AssemblyAI Alternatives (2026): 10 Speech to Text APIs

If you're building transcription into a product, swapping speech providers is a bigger job than it first looks. AssemblyAI describes itself as speech AI models and infrastructure for transcribing and understanding speech. Developers and product teams usually move on for one of three reasons: they want another vendor's hosted API, they'd rather call a Whisper model somewhere else, or they need audio to stay on their own servers.

Here are ten alternatives, in no particular order. They're grouped by approach first, because a self-hosted engine and a cloud API solve different problems, even when both hand you back a transcript.

Specialist APIs, general AI platforms or self-hosted engines

Specialist speech APIs. These vendors live and breathe voice. Deepgram bundles speech-to-text with text-to-speech and voice agent APIs. Rev AI is the developer-first API from the Rev family. Gladia leans towards turning conversations into structured data you can feed into a voice product. Sonix is the odd one out: it's transcription software first, with a developer API on the side, which suits teams that want people and code using the same service.

Enterprise cloud services. IBM Watson Speech to Text is the choice to look at if you're already buying from a large cloud vendor and want transcription, captions and insights from the same account and contract.

General AI platforms and hosted Whisper. If your product already calls large language models, OpenAI speech to text keeps transcription in the same API platform as everything else. Groq offers a fast inference endpoint for transcription and translation. Together AI Whisper hosts the Whisper Large v3 model for you, so you can use that model without running GPUs yourself.

Self-hosted engines. Vosk runs offline on phones, small boards and servers, so audio never has to leave your hardware. NVIDIA Riva is a heavier SDK for building real-time speech pipelines you deploy wherever you like. Both mean your team owns the updates, the hardware and the scaling, which is the trade for keeping data in-house.

Ten speech recognition options side by side

Tool	Approach	Good for
Deepgram	Specialist speech API	Speech to text, voice output and voice agents together
Gladia	Specialist speech API	Turning conversations into structured data
Groq speech to text	Fast inference API	Quick transcription and translation of shorter files
IBM Watson Speech to Text	Enterprise cloud API	Live transcripts and captions inside a big cloud
NVIDIA Riva	Self-hosted SDK	Building real-time speech pipelines you deploy yourself
OpenAI speech to text	General AI platform API	Adding transcription beside other AI model calls
Rev AI	Specialist speech API	A developer-first API from a transcription brand
Sonix	Transcription platform	Transcribing audio, video and documents with an API option
Together AI Whisper	Hosted Whisper	Running the Whisper model without hosting it
Vosk	Self-hosted engine	Offline recognition on phones, small boards and servers

Who each speech service suits

Listed A to Z, one line each.

Deepgram

Best for: Developers who want speech-to-text, text-to-speech and voice agent APIs from one voice AI vendor, which pitches itself as real-time and built for scale.

Gladia

Best for: Voice products that need audio transcribed and enriched through a single API, so calls and meetings come back as structured data rather than plain text.

Groq speech to text

Best for: Apps that need fast transcription and translation of audio files through a simple API, with file size caps that differ by tier per its docs.

IBM Watson Speech to Text

Best for: Enterprises wanting a cloud service from IBM that converts audio to text for real-time transcription, captions and insights through scalable APIs.

NVIDIA Riva

Best for: Engineering teams building and deploying their own real-time speech pipelines anywhere, using an SDK that's part of NVIDIA Nemotron Speech for developers.

OpenAI speech to text

Best for: Products already calling other OpenAI models that want recorded and streamed transcription, plus specialised speech features, from the same API platform.

Rev AI

Best for: Product teams after a developer-first speech-to-text API from the Rev family; the vendor calls it the most accurate in the world, which is its own claim.

Sonix

Best for: Content and research teams that want ready-made transcription software for audio, video and documents, with a developer API available when they need it.

Together AI Whisper

Best for: Builders who'd like the Whisper Large v3 model hosted for them, with broad language coverage and support for large files, per the vendor.

