OpenAI Launches Decisions API for Low-Latency, Cost-Efficient Inference

OpenAI has introduced the Decisions API, a lightweight endpoint designed to return structured, high-confidence outputs for discrete tasks such as intent classification, content moderation, and action routing. Unlike the broader Chat Completions API, Decisions is optimized for sub-100ms latency and predictable pricing, making it suitable for production workloads where every millisecond and token counts.

Why it matters for GPU / AI infrastructure

For teams running inference on GPU clusters — whether on-premises or through cloud providers like AiGpu — the Decisions API represents a shift toward specialized, smaller models that can be batched efficiently on shared hardware. Lower token volumes per request translate to higher throughput per GPU, reducing cost per decision and easing capacity planning for high-QPS services.

The API returns JSON with a decision label, confidence score, and optional reasoning trace, enabling deterministic downstream logic without prompt engineering overhead. Early benchmarks show 5–10× lower latency and up to 90% cost reduction versus equivalent chat-completion calls for classification tasks.

Developers can define decision schemas via a simple type system, and OpenAI handles model selection, distillation, and serving infrastructure. This abstraction lets product teams focus on application logic while benefiting from continuous model improvements behind the scenes.