What is an SSE proxy for LLM streaming?

It is a layer between your callers and your model that forwards a streaming (SSE or chunked) response through to the caller as the model produces it, while handling authentication and rate-limiting at the edge.

Does the proxy buffer the whole response?

No. Chunks are forwarded as your target emits them, so the caller sees tokens as they’re generated rather than waiting for the full response.

Does it work for non-LLM streams?

Yes. Any SSE or chunked HTTP response works — LLM token streaming is the common case, but long-running inference and progress streams behave the same way.

SSE / streaming proxy for LLMs

Stream your model’s tokens through one endpoint.

Point your streaming model or inference target at EchoRelay. We forward its SSE or chunked output to your caller as it’s produced, incrementally and not buffered, while auth and rate-limits stay at the edge.

Worked example

Show a document summary as your model produces it

Your app requests a report summary through EchoRelay. Your model returns Server-Sent Events (SSE), and your app displays each text fragment as it arrives. This example uses a fictional model API.

Your caller sends

{
  "prompt": "Summarise the attached report.",
  "documentId": "REPORT-7",
  "stream": true
}

Your model emits; your caller receives

event: token
data: {"text":"The report "}

event: token
data: {"text":"describes rising demand."}

Configure one streaming target with your model's URL and credentials.
Publish the endpoint and send an authenticated request from your app.
Read the response as a stream and append the text fragments in your UI.

The target defines the SSE event names and data format. EchoRelay forwards its stream without waiting for the complete summary. Caller authentication and rate limits run before the stream opens. Streaming delivery has no automatic retries.

Why EchoRelay

Less to build, less to run.

Chunks, as they’re produced

SSE or chunked transfer, passed straight through to your caller token by token. No waiting for the whole response.

Auth & limits at the edge

We authenticate the caller and apply rate limits at the edge, before the stream to your model opens.

Set up by your agent, over MCP

Describe the streaming endpoint to your AI agent. It connects to EchoRelay’s MCP server and ships the config.

Questions

Answered, honestly.

What is an SSE proxy for LLM streaming?: It is a layer between your callers and your model that forwards a streaming (SSE or chunked) response through to the caller as the model produces it, while handling authentication and rate-limiting at the edge.
Does the proxy buffer the whole response?: No. Chunks are forwarded as your target emits them, so the caller sees tokens as they’re generated rather than waiting for the full response.
Does it work for non-LLM streams?: Yes. Any SSE or chunked HTTP response works — LLM token streaming is the common case, but long-running inference and progress streams behave the same way.

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