How to Build a Sub-15ms LLM Failover Gateway for OpenAI and Anthropic

The Problem: Single Points of Failure in AI

If your AI agent relies exclusively on OpenAI, a sudden API timeout or 503 error doesn't just return a bad response—it breaks the entire agentic loop. For user-facing voice agents or autonomous sales bots, this results in dropped calls and lost revenue.

The Solution: Edge Proxy Failover

To guarantee reliability, we need to intercept traffic at the edge. A failover gateway acts as a reverse proxy. When your application sends a request to OpenAI, it actually goes to the gateway.

If the gateway detects latency exceeding 15ms or receives an HTTP 429 (Rate Limit), it immediately triggers multi-model fallback. The gateway translates the request payload and utilizes semantic LLM routing to send the traffic to a secondary provider (like Anthropic Claude or Google Gemini).

Architecting the Gateway

Building this requires three core components:

1. Circuit Breakers: Monitor upstream health and trip when errors spike.
2. Semantic Payload Mapping: Instantly convert OpenAI-formatted messages to Anthropic-formatted messages.
3. Connection Persistence: Hold the client connection open while the proxy negotiates the failover.

// Example: Handling a Failover Event
try {
  return await sendToPrimary(payload);
} catch (error) {
  if (isRateLimited(error) || isTimeout(error)) {
    const fallbackPayload = mapToFallbackProvider(payload);
    return await sendToFallback(fallbackPayload);
  }
  throw error;
}

Why 15ms Matters

In conversational AI, human latency tolerance is approximately 200-300ms. If your fallback takes 1 second to negotiate, the user will experience an awkward pause. By handling the routing entirely in-memory at the edge, you can guarantee sub-15ms failover switching, keeping the conversation fluid and natural.

See It in Action

Selixes implements everything described in this article — circuit breaking, session budgets, local edge fallback, and private VPC deployment.

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