Azure AI Speech text-to-speech vs Deepgram Text-to-Speech (Aura-2, Flux TTS)

Azure AI Speech text-to-speech

Real-time synthesis keeps neither the input text nor the output audio. An Azure subscription needs a card, even for the free F0 tier.

Deepgram Text-to-Speech (Aura-2, Flux TTS)

OpenAPI 3.1 and AsyncAPI files, llms.txt and Markdown pages. Requests can be kept for training unless each one sets mip_opt_out=true.

Azure AI Speech text-to-speech

1. Send SSML with <speak> and <voice>, and set X-Microsoft-OutputFormat and User-Agent.
2. On 429 retry with backoff, and try the voice's home region or another region rather than asking for more quota.
3. Keep each real-time request under 10 minutes of audio, or use batch synthesis.
4. Use Entra ID tokens instead of resource keys where the agent runs inside Azure.
5. Cache the voice list per region, since it returns hundreds of entries at once.

Deepgram Text-to-Speech (Aura-2, Flux TTS)

1. Set mip_opt_out=true on every request if the text mustn't be kept for training.
2. Split Aura-2 REST text under 2,000 characters or expect a 413.
3. Pass model on /v2/speak, where it's required.
4. Strip SSML before sending, since it's removed with an INPUT_MARKUP_STRIPPED warning.
5. Back off exponentially on 429 and keep traffic in one project.