Keeping AI API Costs Under Control

AI cost control is mostly about tokens you never needed to send

AI cost control rarely requires a smaller ambition. It requires noticing how much of your spend buys nothing. Most runaway LLM bills are not driven by traffic you wanted; they are driven by context you padded, retries you did not cap, and a flagship model doing work a cheaper one would have done identically. Tighten those three and the same product costs a fraction as much, with no visible drop in quality.

The first move is visibility. You cannot manage a number you cannot see, and "the API bill" at month end is not a number you can act on. You need to know which feature, which user, and which call spent the money before you can stop the leak.

Instrument before you optimise

Attribute every API call to a feature, a user tier, and ideally a request ID. Once you can break the bill down, the waste announces itself. One rarely-used feature is often half your spend, or one tier of customers is subsidised into the ground without anyone noticing.

• Log input tokens, output tokens, model, and latency for every call.
• Tag calls by feature so you know what each part of the product actually costs to run.
• Set a budget alert per feature, not just a global one, so a single runaway loop pages you early instead of at invoice time.

This instrumentation pays for itself the first time it catches a deploy that accidentally doubled context size and quietly doubled the bill along with it.

Right-size the model to the task

The biggest single lever is matching model to job. Reaching for the most capable model on every call is like couriering every letter: fine for the important ones, absurd for the routine. Classification, extraction, short rewrites, and routing decisions usually run just as well on a small, cheap model. Reserve the expensive model for genuinely hard reasoning.

A practical pattern is a cascade: try the cheap model first, and only escalate to the expensive one when a confidence check or validation fails. Many requests never escalate, and you pay the premium only when it is earned. Test this carefully against your evaluation set, because the goal is equal quality at lower cost, not lower cost at quietly degraded quality. The cascade only works if you can prove the cheap tier is actually good enough for the requests it handles.

Stop paying for the same answer twice

Caching is the cheapest token there is: the one you never send. Two layers help, and they stack.

1. Exact-match caching for identical requests. FAQ-style queries and repeated lookups return instantly at zero model cost.
2. Prompt caching, where the provider supports it, for the large, stable prefix of your prompt. System instructions and retrieved context that repeat across calls are billed at a steep discount.

For semantically similar but not identical requests, a semantic cache keyed on embeddings can catch near-duplicates. It needs a similarity threshold tuned carefully, though, to avoid returning a cached answer to a subtly different question and quietly serving the wrong thing.

Trim the context you actually send

Every token in the prompt is a token you pay for, on every single call. Bloated context is silent, recurring waste that never shows up as a single dramatic spike. Retrieve fewer, better chunks rather than stuffing the window just in case. Summarise long histories instead of resending the full transcript each turn. Drop boilerplate from system prompts that the model does not need.

A prompt trimmed from six thousand tokens to two thousand cuts that call's input cost by two-thirds, and it often improves the answer, because the model is no longer hunting for signal in a haystack of noise. Leaner context is both cheaper and better, which makes it one of the few optimisations with no real downside.

Cap the failure modes

Cost spikes are usually bugs, not growth. An unbounded retry loop, a recursive agent with no step limit, or a user pasting a novel into a prompt can multiply spend overnight. Put hard ceilings everywhere: a maximum retry count with backoff, a cap on agent steps, a token limit on user input, and a per-user rate limit. These guardrails protect the bill and the user experience at the same time, and they turn a potential midnight emergency into a logged, contained event.

How BSH can help

At BSH Technologies, we help teams cut LLM spend without cutting capability: per-feature cost instrumentation, model cascades, caching layers, and context trimming, all validated against quality so the savings stick. We have brought AI features to production on budgets that have to make sense, for clients in Kerala and across India. If your AI costs are climbing faster than your usage, we can find where the money is leaking and seal it.