Why pay-as-you-go API pricing breaks under AI workloads

In our previous piece, we demonstrated why AI can't reason about location and how layering in additional APIs and data sources can help solve this problem. In this piece, we focus on one of the challenges that arise when you make the move to integrate APIs and layer new data into your product.

The pricing crisis inside AI products

There's a quiet crisis brewing inside AI products and AI teams.

Teams are shipping AI search, AI agents, and AI-driven decision-making tools at record pace, harnessing LLMs, integrating APIs and layering data sources.

But many are discovering that API bills don't behave the way the rest of their stack does. They spike, scale unpredictably, and turn iterative experimentation into a real financial risk.

Many APIs, particularly in geospatial and location intelligence, charge API usage per-call. This means that every time you or your agent calls the location API, you are charged a fee.

How AI changes the shape of API usage

User-facing products

In a traditional product, API usage is shaped by humans. Your users interact with your product – a click, a search, a filter – a query goes out to an API and a response comes back. Volume is roughly proportional to active users, and active users grow predictably.

AI workloads break that pattern. An LLM doesn't behave like a normal user.

LLM-powered agents reason through a problem step by step: calls an API, looks at the result, decides it needs more information, calls again, and refines its approach.

A single user asking an agent, "Where should I open my next retail location?" might trigger one API call or fifty, depending on how the agent reasons through the question. Good agents then ask follow-up questions or offer more ways to refine and filter the response – and the cycle continues.

This isn't a failure of LLMs; it's a feature. The whole point of agentic AI is that it can do more, and do it quickly – explore more options, evaluate more results, try different approaches, ask more questions. But every time the agent does more, the API pricing meter ticks. And the meter doesn't care whether the agent's reasoning was useful or wasted, you're getting charged either way.

Multiply that across thousands of users worldwide and queries that vary in how much reasoning they require, and API usage stops being predictable. This is when pay-as-you-go starts to really hurt.

Building with LLMs

The same dynamic shows up before anything ships.

Traditionally, the developer is in control of the calls. You write the code that hits the API, so you know exactly when a call fires, how many, and why. Costs during development are deterministic.

Building with an LLM removes that control. You hand the model a goal and a set of tools, and it decides how to use them: reasoning through the problem, calling an API, weighing the result, calling again, trying another route. Those calls aren't ones you authored line by line they're a result of the model. And the more capable the model, the more it explores.

That changes the economics of experimentation. Every test run, every prompt you tweak, every "let's see if this works" sends the agent off – and a single prompt might mean running it dozens of times in an afternoon, each run firing a cascade of calls. The cost of building is no longer fixed by your code; it scales with how much the model thinks.

Under pay-as-you-go, that makes iteration a liability. The exploration that makes an LLM build well, trying different approaches, reasoning further, chasing the better answer, is the kind of behaviour the meter charges for.

Every "let's give it a go" carries a price and the experimentation AI development depends on gets discouraged.

A pricing tug-of-war

Pay-as-you-go pricing creates a structural misalignment between what the vendor wants and what the customer wants.

On one end, the vendor wants to pull you towards maximising usage, maximising API calls and growing their revenue. On the other end, API customers want to minimise usage to keep costs low and predictable.

Both sides are pulling. Somewhere in the middle, product, engineering, finance, and leadership team compromise, and wind up with a product that's designed around budget anxiety rather than user value.

Do we let the agent take an extra reasoning step here, even if it might cost us? Can we ship this feature, knowing that one runaway loop in production could blow the month's budget? Should we trial this new idea, despite the additional costs of running the test?

These questions are about how to insulate the business from the API pricing model. And the more capable AI becomes – and the more users expect from it – the more these questions come to dominate the roadmap.

What we believe pricing should do

Pricing isn't just commercial. It shapes how people use technology, build, and iterate. The right pricing model for AI workloads is one where:

• You aren't charged for your product getting better and gaining more users: Caching, optimisation, and intelligent architecture should be encouraged to support growth, not financially punished.
• You can build without watching the meter: We're in an exciting new era of AI growth. Pricing that taxes product iteration is pricing that taxes innovation.
• Costs are predictable: AI workloads and API calls can be unpredictable, but the bill shouldn't be.

That's the framework we've built TravelTime around. We do fixed pricing, full caching rights, and no per-call pricing meter to design around.

It's not the obvious commercial choice in our market (pay-as-you-go is much easier to operate on the vendor end!). But it's the model AI builders actually need.

Where TravelTime fits in AI builds

TravelTime's API pricing model is one of the reasons we deliver the best location infrastructure for building AI products.

Fixed pricing means experimentation isn't penalised. Caching rights mean iteration isn't taxed. But the deeper change is to the relationship between us as a vendor and our customers: the whole commercial relationship is built on the assumption that your product getting better is a good thing for both of us, not a problem to be metered.

The questions that used to dominate the roadmap stop being budget questions, and start being questions about how you can make the product better.

You can let the agent take the extra reasoning step, ship the feature, trial the idea. And make decisions based on whether it's good for your users, not whether it'll blow the month's bill.

That's what makes the pricing model one of the most important things to get right when you build with AI. It's the difference between a product you can build without compromise and one you're constantly defending against the bill.

Build AI without watching the meter

Talk to us about how fixed pricing and caching rights work for your AI project. Or try the API with a free trial key.