Vosk

Best for: Apps where audio mustn't leave the device or server, with offline recognition for Android, iOS, Raspberry Pi and servers in several programming languages.

Upload limits and hosting decide before accuracy does

Every vendor here talks about accuracy, and it's the wrong place to start. The first thing that rules a tool in or out is usually far more boring: how big a file it'll accept, whether it streams, and where your audio goes once it's uploaded.

Here's a concrete example. Groq's own documentation states that its speech to text endpoint accepts files of up to 25 MB on the free tier and 100 MB on the dev tier. That's fine for voice notes and short calls, but a long podcast episode or a recorded webinar will need splitting into chunks, or a different plan. Splitting sounds simple until you're stitching timestamps and speaker labels back together across every join.

Hosting works the same way. If your contracts say customer audio can't leave your own servers, every cloud API is out before you've heard a single transcript, and the self-hosted engines become your whole shortlist. If you've no one to run models and GPUs, the reverse is true, and a hosted endpoint is the sensible route.

Once those limits have done their work, look at the features that break products in real use:

• Speaker labels, so you can tell who said what on a call with two or more voices.
• Streaming versus batch, because live captions and voice agents need results while people are still talking.
• Language support, including the accents and dialects your users speak, not just the language name.
• File length and size limits on every tier you might use.
• Where audio and transcripts are stored, and for how long.

My advice for developers switching speech providers

Write down your hard limits before you open a single trial: the longest file you'll ever send, whether you need live transcripts or batch, which languages matter and whether audio may leave your infrastructure. Those four answers usually cut ten options down to two or three, and they're answers no vendor's home page can give you.

Then treat every accuracy and speed claim as the vendor's claim, because that's all they are. Several pages here make bold statements, and none of those numbers came from your audio. If you're choosing a ready-made app rather than an API, my guide to AI transcription software is a better place to start, and if meetings are the main source of your recordings, my list of Fireflies alternatives covers note-takers that join calls for you.

Finally, keep a thin layer in your own code between your product and the provider, so switching is a configuration change rather than a rewrite. Speech vendors update models and terms often, and you don't want to repeat this whole migration later.

Build a test set from your own recordings

1. Pick twenty to thirty real clips that reflect your users: strong accents, phone-quality calls, crosstalk, background noise and the technical words your product hears.
2. Write a careful human transcript for each one, so you've got something reliable to compare against.
3. Run the same clips through two or three shortlisted options, using the settings you'd use in production.
4. Check speaker labels, punctuation, timestamps and how each one copes with names and jargon, not just overall word errors.
5. Test the awkward cases on purpose: your longest file, a live stream if you need one, and a language you rarely see.
6. Read each vendor's data retention terms before any customer audio goes in.

Do you make speech to text APIs or models? Tool makers can ask to be considered for a featured place on this list.

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Frequently asked questions

What's the difference between a speech API and a self-hosted engine?

A speech API runs on the vendor's servers: you send audio and get text back. A self-hosted engine runs on your own hardware, so audio stays with you, but you manage the models, updates and scaling.

What does hosted Whisper mean?

Whisper is a speech recognition model. Some AI platforms run it on their own infrastructure and give you access through an API, so you can use it without setting up GPUs yourself.

How do I handle audio files that are too big for an API?

Split them into shorter chunks with a small overlap, transcribe each chunk, then join the results and fix the timestamps. Or choose a tier or provider that accepts larger files.

Do I need speaker labels?

If your audio has more than one voice, such as sales calls, interviews or meetings, you almost certainly do. Check how each service labels speakers and how it handles people talking over each other.

Can speech recognition run without an internet connection?

Yes. Some engines are built to run offline on phones, small boards or your own servers, so audio needn't leave the device. Expect to manage models and updates yourself.

How should I compare accuracy claims?

Ignore the headline numbers and run your own recordings through each shortlisted tool. Compare the results against a human transcript, paying close attention to names, jargon and accents.