Image and video decision

Tavus

A narrow, voice-first PAL (STT+LLM+TTS+basic avatar) is feasible for a single developer in about a week, but reproducing Tavus's proprietary high-fidelity face rendering, replica training, stock replica library, and enterprise-grade SLAs is not practical without their proprietary models and infrastructure.

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You pay

$20/mo

$240/yr

Read off the official pricing page.

You’d pay instead

$50one-off30 h to build

$200/mo8 h/mo upkeep

On cash alone, building overtakes the subscription at 11 seats.

No open-source build does this yet

Nothing published replaces this one, so a replacement starts from an empty file. Here is what it would have to cover.

What a replacement has to do

  • Accept user audio/video input, transcribe (STT), feed context to an LLM, synthesize output audio (TTS), and stream rendered video or avatar over WebRTC.

What it still won’t have

  • Proprietary, high-fidelity face rendering models (Phoenix-4) and perception/turn-taking models (Raven-1, Sparrow-1)
  • Stock replica library and integrated replica training pipeline
  • Out-of-the-box low-latency WebRTC video pipeline tuned for realism
  • Enterprise features like guaranteed SLAs, SOC2/HIPAA compliance, and white-label replica consent flows

What remains hard

  • Proprietary modelsWe build models to teach machines to see, hear, understand and even look human.
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 11 seats.

Paid seatsseats

Money you would actually spend

Keep paying
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Subscription price × seats × 12

Build it
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AI build —APIs + hosting —

Time you would spend

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What you would spend

What we assumed

The verdict above measures whether you could build it. This one is only about money.

Runnable build prompt

Not run yet
Build a minimal real-time conversational PAL (voice-first) using Node.js + Express, Postgres, a hosted STT (e.g., Whisper API), an LLM (OpenAI-compatible), and a hosted TTS (ElevenLabs-style) with a WebRTC client for audio streaming. In scope: WebRTC audio capture/client, server STT ingestion, LLM orchestration with short-term memory persisted in Postgres, TTS generation and audio streaming back, conversation transcripts stored, basic auth, and a simple web UI that shows timestamps and transcripts. Out of scope: photorealistic face rendering, replica training, large-scale concurrency, and SOC2/HIPAA certification. Deliverables must include error handling for API failures, retries, unit tests for orchestration logic, and a Docker compose for local dev.
How we checked3 sources · 2/3 runs agreed · evidence score 55

How the score was reached

  • Partly verdict base52
  • 3 cited sources+3
  • Price verified on pricing page+3
  • Hard moats found in the evidence-3
  • Evidence score55

The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time - so the same evidence always produces the same number.

How scoring works →

Cited sources · 3

Every page the run actually retrieved.

Integrity checks

What held up, and what did not.

✓ Price read off the page! 2 of 3 runs agreed; the verdict is the majority✓ Citations limited to fetched pages! 1 moat quoted from the page