Audio and podcasting decision

TranslateMom

A competent developer can assemble a useful self-hosted replacement using existing open-source projects for transcription, translation, and dubbing; the vendor offers convenience and polish but no documented durable moat.

Visit website
You pay

$4.5/mo

$54/yr

Read off the official pricing page.

You’d pay instead

$100one-off80 h to build

$75/mo6 h/mo upkeep

On cash alone, building overtakes the subscription at 19 seats.

Open-source builds that already do this

Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need - the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship. All TranslateMom alternatives, with the arithmetic →

What a replacement has to do

  • Upload video -> extract audio -> transcribe -> translate -> generate/aligned subtitles -> optional TTS dub -> export SRT/VTT/ASS and video with burned-in subtitles or new audio track.

What it still won’t have

  • Priority queueing and enterprise SLA/priority support
  • Custom branding, long-term storage tiers and team/collaboration UX
  • Any proprietary model optimizations or private model access the vendor may provide
  • The vendor's curated preset voices and tuned pipelines for noisy audio

What remains hard

  • Product polish and ongoing maintenance
Read the build prompt

First-year cost

Keep paying

Paying is—cheaper in year one.

On cash alone, building overtakes the subscription at 19 seats.

Paid seatsseats

Money you would actually spend

Keep paying
—

Subscription price × seats × 12

Build it
—

AI build —APIs + hosting —

Time you would spend

—

—

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 self-hosted AI video subtitle+translate+dub service using: React frontend, Node/Express API, PostgreSQL for task metadata, Redis + BullMQ for job queue, S3-compatible object storage, and workers in Python for media tasks. Core features: (1) accept video uploads and remote URLs; (2) extract audio, run Whisper or cloud ASR for time-aligned transcripts; (3) translate transcripts via an LLM/MT API and re-align subtitles; (4) optional TTS generation and audio mixing to produce a dubbed track; (5) subtitle editor (preview, edit timings), and export to SRT/VTT/ASS and burned-in MP4. Out of scope: multi-tenant billing dashboard, advanced proprietary voice cloning, and large-scale enterprise SLA. Include retries, queue backpressure, storage lifecycle, tests for API and worker jobs, CI pipeline, and error handling/logging for failed transcriptions and API rate limits.
How we checked4 sources · 2/3 runs agreed · evidence score 89

How the score was reached

  • Build verdict base78
  • An open-source build was found+5
  • 4 cited sources+3
  • Price verified on pricing page+3
  • Evidence score89

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 · 4

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 recorded