Whisper.Api is live · free · last verified 23 Oct 2026 · viability monitor
Whisper.Api
Self-hosted speech-to-text API that speaks Deepgram's language, built on whisper.cpp so your audio stays on your servers.
Whisper.Api earns a place when the hard requirement is that audio never leaves your network and someone on the team can run a Docker container. The Deepgram-compatible /v1/listen endpoints mean an existing Deepgram SDK integration can be repointed with minimal code changes, which makes a cheap side-by-side evaluation possible before you commit. The built-in CLI key management removes the external auth dependency entirely, and diarization plus SRT/VTT export covers meeting and subtitle work without a second tool. The cost is explicit: transcription runs on your CPU through whisper.cpp, you compile the binary and supply your own GGML models, and throughput tracks your hardware. If you need a
Verdict generatedVerified 7d ago · liveness 51/100 · cite: rightaichoice.com/tools/whisper-api
- Developers needing private, self-hosted speech-to-text
- Teams migrating off Deepgram to an on-premises deployment
- Organizations with data-residency rules covering audio
- Hobbyists and makers running AI on local hardware
- Teams that need a managed cloud service with an SLA
- Non-technical users who cannot deploy and operate a server application
- High-volume batch pipelines needing elastic concurrency without added orchestration
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Skip Whisper.Api if you need a vendor-held SLA for latency and uptime, or if no one on your team can compile a C++ binary and operate a Dockerized FastAPI service.
You supply your own GGML quantized .bin models, so storage and any model-download bandwidth are on your bill, not the vendor's.
Whisper.Api is MIT licensed and self-hosted, so your spend is servers and engineer time rather than a per-minute transcription bill. Against managed transcriber APIs that charge per audio minute, the crossover comes at steady, predictable volume where a box you already run covers the load. At spiky or small volumes, a managed API is usually cheaper once you price in operations.
In short
Whisper.Api — Self-hosted speech-to-text API that speaks Deepgram's language, built on whisper.cpp so your audio stays on your servers. Best for Developers needing private, self-hosted speech-to-text, Teams migrating off Deepgram to an on-premises deployment, Organizations with data-residency rules covering audio. Free to use.
What is Whisper.Api?
Whisper.Api — Self-hosted speech-to-text API that speaks Deepgram's language, built on whisper.cpp so your audio stays on your servers. Best for Developers needing private, self-hosted speech-to-text, Teams migrating off Deepgram to an on-premises deployment, Organizations with data-residency rules covering audio. Free to use.
Whisper.Api wraps the whisper.cpp transcription engine in a Deepgram-compatible REST and WebSocket interface that you deploy on your own infrastructure. A FastAPI server on port 7860 sits between your clients and the compiled whisper-cli binary: it handles audio conversion, authentication and response formatting, then shells out to whisper.cpp and reads GGML quantized .bin models from a local models directory. The surface is small and familiar. A POST /v1/listen endpoint transcribes an uploaded file or a remote URL. A WebSocket /v1/listen endpoint handles real-time microphone streaming, with PCM, WebM, OGG and FLAC supported via auto-detection. GET /v1/models lists the GGML models you have on disk and requires no auth. A POST /v1/auth/test-token endpoint mints API keys but ships disabled by default and is documented as development-only, so production key management happens offline via CLI. Where Whisper.Api carries a clear edge is privacy and swappability. Your audio never leaves your servers, which makes it usable for regulated recordings and data-residency rules. Because it answers on Deepgram's /v1/listen paths, you can repoint an existing Deepgram SDK integration with minimal code changes, then compare cost and quality against the managed API. Speaker diarization and SRT/VTT subtitle export are built in, so meeting transcripts and video captioning don't need a second tool. The honest trade is operational. This is not a managed service: you clone the repo, install Python 3.10+ dependencies, compile the C++ binary, supply your own quantized GGML models, and own throughput, latency, uptime and scaling. Concurrency and speed track your CPU and your model choice, which trades accuracy against speed. It ships as Docker and Docker Compose on Debian Bookworm, persists keys in SQLite via SQLAlchemy, and is MIT licensed. It fits developers and organizations that need private transcription and have the server skills to run a container; it does not fit teams that want an SLA or non-technical buyers who cannot operate a Python and C++ service.
Key features
- Deepgram-compatible POST /v1/listen endpoint for file and URL transcription
- Real-time WebSocket /v1/listen endpoint for microphone streaming
- Local CPU transcription via the compiled whisper.cpp binary
- Audio format auto-detection across PCM, WebM, OGG and FLAC
- Speaker diarization on transcribed audio
- Subtitle export to SRT and VTT
- Offline API key generation and revocation via CLI
- Unauthenticated GET /v1/models endpoint listing installed GGML models
- POST /v1/auth/test-token key minting, disabled by default and development-only
- Works from cURL, Python, JS or the Deepgram SDK
- FastAPI server on port 7860 with native WebSocket support
- SQLite via SQLAlchemy for persistence
- Docker and Docker Compose deployment on Debian Bookworm
- GGML quantized .bin models read from a local models directory
- MIT licensed source you can audit and fork
Our long-form take written Jul 31, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
Models under the hood
as of 2026-10-22
Limitations
- Transcription runs locally on CPU through the compiled whisper.cpp binary, so throughput, latency and concurrent stream capacity depend entirely on your hardware and the GGML model you pick.
