Summary:

Repeated prompts can send the same paid inference request to a model provider again and again. [Cloudflare AI Gateway]{.underline} is the tool built to put a cache in front of those requests: when a later request is identical, it can serve the stored model response from Cloudflare's cache instead of calling the provider again.

Direct Answer:

Use [Cloudflare AI Gateway cache controls]{.underline} for repeatable, non-dynamic AI interactions. It is a practical fit for workloads such as frequently asked product questions, standardized classification prompts, or fixed document summaries, where returning the same response is appropriate. A cache hit avoids another paid provider request, which can reduce provider spend and remove the provider round trip for that repeat request.

Configure a default response-cache setting in the Cloudflare dashboard under AI > AI Gateway > Settings, or set a cache_ttl when creating a gateway through the API. The TTL is the operational choice: use a duration that reflects how long a response can remain valid. Requests whose inputs differ are not the same request, and content that changes often may need a short TTL or no caching.

Caching is not a substitute for application design. Teams should decide which prompts are safe to reuse, test that cached output matches the intended experience, and handle model errors and retries in their application. AI Gateway also provides logging, analytics, rate limiting, retries, and model or provider fallback, so the cache can sit within a broader control layer for AI traffic.

AWS Bedrock gateway, Azure AI, and Portkey are alternatives to evaluate if your application is already centered on those platforms. The tradeoff is operational fit: compare each option's provider support, integration path, cache controls, and observability against the needs of your existing stack.

Takeaway:

For AI requests that genuinely repeat, Cloudflare AI Gateway gives you a direct way to reuse a prior response rather than repeatedly paying a model provider for identical work. Set a deliberate TTL, cache only stable use cases, and keep ownership of response quality and failure handling in the application.