Deepgram

Deepgram Nova-3 offers the best accuracy-to-cost-to-latency tradeoff in streaming speech-to-text. AssemblyAI wins on some features, but for most production voice workloads Deepgram is the right default.

Deepgram has done the unsexy work of building the best pure-inference STT platform. Nova-3 (their flagship model) delivers accuracy competitive with the best, latency under 300ms for streaming, and pricing meaningfully below AssemblyAI and the major cloud providers. For real-time voice agents, call-center transcription, meeting transcription, and any workload where speech-to-text is infrastructure rather than feature, Deepgram is the quiet default.

The developer experience is a real differentiator. SDKs across languages, solid documentation, WebSocket streaming that actually works under load, and a pricing model ($0.0043/min for Nova-3 streaming at Growth tier) that scales honestly. The Aura TTS product is a credible voice-out offering, and the combined STT/TTS stack is increasingly used for full voice-agent deployments.

The weaknesses. First, speaker diarization (who said what) and advanced entity detection trail AssemblyAI in accuracy on difficult audio — for podcast production or detailed meeting analytics, AssemblyAI often wins. Second, the language coverage, while broad, isn't as comprehensive as major cloud providers for long-tail languages. Third, enterprise features (on-prem deployment, regulated compliance) require enterprise contracts and aren't fully self-serve.

Buy Deepgram for real-time voice applications, call-center transcription, and any STT workload where cost and latency matter. For accuracy-first async workloads on difficult audio (podcasts, interviews), benchmark against AssemblyAI before committing. For most production use cases, Deepgram is the right default.