Integrating AI model APIs into your software: where to start

Integrating AI model APIs into a piece of software has become technically simple: a few lines of code and you get a response back. The hard part is everything that comes after, when that call goes to production, with costs piling up and answers that are wrong every now and then. Here are the foundational choices we make when we put an LLM inside a project.

Choose the model for the task, not for the leaderboard

The first instinct is to reach for the most powerful model on the market. In production we reason the other way around: start from the task and look for the smallest model that handles it reliably. Classifying a ticket, extracting fields from a document or summarising a text are tasks the fast, cheap models handle well; we reserve the top-tier models for the steps where reasoning matters.

Two practical pointers:

• evaluate providers on what surrounds the model too: documentation, API stability, data-use policies, availability in your region;
• do not hard-code the model name all over the codebase. Centralise the configuration, so changing model or provider remains a single-point operation.

The big providers update their price lists and models frequently: an architecture that leaves you free to switch is worth more than any initial choice.

Put a layer between your software and the model

The most common architectural mistake is calling the provider's API directly from wherever it is needed in the code. It works as long as it is an experiment; in production it pays to route all calls through a single internal layer, which takes care of:

• managing prompts as versioned artifacts, outside the code, so they can be edited and tested without a release;
• logging every call: input, output, model used, timings and tokens consumed. Without logs you can neither debug nor control costs;
• applying the cross-cutting rules: timeouts, retries, per-user usage limits, filtering of sensitive data before sending;
• standardising the interface towards the rest of the application, so the day you change provider you touch one module and not fifty.

This layer is also the right place for caching: many user requests resemble each other, and an already computed answer is the cheapest and fastest call there is.

Keep costs under control from day one

With pay-per-use APIs the bill grows silently, and explodes with the product's success. The practices that have saved us from bad surprises:

• measure tokens per feature, not just the total: you will find that a single feature often accounts for most of the spend;
• shorten your prompts: useless context repeated on every call is money leaving with every request;
• set per-user and per-day limits, with alerts when spend accelerates;
• use different models for different steps of the same flow, reserving the expensive one for the final step;
• take advantage of the caching and batch-processing mechanisms providers offer when the use case allows it.

The question to ask about every AI feature: how much does it cost to serve one active user per month? If you cannot answer, the feature's business model does not exist yet.

Errors, timeouts and fallbacks: designing for failure

An LLM in production fails in ways traditional software does not: the API can respond slowly or return errors under load, and the model can return output outside the expected format while having responded correctly at the HTTP level. Both levels must be handled:

• explicit timeouts and retries with increasing backoff on temporary errors;
• systematic output validation: if you ask for JSON, verify that it is JSON and contains the expected fields, with a second attempt or an alternative path if it is not;
• a dignified fallback for the user: a clear message, a reduced mode, or handover to a human operator in support flows;
• never block a critical operation waiting for the model: wherever possible, AI processing should be made asynchronous.

The rule we repeat in the projects we handle: the system must remain usable even when the model does not respond. AI adds value; it must not become the single point of failure.

Measure quality, not just uptime

With traditional software a test passes or fails; with an LLM the answer can be wrong in a plausible way. That is why extra tooling is needed: a set of test cases with expected answers to re-run at every prompt or model change, user feedback collected directly in the interface, and a periodic sample review of real conversations. It is ongoing work, and it must be planned into the project from the start, like tests and monitoring.

Integrating an AI model well, in short, is a software engineering problem before it is an artificial intelligence problem. It is the kind of work we do when we build custom software with AI features: architecture, costs and errors designed together with the functionality.

Want to put AI in your software without surprises?

If you are considering an AI feature in your business software or platform, we can take care of architecture, integration and cost control. We build custom software with AI components designed for production, not for the demo. Book a free call and let's talk about your use case.