- You build the binary and supply your own quantized .bin models; model choice trades accuracy against speed.
- The /v1/auth/test-token endpoint is disabled by default and documented as development-only, so production key management is CLI work.
- Persistence is SQLite via SQLAlchemy, which is not a job queue.
- Diarization and subtitle export exist, but scaling, tuning, retries and uptime are your responsibility.
as of 2026-10-23
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Is Whisper.Api still active?
Whisper.Api is live · free · last verified 23 Oct 2026 · viability monitor.
Viability score
How well maintained and how widely used is Whisper.Api? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What do users say about Whisper.Api?
What people actually say about Whisper.Api — is it worth it?
Our community sentiment research written Jul 31, 2026 is more than 90 days old, so it is being re-reviewed against current data and is not shown.
How we verified this
Every listed fact about Whisper.Api— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 23 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Whisper.Api 7 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed setup time, switching in, features, pricing fit, switching out, use cases, who should skip it, models, summary, hidden costs, our verdict, integrations, limitations, description
- — changed pricing page
- — changed hidden costs, description, our verdict, features, who it suits, limitations, switching in, switching out, models, who should skip it, pricing fit, setup time, summary, use cases
- — changed SSL certificate
- — changed limitations
- — changed domain expiry, SSL certificate
Showing the 6 most recent of 7 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Whisper.Api cost in 2026?
Whisper.Api: Free to use. Pricing last verified 23 Oct 2026.
Where the pricing makes sense
The company stage and team size where Whisper.Api's pricing actually pencils out — and where peers do it cheaper.
Whisper.Api is MIT licensed and self-hosted, so your spend is servers and engineer time rather than a per-minute transcription bill. Against managed transcriber APIs that charge per audio minute, the crossover comes at steady, predictable volume where a box you already run covers the load. At spiky or small volumes, a managed API is usually cheaper once you price in operations.
Has Whisper.Api's pricing changed?
No plan price for Whisper.Api has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Whisper.Api?
Pick Whisper.Api if you are Developers needing private, self-hosted speech-to-text, Teams migrating off Deepgram to an on-premises deployment, Organizations with data-residency rules covering audio, Hobbyists and makers running AI on local hardware.
Look elsewhere if you are Teams that need a managed cloud service with an SLA, Non-technical users who cannot deploy and operate a server application, High-volume batch pipelines needing elastic concurrency without added orchestration.
Skip it if Skip Whisper.Api if you need a vendor-held SLA for latency and uptime, or if no one on your team can compile a C++ binary and operate a Dockerized FastAPI service.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Whisper.Api actually fits — and what changes day-one when you adopt it.
You have a Deepgram-backed transcription service and a compliance rule that patient audio cannot leave the VPC. You deploy Whisper.Api via Docker Compose, install GGML models, mint keys with the CLI, and swap the base URL in the existing Deepgram SDK client.
Outcome: Transcription keeps working on the same /v1/listen contract while audio stays inside the network, and you can diff transcripts against the old provider on real recordings before cutting over.
You run the FastAPI server locally and post media files to /v1/listen, then export SRT and VTT alongside each published video.
Outcome: Subtitle files come out of the same pipeline that transcribes, with no cloud account and no per-minute charges.
You open the WebSocket /v1/listen endpoint and stream PCM from a microphone, sizing CPU for the number of concurrent streams you actually need.
Outcome: Live transcripts arrive on hardware you control, with latency governed by your chosen GGML model rather than a provider's queue.
Use cases
- Transcribe meeting recordings on your own servers so audio never leaves the network.
- Stream microphone input over WebSocket for live captioning on local hardware.
- Repoint an existing Deepgram integration at a self-hosted endpoint with minimal code changes.
- Run offline speech-to-text on regulated or sensitive audio that cannot go to a cloud API.
- Generate SRT or VTT subtitles for video as part of an internal publishing pipeline.
- Pilot a self-hosted transcriber against a cloud API using identical endpoints before committing.
Setup time & first value
How long it actually takes to get something useful out of Whisper.Api — broken out by persona, not the marketing-page minute.
Developers comfortable with Docker: roughly an hour to clone, install Python dependencies, build whisper-cli, drop in a GGML model and hit /v1/listen. Teams migrating a Deepgram integration: add an afternoon to repoint the SDK and diff transcripts. Without prior Docker and C++ build experience, budget a full day or more.
Switching to or from Whisper.Api
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From Deepgram: point the Deepgram SDK base URL at your self-hosted instance and reuse the existing /v1/listen calls with minimal code changes.
