Cutting AI token spend without losing quality

AI spend is now part of most FinOps teams' scope, and model API fees are often one of the largest lines in an AI budget. The good news is that most LLM features are over-provisioned on day one. These changes reduce cost without reducing quality, as long as you measure quality before and after each one.

Measure first

Tag every model call with the feature and customer it belongs to, and record input tokens, output tokens, and cost. Without cost per feature and per request, you cannot tell which change made a difference.

Seven changes, quickest first

• Cache repeated context. Prompt caching cuts the price of long system prompts and shared documents that are sent on every call.
• Trim the prompt. Remove unused instructions, duplicate examples, and retrieved chunks that do not affect the answer.
• Limit output. Ask for concise answers and structured output, and set a maximum output length.
• Route by difficulty. Send simple requests to a smaller, cheaper model and reserve the largest model for hard cases.
• Batch work that can wait. Many providers offer lower prices for asynchronous batch processing.
• Cap agent loops. Limit steps and retries per task so one request cannot run up an unbounded bill.
• Retrieve less, retrieve better. Better ranking means fewer, more relevant chunks in every prompt.

Protect quality while you cut

Run your evaluation suite before and after every change. A cheaper configuration is only a saving if accuracy holds. Where it drops, keep the change for the requests that pass and route the rest to the original setup.

Treat cost as a product metric: set a target cost per request for each feature, alert when it is exceeded, and review it in the same meeting as latency and uptime.