An AI model proxy sits between a client and model endpoints. It can translate request formats, apply authentication, choose a route, record usage and normalize responses so the client does not need a separate integration for every provider.
A proxy can make providers interchangeable at the connection layer, but their capabilities are not identical. Context limits, tool-calling behavior, reasoning controls and output formats still need testing because translation cannot create features the selected model does not support.
ELI5
A model proxy is a service between an application and one or more AI model providers. The application sends one familiar request format to the proxy, which can authenticate, route and translate the request before returning a normalized response.
For example, a product can call one proxy while the proxy chooses between two compatible text models. The connection may look the same, but teams still need to test differences in tool use, context limits and output behavior.
Why put a proxy in front of AI models?
A proxy can centralize routing, provider settings, compatibility translation and usage records while preserving one stable endpoint for applications.
Does a model proxy make every model equivalent?
No. It can normalize protocols, but quality, context length, tool support, latency and pricing still differ among models and providers.
