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

ElevenLabs Text to Speech API + MCP

Keys can be limited to chosen endpoints and given a credit quota, and service accounts hold keys that don't belong to a person. Content may be used for training unless you opt out under Data use, and the opt-out only applies going forward.

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.

ElevenLabs Text to Speech API + MCP

1. Call Eleven v4 through Text to Dialogue, not /v1/text-to-speech.
2. Spell out numbers yourself on eleven_flash_v2_5, which doesn't normalise them by default, and turning that on is Enterprise only.
3. On 429 read code, back off on rate_limit_exceeded and wait for running requests on concurrent_limit_exceeded.
4. Keep each request under 10,000 characters on v4 and Multilingual v2, 5,000 on v3.
5. Give the agent a key scoped to Text to Speech with a credit quota.