Audio and podcasting decision
Speechyou
A competent developer can reproduce the core transcription, timestamping, and summary features using open-source projects, but cannot easily match the vendor's claimed proprietary model and hosted UX without ongoing engineering and model work, so keeping the paid service may be justified for best accuracy and polish.
Visit website↗$15/mo
$180/yr
Read off the official pricing page.
$100one-off76 h to build
$50/mo6 h/mo upkeep
On cash alone, building overtakes the subscription at 4 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
- Record audio (browser recorder or file upload) → transcribe audio to text with timestamps → generate summaries/action items via an LLM → export/share transcripts and subtitles.
What it still won’t have
- Proprietary "MultiLingual Pro" model accuracy and any tuned models
- Hosted workspace UX (workspaces, shareable view-only guests) and priority support
- Built-in iOS app and any managed integrations
What remains hard
- Proprietary models
Whisper and our proprietary MultiLingual Pro model convert your speech to text with incredible accuracy.
First-year cost
Keep paying
Paying is—cheaper in year one.
On cash alone, building overtakes the subscription at 4 seats.
Money you would actually spend
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
Build a self-hosted transcription web app using: React frontend, Node.js (Express) backend, Postgres for metadata, S3-compatible storage, Redis + Bull for job queue, and open-source ASR (use whisper.cpp or faster-whisper with GPU/onnx). Core features in scope: browser recorder (mic + system audio), file uploads, background transcription with timestamps, optional speaker diarization (whisperX), LLM-based summaries/action-items via an external LLM API, export to TXT/SRT/VTT/JSON, basic shareable links and a single-user workspace. Out of scope: mobile native apps, multi-tenant billing, and training proprietary ASR models. Include error handling, retries for transcription jobs, unit tests for API endpoints, and deployment docs (Docker Compose + Kubernetes manifests).
How we checked
How the score was reached
- Pay verdict base20
- 3 cited sources+3
- Price verified on pricing page+3
- Hard moats found in the evidence-3
- Evidence score23
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.
- official productSpeechyou
- official pricingSpeechyou Pricing
- official docsEducation use cases
Integrity checks
What held up, and what did not.
