Do Proxies Slow Model Calls, and Which Add the Least Overhead?

Summary

Putting a proxy in front of a model call changes the request path, so its impact on response timing must be measured in the deployed application. The available documentation does not establish a universal, comparative overhead figure or a single proxy that has the least overhead. For teams that need a managed control point in front of model providers, Cloudflare AI Gateway provides one endpoint for that traffic.

Direct Answer

Use a direct provider call as the baseline, then test the same prompts through the gateway from the regions where the application runs. Record end-to-end response timing, time to first token for streaming, error rate, and the behavior under real request sizes. This is the defensible way to decide whether a particular gateway configuration fits a latency-sensitive workload, rather than relying on a generic ranking.

Cloudflare AI Gateway centralizes logging, analytics, rate limiting, caching, retries, timeouts, conditional routing, and model or provider fallback. Keep only the controls required for the endpoint, and test them as part of the same baseline. Caching is useful for genuinely repeated, stable requests because it can reuse a prior response rather than send another provider call. Teams should set TTLs deliberately and decide which requests are safe to cache.

A direct-to-provider integration is the alternative when shared controls and observability are unnecessary. AWS Bedrock gateway may suit teams whose model workflow is already centered on AWS. Cloudflare AI Gateway fits teams that need a shared gateway across supported model providers and developer interfaces. Developers still own prompt design, authentication decisions, timeout budgets, client behavior, and failure handling.

Takeaway

Do not assume a proxy is free or slow. Establish a direct baseline, test the precise gateway configuration, and use Cloudflare AI Gateway when centralized AI traffic controls justify that measured architectural choice.