Linux on desktop is great for you, but this is a tool for people delivering solutions.
2) you know nothing about me
3) of course I am not! I do know better
That's their dream. Your dream. The AI dream. Many would say it's AI psychosis.
Or even something more managed like Vapi?
Second difference is where it runs. Our gateway is open source and runs in your own container, including with self-hosted livekit/pipecat. You get a temporary token before the session starts, and then your orchestration connects directly to the provider.
Vapi is a managed platform: you use their infra to use the voice AI stack. In our case you can have your own infra and switch between models, so you are not locked into a vendor. A lot of teams we talk to build their own infra as they mature, and that is where the router comes handy.
To use a claudism, I would like to push back on this. The industry is very much moving towards one-model-does-all end to end trained similar to LLMs and VLMs. Mostly for latency reasons and partially because the results for the end to end trained models are just so much better than those using three pieces architectures.
I think most of the value prop is in automatic evals, not routing specifically. A better pitch for you would be "the LM Arena of voice models" rather than comparing yourself to openrouter because the value add is rather questionable. For TTS specifically, the current SOTA for production systems are all using prompt based voice gen i.e. instead of having 10 different Tacotron models trained on 10 different models, these days it's all a single large model and the "style" is a prompt in the system prompt. The input is usually something like
<System prompt>
Speak in a deep smooth voice similar to a documentary narrator
</System prompt>
<Text to Narrate>
Speko is the ultimate evaluation platform for voice agents. We do automatic evals.
</Text to Narrate>
So something like OpenRouter for voices offer questionable value given that stakeholders usually make this sort of decisions once at the start of the project. On the other hand if you can offer automatic evals and figure out which prompts give the most similar results across different voice providers, that would offer a lot more value. It would be nice to be able to switch from e.g. Grok voice agents to ChatGPT voice agents knowing that the output style won't change too much. There are many companies now with evals as a core business model: LM Arena, Artificial Analysis, Prompt foo (before they got acquired and pivoted to security only) so many take a look at them.
Source: we have been building TTS systems for over a decade too https://narrationbox.com
On "promptfoo of voice models": that is closer to how it started. At my last company we ran these evals manually, we would even hire native-speaking raters, benchmark, switch if it wins. The evals are the value, agreed. The routing is what makes them actionable: teams told us swapping always looked like an R&D project, so scores alone did not change what ran in production.
On prompt-based voice gen and reference-audio cloning: agreed, that is what we see too. It makes continuous measurement more important: the same style prompt behaves differently per language and per content type, so we rank the voices themselves, tagged by use case: https://benchmarks.speko.ai/tts-voices