Cost optimization for AI apps means cutting what you pay for model calls, using fewer tokens, smaller models, caching and cheaper batch processing, without losing too much quality.
In plain words
Model providers price usage by tokens, so costs grow with every request. Common levers: send fewer requests and fewer tokens; use a smaller model where it is accurate enough; cache prompt parts that repeat, since cached input is billed at a lower rate; and send work that can wait through a batch API, which costs less in exchange for slower results. Cutting tokens and requests often makes apps faster too.
A quick example
Imagine a team that tags 100,000 support tickets every night. Nobody needs the tags instantly, so they send the job through a batch API at a discount. They also reuse one long, fixed set of instructions for every ticket, so the cached part is billed at a lower rate.
Where you'll meet it
- High-volume LLM features in production.
- Batch jobs such as large-scale evaluations and content moderation.
Related
Sources
- DocsCost optimization, OpenAI API documentation · read 21 Oct 2026
- DocsBatch processing, Claude API documentation · read 21 Oct 2026
- DocsPrompt caching, Claude API documentation · read 21 Oct 2026
Checked by WhatIsAI against the sources above · 21 Oct 2026. Spotted an error? Report it.