Bland AI API + MCP vs CueLight

Bland AI API + MCP

One per-minute rate covering STT, LLM, TTS and telephony, $0.14 on Start and $0.12 on Build. No OpenAPI document.

CueLight

CueLight gives a voice pipeline a local end-of-turn probability every 20 ms from audio alone, with a 1.9 MB model and Apache-2.0 weights. It is one day old, English only, installs from Git rather than PyPI, has no CI or changelog, and its accuracy figures are the author's own.

Bland AI API + MCP

1. Call wait_for_call (it waits up to 45 seconds) instead of polling get_call_log
2. Send destructive MCP tools a second time with the confirmation argument
3. Space calls to the same number at least 10 seconds apart or expect a 429
4. Budget $0.015 for every outbound attempt, including ones that never connect
5. Run validate_pathway before saving a pathway graph

CueLight

1. Feed 320 float32 samples at 16 kHz mono per push. Resample first, or the call raises an AssertionError
2. Spell the preset as snappy or careful. Any other name silently uses default thresholds
3. Call clear() before a new call or session. Pass reset_on_turn_start=False if your pipeline already resets per turn
4. Pin weights by downloading them to a local folder and passing that path to from_pretrained
5. Don't use it to identify speakers or clone voices. The training data's conditions forbid it