How to reduce LLM API costs

Many teams reach for a cheaper model first, but the fastest savings usually come from sending less text. Reused system prompts, oversized retrieval payloads, duplicated instructions, and unnecessary conversation history quietly inflate every request. If those tokens show up on every call, they become a structural cost problem.

Start by trimming what the user never sees. Shorten system prompts, remove redundant formatting rules, cap retrieval payload size, and stop shipping the same background context over and over. If your provider supports cached input pricing, repeated prompt material becomes even more important to isolate and model correctly.

Not every step in a workflow deserves your most expensive model. Routing everything through a premium model is convenient, but it is rarely necessary. Classification, extraction, lightweight rewriting, and moderation often do fine on smaller or cheaper models, while only the harder reasoning step needs higher quality.

The point is not to downgrade blindly. It is to separate the job into parts and pay for quality where it changes the outcome. When teams do this well, they reduce spend without making the product feel noticeably worse to the end user.

A surprising amount of spend comes from failure paths rather than normal traffic. Automatic retries, agent loops, fallback chains, and validation reruns can multiply token usage without anyone noticing. The user sees one response, but your system may have paid for three attempts.

Look at where requests are repeated and why. If the same prompt often fails, the fix may be prompt design, output constraints, or input validation rather than more retries. Cost reduction gets easier once the system stops paying for avoidable mistakes.

If a task does not need an immediate response, treat it like a background job. Summaries, backfills, large-scale labeling, and nightly processing are often better candidates for batch APIs or delayed execution than for live requests. That single workflow decision can lower cost more than prompt tuning.

This is one of the few optimizations that can cut spend without touching user-facing quality at all. If the customer is not waiting on the answer, you should at least test whether asynchronous processing changes the economics in your favor.