Deepgram Text-to-Speech (Aura-2, Flux TTS) vs Rime TTS API + MCP

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.

Rime TTS API + MCP

Only character counts are kept by default, and customer data isn't used for training without an opt-in. No OpenAPI or AsyncAPI file and no official SDKs.

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.

Rime TTS API + MCP

1. Always send modelId, since a missing one routes to Mist v3 and Coda speakers then fail.
2. Keep HTTP requests under 1,000 characters or use the WebSocket.
3. Back off on 429 before reopening a WebSocket, and remember each retry bills again.
4. Use /ws3 when you need word timestamps.
5. Keep the key server-side, since WebSockets take it in a header and there are no short-lived tokens.