Gladia needs real-time turn-taking

The real battleground is not who prints words fastest, it is who makes a voice app know when to stay quiet and when to speak. In practice, developers building phone agents need transcription, end of turn detection, interruption handling, and stable streaming output in one loop. Deepgram and AssemblyAI now package those turn taking controls directly into voice agent oriented streaming, while Gladia is strongest where broad language coverage and downstream conversation analysis matter most.1 2 3 4 5

• Deepgram split its lineup into Nova for general speech recognition and Flux for voice agents. Flux is built around model integrated end of turn detection and configurable turn taking, while Deepgram’s standard endpointing also lets developers set silence thresholds in milliseconds. That means the product is selling conversation control, not just transcript speed.2 3
• AssemblyAI made the same move with Universal-Streaming. It combines fast immutable transcripts with built in endpointing, and frames the product around avoiding awkward pauses and premature cutoffs. That puts Gladia next to rivals that increasingly market a full live conversation stack instead of a narrower speech to text API.4 5 1
• Gladia is positioned differently. It is described as real time multilingual transcription and conversation analytics, and its help docs emphasize code switching, translation, and recorded call workflows. That matters for customers analyzing global conversations after the fact, but those strengths do not automatically solve the timing problem inside a live agent exchange.1 6 7

Going forward, speech vendors will keep collapsing transcription, turn detection, and agent orchestration into a tighter real time loop. For Gladia, the clearest path is to turn its multilingual edge into a full live interaction layer, where understanding many languages and knowing exactly when to respond become one product, not two separate steps.1 2 4 6