How to Reduce OpenAI API Cost

Default new features to gpt-4o-mini (or the current cheapest OpenAI chat model that passes your eval). Keep GPT-4o / o-series only behind an explicit quality gate. Document which product surfaces are allowed to call frontier models so cost does not creep through “temporary” experiment keys.

Turn on OpenAI prompt caching for stable system prompts and large contexts that repeat across requests. Shorten chat history windows, summarize older turns, and avoid re-sending full RAG corpora on every call. Measure output tokens — Chat Completions with verbose assistants are often the real bill driver.

In the OpenAI platform settings, set monthly budgets, project limits, and email alerts. Use separate projects for prod vs staging so a leaky test script cannot exhaust the production budget. Rotate keys that appear in CI logs.

If you are venture-backed or in an eligible accelerator, apply for OpenAI for Startups (or current partner programs) before scaling paid traffic. New accounts may receive limited evaluation credit — use it on eval harnesses, not production user traffic.

Only after you have right-sized OpenAI models and caps should you compare leaving OpenAI. Use /cheaper-openai-api-alternatives for cross-lab pricing, /openai-api-cost-calculator for OpenAI-only estimates, and /llm-cost-optimization for provider-agnostic methods.