Pick a Deepgram alternative by the part of it you lean on: plain transcription, live streaming speech-to-text, or the voice agent layer it now sells alongside. Transcription-only services are the easiest to swap in, while voice suites replace more of your stack, so decide what you're replacing before you compare accuracy or speed.
Are you leaving a transcription engine or a voice platform? It's worth settling that first, because Deepgram's homepage now sells speech-to-text, text-to-speech and voice agent APIs for real-time enterprise voice products, all under one roof.
The nine options below are listed in no particular order. Nothing is ranked, nothing is linked, and each one gets a single line on what it's built for. The section straight after this one is the one that'll save you the most time.
Transcription, the agent layer, or both?
Deepgram's homepage banner announces that Daily and Pipecat are joining the company, and the voice agent API sits next to transcription in its main menu. So depending on how you built on it, you may be leaving a voice-agent platform rather than a transcription engine. Those are two very different migrations.
If all you send is audio in and text out, the move is mostly about accuracy on your own recordings and how the output is shaped. If you built a voice agent, you're also replacing the voice your callers hear, the way the agent listens and replies, and the glue between the two. Soniox and Smallest.ai both cover more than transcription, so they're the ones to look at if you want one vendor for several of those layers. Vocapia and SpeechText.AI sit much closer to transcription only, which keeps things simpler if that's all you need.
One warning before you read anyone's homepage too closely. Accuracy and latency claims aren't measured the same way from vendor to vendor: different audio, different languages, different conditions. Treat every headline number as a reason to test, never as a result you can compare across pages.
What each speech service is built for
AssemblyAI
Best for: Developers who want speech understood as well as transcribed, through a set of AI models served over an API.
Gladia
Best for: Product teams turning calls and meetings into structured data, with transcription and enrichment handled by a single developer API.
IBM Watson Speech to Text
Best for: Enterprises wanting real-time transcription, captions and insights from audio, sold as scalable AI APIs by a long-standing enterprise vendor.
OpenAI speech to text
Best for: Apps already sending other model calls through the same account that need recorded files transcribed or file transcripts streamed back.
Smallest.ai
Best for: Builders who want speech-to-text, text-to-speech and voice agents in one realtime suite, with its Pulse model doing the listening.
Soniox
Best for: Multilingual products needing realtime speech-to-text, text-to-speech and translation from one speech API, with low-latency streaming as the vendor's headline claim.
SpeechText.AI
Best for: Anyone with audio files to turn into text who doesn't need agents, synthetic voices or a wider platform around the transcript.
Verbit
Best for: Law firms, courts, media groups and universities wanting AI transcription and captioning delivered as a service rather than a bare API.
Vocapia
Best for: Broadcast monitoring, lecture transcription, subtitling and conference calls, with diarization, language identification and keyword search in its VoxSigma suite.
Speech APIs and services compared
| Tool | Type | Where it's strongest |
|---|---|---|
| AssemblyAI | Speech AI API | Understanding speech, not only the words |
| Gladia | Audio API | Turning conversations into structured data |
| IBM Watson Speech to Text | Enterprise speech API | Captions and insights at enterprise scale |
| OpenAI speech to text | Big-lab speech API | Keeping speech inside an account you already use |
| Smallest.ai | Voice AI suite | Listening, speaking and agents from one vendor |
| Soniox | Realtime multilingual API | Live speech across many languages |
| SpeechText.AI | Transcription service | Plain file-to-text jobs |
| Verbit | Managed transcription | Legal, media and education captioning |
| Vocapia | Software and service | Broadcast, lecture and conference transcription |
Sorted by what you used Deepgram for
Transcription with something extra on top. If you were turning calls or meetings into data, not just text, two developer APIs lead with exactly that. AssemblyAI pitches understanding speech, not only writing it down. Gladia describes itself as audio infrastructure that transcribes and enriches conversations through one API, so the output is structured rather than a wall of words.
Speech inside a platform you already pay attention to. If your app already calls a big model provider, keeping speech in the same account is tidy. OpenAI's speech to text covers recorded files and streamed file transcripts alongside its other API features. IBM Watson Speech to Text is the enterprise route, built for real-time transcription and captions at scale.
Want AI doing the heavy lifting in your marketing?
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A service, not an API. Some teams were only using an API because they needed transcripts, and a managed service would suit them better. Verbit is built for that, with AI transcription and captioning aimed at legal, court, media and education work.
The advice I'd give a voice team before switching
Don't move everything at once. Split your traffic by job first: recorded files, live streams and any agent calls. Recorded files are the easiest to switch and the lowest-risk place to learn how a new provider handles your audio. Live streams come next, because delay depends on your own servers and network as much as the vendor's. Agent calls come last, because they touch the most code and the most customers.
If text-to-speech is the half you're replacing too, keep the two decisions separate unless one suite clearly wins on your own tests. A single vendor is simpler to run, but two specialists can each be stronger at their own job.
For a wider view of the transcription side, my guide to the best speech-to-text APIs goes broader than this list, and my roundup of AI transcription software covers ready-made apps for teams who don't want to code at all. If the voice your callers hear is the bigger worry, my list of Cartesia alternatives looks at the text-to-speech side.
Build a test set from your own recordings
The only comparison that counts is the one run on your audio. It takes an afternoon to set up and saves weeks of second-guessing later.
- Collect a small set of real clips: quiet calls, noisy ones, strong accents, people talking over each other and the jargon your customers use.
- Write a careful human transcript for each clip so you've got something to score against.
- Run every clip through each provider on your shortlist with the settings you'd use in production.
- Count the errors that matter to you, such as names, product terms and numbers, not just overall word errors.
- For live use, measure the delay from your own servers, at the times of day you're busiest.
- Check the output format: timestamps, speaker labels and punctuation, because that's what your code reads.
- Keep the clips. You'll want them again the next time a vendor changes its model.
A provider that wins on someone else's benchmark can still stumble on your callers. Your own clips will tell you which one doesn't.
Do you make a speech-to-text or voice API? Tool makers can ask to be considered for a featured place on this list.
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Frequently asked questions
What does Deepgram sell now?
Its homepage describes speech-to-text, text-to-speech and voice agent APIs for real-time enterprise voice products, and its banner says Daily and Pipecat are joining the company.
Is a transcription-only service enough to replace it?
Yes, if you only send audio and read back text. If you built voice agents on it, you'll also need to replace the speaking and agent parts, either with a voice suite or with separate tools.
How should I compare accuracy between speech APIs?
Run the same set of your own recordings through each one and score the results against a human transcript. Vendors measure accuracy in different ways, so their published claims don't line up.
What's speaker diarization?
It's labelling who spoke when in a recording. It matters for meetings, interviews and calls, where a transcript without speaker labels is much harder to use.
Should I use a managed transcription service or an API?
An API suits products that process audio automatically. A managed service suits teams who need finished transcripts or captions, often for legal, media or education work, without building anything.
Can I switch speech providers without rewriting my app?
Usually you'll need some changes, because each provider shapes its output differently. Keeping your speech calls behind one small layer in your code makes the next switch much easier.
