Deployment

OpenAI-compatible API

An OpenAI-compatible API follows the same request and response format.

Quick definition

An OpenAI-compatible API follows the same request and response format.

  • Category: Deployment
  • Focus: hosting and runtime tradeoffs
  • Used in: Running local models for privacy-sensitive workflows.

What it means

It allows tools and apps to swap providers with minimal changes. In deployment workflows, openai-compatible api often shapes hosting and runtime tradeoffs.

How it works

Deployment choices include cloud APIs, local inference, or hybrid setups. Each option trades off privacy, cost, and performance.

Why it matters

Deployment choices affect privacy, performance, and cost.

Common use cases

  • Running local models for privacy-sensitive workflows.
  • Using managed APIs for fast iteration and scaling.
  • Hybrid setups that keep data local but call cloud models.

Example

Point a client to a compatible endpoint.

Pitfalls and tips

Local deployments require hardware planning and updates. Cloud deployments require governance and cost control.

In BoltAI

In BoltAI, this appears in provider, hosting, or local model settings.