Enterprise AI: Build or Buy?
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Enterprise AI pricing. It's broken. You've seen the pattern. Your team identifies a promising AI use case. You talk to vendors. They quote you per-seat licenses, consumption fees, or massive upfront project costs. Six months later, you're paying for software that half your team doesn't use, your token costs have spiraled out of control, or you're still stuck in implementation while the invoices keep coming.
Meanwhile, nobody can tell you whether the AI is actually delivering ROI. You're paying for access, not results. And if it doesn't work? That's your problem.
There's a different approach. Instead of paying upfront and hoping for the best, what if you could test a solution on your own data, validate that it works, and only then commit to a predictable annual fee with unlimited users and usage? No per-seat math. No consumption surprises. Just solutions that work, priced in a way that makes sense.
That’s what outcome-based pricing is all about. And we want to spend some time explaining what it really means for your organization.
Before we talk about the alternative, it's worth understanding why the standard pricing models create problems for enterprise buyers.
Seat-based licensing sounds straightforward. You pay per user, per year. But think about what this actually incentivizes. The vendor gets paid whether your users log in or not. Whether they get value or not. Whether the AI helps your business or sits on the shelf collecting dust.
Here's the deeper problem with seat-based models. The more effective their AI becomes, the fewer human agents you need, which means fewer seats to sell. Their revenue model is literally at odds with making AI that works too well. Think about that.
Consumption-based pricing seems better on the surface. You pay for what you use. No waste, right? Except consumption doesn't equal value. You could run queries endlessly without extracting any real business impact. You could also "leave the water running overnight" and blow out your cost structure without anyone noticing until the invoice arrives.
Project-based and consulting fees put all the risk on you. Large upfront investment before seeing any results. Implementation cycles that stretch months to years. And if the project fails or goes over budget? The consultants have already been paid. They move on to the next engagement while you're left holding the bag.
None of these models deliver business outcomes. They're designed to benefit the vendor, not validate the business case.
The problem with how vendors talk about "outcome-based" pricing is they use the term loosely. Some mean performance bonuses on top of traditional fees. Others mean success-based consulting where you still pay regardless of success.
Real outcome-based pricing is more specific. It means you don't commit financially until the solution delivers measurable business value. You can demo it, test it with your own data, and validate that it works, all before signing anything.
The best implementations of this model go a step further. Instead of complex usage-based billing after you commit, you get solution-based pricing. A predictable annual fee per solution, with unlimited users and unlimited usage. No counting seats. No tracking tokens. No surprise invoices.
The tangible difference is that the vendor has real skin in the game during evaluation. They're not incentivized to sell you something and move on. They're incentivized to prove the solution works, because that's how they earn your business. What does this look like in practice? You tell the vendor what you're trying to accomplish. They build a tailored solution.
The number one barrier to enterprise AI adoption isn't technology. It's risk. Decision-makers are tired of pilots that never scale, implementations that drag on for years, and vendors who disappear after the contract is signed.
Outcome-based pricing removes that risk. You experience real impact before committing. If the solution isn't generating the results you need, you're not stuck paying while the vendor blames your team. And once you do commit, solution-based pricing gives you cost certainty. No usage-based surprises. No seat-counting headaches. Just a predictable annual investment you can budget for.
It also accelerates everything. When there's no upfront payment, there's less procurement friction.
When you can test with real data before committing, decisions happen faster. When results are visible in days rather than months, the business case writes itself. Your CFO stops asking "what if this doesn't work?" because you've already proven that it does. And they stop asking "what if usage spikes?" because the annual fee doesn't change.
The "try before you commit" model works best when success criteria are clear, measurable, and agreed upon upfront. The good news is that enterprise AI use cases map naturally to measurable outcomes.
Knowledge on-demand translates to time saved finding information, decisions accelerated, and questions resolved without escalation. If your employees spend 40% of their time searching for information across disconnected systems, reducing that number with tailored enterprise search is a clear, demonstrable result you can validate before committing.
Data extraction and abstraction measures documents processed, manual hours eliminated, and accuracy rates. When you're pulling entities, clauses, and obligations from thousands of contracts, you can see the volume of work automated during evaluation and know exactly what you're getting.
Automation and AI agents track tasks completed, error rates reduced, workflows executed, and escalations avoided. When an agent handles a customer inquiry end-to-end without human intervention, that's something you can observe and measure before you sign anything.
Reporting and observability counts reports generated, insights delivered, and anomalies detected. If your team used to spend two days compiling a weekly operations report and now it's generated automatically, you'll see that value during the trial period.
The key is validating these outcomes during evaluation, before any financial commitment. Once you've seen the results, the decision to commit to an annual solution fee becomes straightforward—you already know what you're getting.
If you're skeptical, you should be. This sounds great in theory, but you've probably been burned before by vendor promises that didn't hold up. Here's how the real concerns get addressed.
"How do we know what success looks like?" This gets defined before evaluation begins, not after. Both sides agree on what the solution should accomplish, how it will be measured, and what results would justify commitment. Clear criteria upfront mean both sides know when the solution has proven itself.
"What about budget predictability after we commit?" This is where solution-based pricing shines. Unlike consumption models where costs fluctuate with usage, a solution-based annual fee is completely predictable. You know the number on day one, and it doesn't change based on how many people use it or how many queries they run. Finance loves this because they can actually plan around it.
"What if we want to expand to more users?" With unlimited users and usage, expansion is already built in. You don't need to go back to procurement for more seats. You don't need to renegotiate. Your entire organization can adopt the solution without additional per-user costs.
"What about data security during the trial period?" This is a real concern. The answer is architecture. Solutions that keep data safe within your perimeter, whether on-prem, private cloud, or a secure managed environment, eliminate the need to share sensitive information outside your security boundary. You can validate AI solutions on your actual data without compromising governance.
This model isn't for everyone. It works best when you have clear use cases with measurable outcomes, when you're tired of AI experiments that never scale, and when you want the freedom to expand adoption without per-seat negotiations.
It's a good fit if you've been burned by pilots that never made it past proof-of-concept. If you need to prove ROI before expanding the budget. If you want cost predictability after commitment. If you value the ability to scale adoption across your organization without going back to procurement for every new user.
Ask any AI vendor you're evaluating:
If they can't answer these questions clearly, they're offering traditional pricing with marketing spin.
At Unframe, we've built our entire model around solution-based pricing. You tell us the use case. We build a tailored solution. You test it on your data, with no restrictions. And when you're ready to commit, you pay a predictable annual fee per solution. With unlimited users and unlimited usage. Just check out all of the success stories we have thus far.
No per-seat licenses. No consumption surprises. No going back to procurement every time you want to expand access. Just solutions that work, priced in a way that lets you scale adoption without scaling costs.
The first step is simple. Talk to us about your specific situation. Tell us what you're trying to accomplish. We'll show you what's possible, how fast you could move, and exactly what solution-based pricing would look like for your use case.
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