Real-Time AI Inference at Scale with WebSockets and Durable Objects

In October 2024, we talked about storing billions of logs from your AI application using AI Gateway, and how we used Cloudflare's Developer Platform to do this.

In October 2024, we covered how to store billions of logs from your AI application using AI Gateway, and how we used Cloudflare’s Developer Platform to do this.

With AI Gateway already processing over 3 billion logs and experiencing rapid growth, the number of connections to the platform continues to increase steadily. To help developers manage this scale more effectively, we wanted to offer an alternative to implementing HTTP/2 keep-alive to maintain persistent HTTP(S) connections, thereby avoiding the overhead of repeated handshakes and TLS negotiations with each new HTTP connection to AI Gateway. We understand that implementing HTTP/2 can present challenges, particularly when many libraries and tools may not support it by default and most modern programming languages have well-established WebSocket libraries available.

With this in mind, we used Cloudflare’s Developer Platform and Durable Objects (yes, again!) to build a WebSockets API that establishes a single, persistent connection, enabling continuous communication.

Through this API, all AI providers supported by AI Gateway can be accessed via WebSocket, allowing you to maintain a single TCP connection between your client or server application and the AI Gateway. The best part? Even if your chosen provider doesn’t support WebSockets, we handle it for you, managing the requests to your preferred AI provider.

By connecting via WebSocket to AI Gateway, we make the requests to the inference service for you using the provider’s supported protocols (HTTPS, WebSocket, etc.), and you can keep the connection open to execute as many inference requests as you would like.

To make your connection to AI Gateway more secure, we are also introducing authentication for AI Gateway. The new WebSockets API will require authentication. All you need to do is create a Cloudflare API token with the permission “AI Gateway: Run” and send that in the cf-aig-authorization header.

In the flow diagram above:

1. When Authenticated Gateway is enabled and a valid token is included, requests will pass successfully.

2. If Authenticated Gateway is enabled, but a request does not contain the required cf-aig-authorization header with a valid token, the request will fail. This ensures only verified requests pass through the gateway.

3. When Authenticated Gateway is disabled, the cf-aig-authorization header is bypassed entirely, and any token — whether valid or invalid — is ignored.

How we built it

We recently used Durable Objects (DOs) to scale our logging solution for AI Gateway, so using WebSockets within the same DOs was a natural fit.

When a new WebSocket connection is received by our Cloudflare Workers, we implement authentication in two ways to support the diverse capabilities of WebSocket clients. The primary method involves validating a Cloudflare API token through the cf-aig-authorization header, ensuring the token is valid for the connecting account and gateway.

However, due to limitations in browser WebSocket implementations, we also support authentication via the “sec-websocket-protocol” header. Browser WebSocket clients don't allow for custom headers in their standard API, complicating the addition of authentication tokens in requests. While we don’t recommend that you store API keys in a browser, we decided to add this method to add more flexibility to all WebSocket clients.

After this initial verification step, we upgrade the connection to the Durable Object, meaning that it will now handle all the messages for the connection. Before the new connection is fully accepted, we generate a random UUID, so this connection is identifiable among all the messages received by the Durable Object. During an open connection, any AI Gateway settings passed via headers — such as cf-aig-skip-cache (which bypasses caching when set to true) — are stored and applied to all requests in the session. However, these headers can still be overridden on a per-request basis, just like with the Universal Endpoint today.

How it works

Once the connection is established, the Durable Object begins listening for incoming messages. From this point on, users can send messages in the AI Gateway universal format via WebSocket, simplifying the transition of your application from an existing HTTP setup to WebSockets-based communication.

When a new message reaches the Durable Object, it’s processed using the same code that powers the HTTP Universal Endpoint, enabling seamless code reuse across Workers and Durable Objects — one of the key benefits of building on Cloudflare.

For non-streaming requests, the response is wrapped in a JSON envelope, allowing us to include additional information beyond the AI inference itself, such as the AI Gateway log ID for that request.

Here’s an example response for the request above:

For streaming requests, AI Gateway sends an initial message with request metadata telling the developer the stream is starting.

After this initial message, all streaming chunks are relayed in real-time to the WebSocket connection as they arrive from the inference provider. Note that only the eventId field is included in the metadata for these streaming chunks (more info on what this new field is below).

This approach serves two purposes:

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