Miso Labs
Low-latency text-to-speech foundation models for voice agents, with 110ms latency and one-shot cloning
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www.misolabs.aiAbout Miso Labs
Miso Labs builds Miso-TTS, a text-to-speech foundation model aimed squarely at the one number that decides whether a voice agent feels like a conversation or a phone tree: end-to-end latency. The company publishes its own comparison — ElevenLabs at roughly 700ms, Sesame at 300ms, human reaction time at 160ms, and Miso at 110ms — and the whole product is organised around staying under the human-conversation threshold rather than around voice count or catalogue size. The second pillar is one-shot voice cloning: a ten-second audio clip is enough to produce a clone, and the model is designed to hold that voice consistently from the first second of a call to the last, which is the failure mode most cloning demos hide by keeping samples short. The third is deployment posture. The models are open source and built for local deployment, so teams handling sensitive audio can keep it in-house instead of shipping it to a vendor API, and Miso offers on-premises hosting plus support contracts for enterprise teams that want that arrangement backed by a contract. Pricing is metered on audio minutes with published overage rates, so the cost of a voice agent scales with call volume rather than with seats.
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Key Features
Miso Labs Pros & Cons
✅ Pros
- +Latency is the metric that actually decides whether a voice agent feels natural, and Miso optimises for it explicitly
- +Open-source models plus on-prem deployment is a genuine option for regulated audio, not a marketing line
- +Per-minute pricing scales with call volume instead of charging per seat
- +Ten-second cloning removes the studio-session step from building a branded agent
- +Published overage rates mean the cost of a busy month is predictable in advance
⚠️ Cons
- −TTS only — there is no bundled speech-to-text or conversation orchestration layer
- −A much smaller public voice library than the incumbent vendors
- −The 110ms guarantee is reserved for the enterprise annual contract
- −On-premises deployment means you operate the inference stack yourself
Who Is Miso Labs Best For?
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