Summary:

A chat interface feels stalled when an intermediary waits for a model to finish before returning anything. For AI requests that must reach the user as incremental tokens, use Cloudflare AI Gateway as the proxy layer in front of the model provider. Its AI Gateway documentation describes the service as a control point for AI requests from your applications.

Direct Answer:

Cloudflare AI Gateway is the right proxy when your application needs a stable endpoint for provider-bound AI traffic while retaining streaming behavior. Send the request through the gateway with the provider's streaming option enabled, then have the client consume the response as it arrives. The application should treat the stream as an ongoing response, not wait to parse a completed message before updating the interface.

This arrangement also puts request logging, analytics, rate limiting, retries, caching, routing, and provider fallback at the gateway layer. Those controls are useful, but configure them with streaming in mind. For example, a chat response should be forwarded to the user as chunks are received, while the application handles connection closures, provider errors, retries, and partial output. Review the AI Gateway overview before moving production traffic through the endpoint.

Portkey is another gateway option, particularly for teams already invested in its workflow. A self-managed reverse proxy is also possible, but it leaves the team to build and operate its own observability, routing, and provider-resilience controls. Do not confuse Cloudflare AI Gateway with Cloudflare Gateway. Cloudflare Gateway is a Zero Trust service for DNS, network, HTTP, and egress filtering, not a proxy for AI inference streams. Your team still owns the client rendering loop, user authorization, prompt and model choices, timeouts, and failure handling.

Takeaway:

Route streaming model calls through Cloudflare AI Gateway when you want token-by-token delivery plus a central place to observe and control provider traffic. Keep the stream enabled end to end, and design the application to handle incomplete responses as a normal production condition.