Speko is basically OpenRouter but for the voice AI stack

Building a production-ready voice agent usually requires stitching together three different models: Speech-to-Text (STT), an LLM, and Text-to-Speech (TTS). The problem is that the landscape moves so fast that by the time you've integrated a specific vendor, a cheaper or faster model has already dropped. Most devs just stick with their initial choice because swapping vendors feels like a full-blown R&D project involving manual benchmarking and code rewrites.

Speko solves this by acting as an intelligent router. Instead of hardcoding your providers, you send a request specifying your constraints—whether you're optimizing for latency, cost, accuracy, or a balance of all three—along with the language and region. The platform then references its own benchmarked data to route the request to the winning combination of models.

How the routing and failover work

The technical implementation is designed to avoid the "control-plane lag" that kills the feeling of a real-time conversation.

1. Optimization Request: You define your criteria (e.g., "lowest latency" or "highest accuracy") and the target region.
2. Model Selection: The router filters through its public benchmarks to find the current winner for that specific combination.
3. Session Prefetching: To prevent the caller from waiting, the gateway prefetches signed session plans. This means the session connects to the provider directly from memory, skipping an extra round trip.
4. Connection Failover: Failover is handled strictly during the connection setup. If the primary provider rejects the attempt, the system immediately pivots to the runner-up model.

For those who are worried about adding another network hop to their audio path or sharing API keys, they've open-sourced the gateway as a Go binary. You can run it as a sidecar in your container, communicating via a Unix socket to pin provider hosts and attach keys locally.

# Example of the gateway logic (Conceptual)
# The Go binary runs as a sidecar, handling the local protocol 
# and attaching keys to the provider requests.

The benchmarking approach

What makes this actually useful is the transparency of the data. They aren't just using "vibes" or short 30-second clips. Their benchmarks include ten-minute takes and spontaneous speech to see which models actually hold up over a long conversation. They've even trained an automatic scorer for TTS naturalness based on blind head-to-head human listening votes, which they claim matches human rater agreement levels.

Since Speko doesn't train or sell its own models, the rankings remain impartial. This is a massive win for anyone building a real-world AI workflow where switching from a mediocre STT to a high-accuracy one can be the difference between a usable product and a frustrating one.

If you're currently managing a complex voice stack, using a router like this turns a manual engineering task into a simple API call.

https://speko.ai/