- →From a cloud transcription API: post files or URLs to the self-hosted POST /v1/listen endpoint and parse the returned transcript instead of the old response.
- →From manual transcription: deploy via Docker Compose, install GGML models, and batch existing recordings through POST /v1/listen with SRT or VTT export.
- ↗To Deepgram: the /v1/listen contract is shared, so swapping the base URL back is the same exercise in reverse; re-add cloud key management.
- ↗To another managed API: re-implement request and response mapping, since only Deepgram's endpoint shape is mirrored here.
- ↗To a self-run successor: keep your GGML model files and audio pipeline, and replace the FastAPI layer around whisper-cli.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Whisper.Api”, and we withheld 5: 5 did not mention Whisper.Api. Showing the 1 we can prove is about Whisper.Api.
Official links
What are the best Whisper.Api alternatives?
The closest alternatives to Whisper.Api we list are Deepgram, Universal and Gladia. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Deepgram | Paid | Pricing not published | — |
| Universal | Freemium | Pricing not published | — |
| Gladia | Freemium | Pricing not published | — |
Alternatives to Whisper.Api
View all
Tools that pair well with Whisper.Api
Common stack mates teams adopt alongside Whisper.Api, with the specific reason each pairing earns its keep.
Deepgram
Deepgram sells speech-to-text, text-to-speech and a one-call Voice Agent API, with Flux STT doing turn detection and interruption handling for live agents.
Universal
Universal-2 delivers accurate async speech-to-text at $0.15/hr for batch audio — with 99-language coverage.
Gladia
Real-time and async speech-to-text API with speaker diarization, sentiment, and 100+ language audio intelligence.
Whisper.Api vs its closest rival
Whisper.Api's closest head-to-head is with Spider Cloud — These are not competitors, and the honest answer is that you should never be choosing between them — they belong in different layers of a stack. Pick Whisper.Api if your problem is turning audio into text without shipping recordings off-prem: it is free to run, Deepgram-compatible, and fully offline, at the cost of you owning deployment, model selection, and scaling. Pick Spider Cloud if your problem is pulling live web pages into an agent or RAG pipeline: you get rendering, crawling, search, proxies, and an MCP endpoint behind one key, on a freemium model where proxies, AI extraction, and Unlimited-tier geo-targeting can bill separately. If you somehow need both, you run both — a local transcription service and a hosted scraping API do not replace each other. Verdict generated
Head-to-head comparisons
Whisper Api vs Spider Cloud
These are not competitors, and the honest answer is that you should never be choosing between them — they belong in different layers of a stack. Pick Whisper.Api if your problem is turning audio into text without shipping recordings off-prem: it is free to run, Deepgram-compatible, and fully offline, at the cost of you owning deployment, model selection, and scaling. Pick Spider Cloud if your problem is pulling live web pages into an agent or RAG pipeline: you get rendering, crawling, search, proxies, and an MCP endpoint behind one key, on a freemium model where proxies, AI extraction, and Unlimited-tier geo-targeting can bill separately. If you somehow need both, you run both — a local transcription service and a hosted scraping API do not replace each other.
GeneratedWhisper Api vs Dbos
Choose Whisper.Api if you need a private, offline speech-to-text solution that mirrors Deepgram's API. Choose DBOS if you're building fault-tolerant AI workflows or agents and already use Postgres — it eliminates extra orchestration infrastructure. They solve completely different problems, so your pick depends on whether your need is audio transcription or reliable backend execution.
GeneratedWhisper Api vs Temporal Ai
If you need to build reliable, crash-proof AI agents or orchestrate long-running workflows, Temporal is the clear choice—it’s trusted by OpenAI and NVIDIA for mission-critical durability. If your need is private, self-hosted speech-to-text with no cloud dependence, Whisper.Api offers a straightforward, cost-free solution. They solve completely different problems; pick one based on whether you need durability or transcription.
GeneratedWhisper Api vs Voyage Ai
These aren't competitors — don't shortlist them against each other. Pick Voyage AI if your problem is retrieval quality: embeddings and rerankers for enterprise RAG over finance, legal, code, or image+text corpora, with domain and company-specific tuning (contact sales for pricing). Pick Whisper.Api if your problem is transcription: a free, self-hosted, Deepgram-compatible speech-to-text server you run yourself for privacy and offline operation, accepting that you manage models, scaling, and performance. The only overlap is that a RAG stack could eventually feed Voyage embeddings on top of Whisper.Api transcripts, which is a complementary pairing, not a choice.
GeneratedWhisper Api vs Rapidsos
RapidSOS and Whisper.Api serve completely different worlds—RapidSOS is a mission-critical emergency response platform for public safety agencies, while Whisper.Api is a self-hosted speech recognition tool for developers. There is no overlap. Choose RapidSOS if you run a 911 center or need real-time emergency data integration. Choose Whisper.Api if you need private, on-premise speech-to-text without cloud dependency.
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