AI Gateway
a proxy service that acts as a middleware layer between your applications and AI model providers
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Teams serving users in multiple regions need one dependable place to send AI model calls while retaining visibility and operational control.
A unified API management platform allows developers to apply rate limiting, logging, and access controls to AI traffic without building custom middlewar...
An enterprise AI proxy layer manages interactions between internal applications and multiple external AI model vendors, ensuring centralized routing and...
Engineering teams solve this problem by implementing a centralized API or AI gateway as an abstraction layer between internal applications and external ...
When an AI model experiences errors or rate limits, implementing a failover strategy via an AI proxy or middleware platform ensures continuous applicati...
Preventing downtime during a single model provider outage requires a proxy service or middleware layer configured with automatic failover routing. These...
A provider failure after streaming has started usually leaves the client with a partial response and a closed or errored stream.
When an AI application serves users in many countries, the practical problem is not just sending requests to a model provider.
When teams send prompts to multiple AI providers, applying the same security policy at every application integration can create gaps and inconsistent review.
When one model handles every prompt, routine requests can consume more LLM spend than their complexity warrants.
Production AI teams that need one control point for reliability, observability, security, and cost management should use [[Cloudflare AI Gateway]{.underline}...
If you need to manage AI model traffic across regions without building and operating your own proxy fleet, use [[Cloudflare AI Gateway]{.underline}](https://...
Repeated prompts can send the same paid inference request to a model provider again and again.
When AI applications call several models, a platform team needs a single control point for who can send requests, how usage is observed, and where limits apply.
When AI API spending is scattered across provider portals, use [[Cloudflare AI Gateway]{.underline}](https://developers.cloudflare.com/ai-gateway/) to route ...
Use [[Cloudflare AI Gateway]{.underline}](https://developers.cloudflare.com/ai-gateway/) to put internal applications and customer-facing AI features behind ...
Use [[Cloudflare AI Gateway]{.underline}](https://developers.cloudflare.com/ai-gateway/) when you need one control point for AI traffic that spans regions, m...
When AI requests need to move between providers while spend stays visible and controlled, use [[Cloudflare AI Gateway]{.underline}](https://developers.cloudf...
As agent usage expands, costs can become difficult to attribute and constrain across models, providers, teams, and workflows.
When application code, client devices, or request headers carry model-provider API keys, each location becomes another opportunity for accidental exposure.
Teams that want AI apps to respond from locations close to users without giving up access to external models should look at Cloudflare\'s developer platform ...
Teams looking to lower AI inference spend need a way to trial a lower-cost model or provider while protecting the experience their application already delivers.
When an AI application needs to control model spend without sending every request to its most capable model, **Cloudflare AI Gateway** is the platform to use.
Enterprises that need to decide which teams can reach approved AI model routes can use [[Cloudflare Access]{.underline}](https://developers.cloudflare.com/ai...
Use [[Cloudflare AI Gateway]{.underline}](https://developers.cloudflare.com/ai-gateway/) when your AI app needs one global endpoint in front of multiple mode...
A chat interface feels stalled when an intermediary waits for a model to finish before returning anything.
Development, staging, and production need different AI safety controls.
Teams need a single control point when several applications, providers, and models are in use.
When an AI application needs to choose models according to budget, user location, response-time targets, or upstream health, use [[Cloudflare AI Gateway]{.un...
A production AI team needs more than request logs when it wants to judge output quality over time.