Putting LLMs into production without burning your budget

Building an impressive AI demo takes an afternoon. Running the same feature reliably, at scale, without a surprising invoice at the end of the month, is a different job entirely.

Here's what we've learned separates AI features that survive production from those that quietly get switched off.

1. Define "good" before you build

Most LLM projects fail because nobody wrote down what a correct answer looks like. Before writing prompts, collect 50 to 200 real examples of inputs with the outputs you'd accept. This becomes your evaluation set.

Every change, whether a new prompt, a new model or a new retrieval setup, gets run against it. Without this, you're shipping on vibes.

2. Use the smallest model that passes your evals

The largest model isn't automatically the right one. Many tasks such as classification, extraction and routing work just as well on smaller, faster and much cheaper models. Run your evaluation set across a few options and pick the cheapest one that meets your quality bar. Re-check when new models are released; the trade-offs shift quickly.

3. Route requests by difficulty

A common pattern: a small model handles the routine majority of requests, and only the hard or ambiguous cases escalate to a larger model. Done well, this cuts costs substantially with little or no loss in quality.

4. Cache aggressively

If many users ask similar questions, or your prompts share a long fixed preamble, caching repeated work saves both money and latency. Most major model providers now offer some form of prompt caching. Use it.

5. Ground answers in your own data

For anything factual about your business, retrieval beats hoping the model "knows". Pull the relevant documents, records or policies into the prompt at request time, and require the model to answer from them. It reduces hallucinations and keeps answers current without retraining anything.

6. Put hard limits in place

Set per-request token limits, per-user rate limits and a monthly budget alert. Log every request with its cost. The first time someone accidentally builds a loop that calls the model ten thousand times, you'll be glad you did.

7. Keep a human in the loop where it matters

For actions with real consequences, such as sending messages to customers, changing records or approving payments, let the model draft and a person approve, at least until your evaluation results justify more autonomy.

The takeaway

Production AI is mostly ordinary engineering: clear requirements, tests, cost controls, monitoring. The model is one component. Treat it like the rest of your stack and it will behave like the rest of your stack.