OpenAI’s platform documentation describes the current APIs and capabilities. A model API is only one part of a production system.
What does OpenAI provide to developers?
OpenAI provides hosted inference, model APIs, tool use, structured outputs, and supporting platform services. The provider runs the model, while the application remains responsible for workflow logic and business rules. An AI agent still needs permissions, state, tools, and checks around each model response.
The platform changes frequently, so production code should use stable model aliases or explicit versions according to the application’s change policy. Model upgrades still need evaluation against real tasks.
When should a company consider OpenAI?
OpenAI is one candidate when a team needs broad model capabilities, mature APIs, or tool calling. The choice should follow workload tests, data requirements, latency, availability, and total cost rather than brand recognition. A company can reduce switching cost with an LLM gateway and provider-neutral application contracts.
What should a production evaluation measure?
A useful evaluation covers more than answer quality. Teams should measure response time, refusal behavior, structured output reliability, tool-call accuracy, and cost on the same representative requests. They should also test rate-limit handling, retries, and the behavior of any OpenAI-compatible API integration. The winning model is the one that meets the operational requirement consistently, not the one that performs best on a single demo. Record model versions and prompts in an AI evaluation harness so later changes can be compared against a stable baseline.