What Is an AI Gateway?
An AI gateway is a proxy layer that sits between your application and one or more AI model providers, such as OpenAI, Anthropic, or Google. Instead of calling each provider's API directly from your code, your application sends requests to the gateway, which handles authentication, logging, caching, cost tracking, rate limiting, and automatic fallback to a different provider if one is down or over budget. This centralizes AI usage management across every application and team using AI in an organization, without changing the underlying model calls each application makes.
An AI gateway is a single layer that sits between your application and multiple AI providers, handling routing, caching, cost tracking, and fallback in one place.
An organization that starts with one team calling the OpenAI API directly usually ends up, a year later, with five teams each calling a different provider directly, no shared view of total AI spend, and no plan for what happens when a provider has an outage during a demo. An AI gateway is the fix for that specific kind of sprawl.
The core idea: one door, many providers
Instead of every application holding its own API key and calling OpenAI, Anthropic, or Google directly, every application sends its AI requests to one gateway endpoint. The gateway holds the actual provider credentials, forwards the request to the right provider, and returns the response. From the application’s point of view, nothing changes about how a request is structured; what changes is where it goes first.
This single point of passage is what makes every other feature of a gateway possible: you cannot track total cost, cache responses, or fail over between providers if requests bypass a shared layer entirely.
Cost visibility across every team and application
Without a gateway, understanding how much an organization spends on AI usually means asking every team to check their own provider dashboard and adding the numbers up manually, if anyone remembers to. With a gateway, every request passes through one place, so token usage and cost can be logged per application, per team, or per individual API key automatically. This turns “we think AI costs are getting expensive” into an actual number you can look at, broken down by where the spend is coming from.
Automatic fallback when a provider has problems
AI providers occasionally have outages, rate limit spikes, or degraded performance. A gateway can be configured to detect a failed or slow request to the primary provider and automatically retry the same request against a secondary provider, transparently to the application. A customer-facing chatbot built this way keeps working during an OpenAI outage by silently falling back to Anthropic’s API, rather than displaying an error to every user until someone notices and manually flips a configuration switch.
Caching identical requests
Some AI workloads generate the exact same request repeatedly, for example a support chatbot answering the same frequently asked question, or a batch job processing similar documents. A gateway can cache the response to an identical request for a configurable period and return the cached result instantly instead of paying for and waiting on a new model call every time. For high-volume, repetitive workloads this can meaningfully cut both cost and latency.
When a gateway is worth setting up
A single application built by one developer, calling one AI provider, rarely needs a gateway; the API call can go straight from the application to the provider. The calculation changes once an organization has multiple applications or teams using AI, wants a single source of truth for cost, or has experienced an outage that took down a customer-facing feature with no fallback. At that point, routing everything through Cloudflare AI Gateway, Portkey, or a similar tool centralizes control without requiring every application to change how it makes AI calls.
Our tip: If you are running more than one AI-powered feature in production, put an AI gateway in front of them even before you need advanced routing logic. The logging and cost visibility alone are usually worth the setup, and you can add caching and fallback rules later once you know which workloads actually need them.
Frequently asked questions
Why not just call the OpenAI or Anthropic API directly from my application?
You certainly can, and for a single small application that is often simpler to start with. An AI gateway becomes valuable once you have more than one application using AI, want a single place to see total cost across all of them, need automatic fallback if a provider has an outage, or want to switch or test model providers without changing application code. It adds an operational layer that pays off at a certain scale, not before.
Does an AI gateway slow down responses?
A well-built gateway adds only a small amount of latency, typically a few milliseconds, since it is usually deployed at the edge, close to where the request originates, and simply forwards the request to the underlying provider. In exchange, it can actually reduce latency for repeated queries through response caching, and reduce the impact of a slow provider by automatically retrying against a faster fallback.
What features do most AI gateways provide?
Common features include unified logging of every AI request and response across providers, cost and token usage tracking per application or team, response caching to avoid paying for identical repeated requests, automatic fallback to a secondary provider on error or timeout, and rate limiting to prevent one application from consuming an entire shared budget. Some gateways also support prompt versioning and A/B testing between models.
Which AI gateways are commonly used?
Cloudflare AI Gateway is a widely used option because it integrates directly with Cloudflare Workers and requires no separate infrastructure to run. Other options include Portkey, Helicone, and LiteLLM, each with different emphasis on caching, observability dashboards, or multi-provider routing logic. Larger organizations sometimes build a thin internal gateway themselves when their requirements are simple and provider-specific.