openai · OpenAI Platform Docs

Cost optimization

Strategies and implementation paths for reducing API expenditures through token minimization, model selection, asynchronous Batch API usage, and flex processing for non-priority workloads.

Import to Prompt Buddy

Derived skill

Files assembled from official documentation

Viewing SKILL.md

Cost optimization

Strategies and implementation paths for reducing API expenditures through token minimization, model selection, asynchronous Batch API usage, and flex processing for non-priority workloads.

When To Use

Use when you need to implement asynchronous processing via the Batch API or select architectural strategies to lower token consumption and model costs.

Reference Files

File	Contains	Use For
`SKILL.md`	Entry point: scope, routing table, and workflow.	Start here.
`docs/cost-optimization-workflow-guide.md`	A guide detailing strategies to reduce costs and latency when using OpenAI models, including the Batch API and flex processing.	Questions about a guide detailing strategies to reduce costs and latency when using OpenAI models, including the Batch API and flex p...

What This Skill Covers

There are several ways to reduce costs when using OpenAI models. Cost and latency are typically interconnected; reducing tokens and requests generally leads...
Main sections: Cost and latency, Batch API, Flex processing.

Workflow

Open the most relevant file under docs/ for the exact documented workflow and wording.
Open schemas/ files for exact structured contracts.
Open examples/ files for concrete requests, commands, snippets, and manifests.
Do not add behavior or configuration that is not present in the attached source files.

Canonical source: https://developers.openai.com/api/docs/guides/cost-optimization.md