure and deliver outcomes consistently. But that was before AI changed the game. Business outcome pricing only works when your delivery system is tight: → Requirements captured clearly → Artifacts generated instantly → Knowledge carried across every handoff → Every decision traceable, every change aligned That used to require heroic effort. Now, with agentic systems like Auctor, it's the norm. We believe outcome-based delivery will define the next decade—not because it’s trendy, but because AI finally makes it possible. ✅ Flexibility comes from intelligent agents that adjust as projects evolve ✅ Transparency comes from clear traceability and centralized context ✅ Predictability comes from repeatable patterns and aligned artifacts ✅ And provider risk? It’s mitigated when execution isn’t guesswork The model didn’t change. The tools did.

Biopharma is undergoing a radical shift from software-as-a-service to services-as-software—where AI isn’t just a dashboard, it’s a doer. Flat license fees for passive tools are outdated. In an industry where Improzo copilots are generating call summaries, surfacing KOL insights, and automating field actions, charging per seat misses the point. 💡 Improzo is the AI-native execution layer built for this new reality—embedding automation and intelligence directly into the systems teams already use, without the need to replatform. Here are the three pricing models we’re seeing emerge with biopharma clients: 🚀 Adoption-Led Entry (Implementation + Light License + Value Ramp): A low-risk, high-speed path to activation. Customers are open to starting with a modest implementation and license fee, and let pricing grow with the value Improzo delivers. 🎯 Outcome-Based: For strategic clients who want accountability tied to results—revenue impact, hours saved, or patient pull-through. This model ensures pricing tracks with measurable outcomes. ⚙️ Workflow-Based (Experimental): Ties pricing to tasks—like documents processed, HCP actions triggered, or claims handled. A few clients are exploring this model, but it’s still early-stage and best suited for modular, repeatable use cases. At Improzo, we don’t believe in pricing access—we price execution. Because in biopharma, outcomes aren’t driven by dashboards. They’re driven by what gets done. cc: Abhishek Trigunait Jane Urban Ashok Natarajan Michelle Bookholdt Narasimha Rao Taduri Kalyani Nivsarkar Charu Chawda Bhagat Peter Dussias

Is seat-based pricing going away? Not yet. Across 170K+ contract line items from 2020 to 2026 in Vendr’s dataset, seat-based pricing has been surprisingly stable at 43-48% of all line items. There is no dramatic migration away from per-seat pricing. Non-seat models like usage-based credits, MAUs, GB, and platform fees make up the majority at 54-58% of line items. What is changing is how suppliers package pricing. Consumption and flat-rate models are being offered alongside per-seat pricing, not as replacements. The real inflection point would be a structural change in how contracts are written. When AI agents truly replace human users, we would expect to see fewer seat-based line items or materially lower committed seat volumes in contracts. That shift may be on the horizon, but in the data today, seat-based constructs and committed seat counts remain largely unchanged at scale. The per-seat model is not collapsing (yet). It is being supplemented.