The right replacement for a real-time voice API depends on why you're moving: more voices and languages, private deployment in your own cloud, stronger regional language support, or a vendor that also covers studio work. Shortlist two or three, then measure time to first audio from your own servers, because vendors don't all time the same thing.
Most developers looking past Cartesia aren't unhappy with the speed. Its Sonic model is built for real-time voice, with Ink for speech to text and managed voice agents alongside. The push to move usually comes from somewhere else: a language you need that isn't covered well, a security team asking for the model to run inside your own cloud, or a product that needs a far bigger voice library.
Below are nine voice APIs and platforms, in no particular order. I've grouped them by the reason you might switch, because that's the decision you're making. First, though, a warning about the one number every vendor leads with.
Why two speed claims rarely compare
Every voice API leads with speed, usually as time to first audio: how long between sending text and hearing the first sound. The trouble is that vendors don't measure it the same way. Some quote the model's own processing time. Others quote the full trip, including the network and streaming the audio back to your app.
Murf AI's Falcon page is a useful example because it shows both. It states 55ms for model inference and 130ms end to end, as the vendor's own numbers. The gap between those two is the part a headline can leave out. Put one vendor's model-only number next to another's round-trip number and the comparison tells you nothing.
So treat every speed claim on this page, and on every vendor page, as a starting point. The only number worth comparing is the one you measure from your own servers, in your own region, with the kind of text your product sends.
Nine voice APIs side by side
| Tool | Kind of API | Strongest reason to pick it |
|---|---|---|
| Azure Speech | Big-cloud speech service | Teams already building on Microsoft's cloud |
| Deepgram | Speech-to-text company with a TTS API | Voice agents that need private deployment |
| ElevenLabs | Broad voice library and agents platform | A very wide choice of voices plus agents |
| Fish Audio | Expressive TTS and voice cloning | Emotion control written into the script |
| Gradium | Newer voice API with an on-device model | Speech in and speech out behind one API |
| Inworld | Realtime TTS for consumer apps | Consumer apps that speak many languages |
| Murf AI | Studio plus voice agent API | One vendor for API, studio and dubbing |
| Rime | Enterprise conversational voice models | Enterprise agents on private infrastructure |
| Sarvam | Indian-language TTS API | Indian languages and code-mixed English |
Grouped by your reason for switching
You need private deployment. Deepgram offers its text to speech in the cloud, in a VPC or on-prem, and Rime also lists VPC and on-prem options, with SOC 2 Type II on its page. Both are aimed at companies whose security or data rules keep audio inside their own walls.
You need more languages or voices. ElevenLabs is the broad option, with a very large voice library and a full agents platform on top. Inworld pitches realtime speech across a very wide range of languages and dialects for consumer apps, and lists voice design next to cloning.
You need regional language strength. Sarvam is built around Indian languages, including the code-mixed English many people speak day to day. If your callers switch between a regional language and English mid-sentence, a model trained for that matters more than any headline speed.
You want expression and control. Fish Audio lets you write emotion tags into the text itself, which is handy for characters, games and storytelling. Gradium is a newer voice API that puts speech in, speech out, cloning and voice design behind one interface, and it's announced an open model that runs on the device.
You want it inside a big cloud or a wider studio. Azure Speech lives inside Microsoft's platform, so it shares security and deployment with everything else you run there. The studio-plus-API option from the speed section above suits teams who also produce voiceovers and dubbing, not only live agents.
One line on each voice API
Alphabetical, and in no particular order of merit.
Azure Speech
Best for: Teams already on Microsoft's cloud who want speech recognition and text to speech sharing billing, security and deployment with their other Azure services.
Deepgram
Best for: Developers who want a text-to-speech API for agents that runs in the cloud, a VPC or on-prem, with a Flux TTS model that reads the whole conversation for context.
ElevenLabs
Best for: Products that need a very wide choice of voices and languages, plus a full voice agents platform with APIs and SDKs; its newest model is pitched as its most emotive.
Fish Audio
Best for: Creators and app makers who want inline emotion tags such as [angry] or [sad] written straight into the text, plus quick voice cloning and an API.
Gradium
Best for: Small teams wanting text to speech, speech to text, cloning and voice design behind a single API, with an open on-device model announced too.
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Inworld
Best for: Consumer apps and agents needing realtime speech, voice cloning and voice design, with the vendor claiming very wide language and dialect coverage.
Murf AI
Best for: Teams who want a voice agent API alongside a studio, dubbing and a voice changer, from a vendor that separates model time from end-to-end delay on its page.
Rime
Best for: Enterprises running real-time conversations that want speech-to-speech as well as text to speech, deployable in a VPC or on-prem (vendor claim).
Sarvam
Best for: Voice products for Indian audiences, where speech has to sound native in regional languages, including English mixed into the same sentence; a voice agents product sits alongside.
My advice before you move your voice stack
Start from the reason you're leaving, not from the longest feature list. If it's data rules, only look at the private deployment group. If it's a language, test that language first and nothing else. If it's voice choice, listen to the voices your product would use, not the demo reel.
Keep your speech layer behind a thin wrapper in your own code, so swapping providers is a configuration change rather than a rewrite. That one habit makes every future move smaller. If you're still shaping the product, my guide to how an AI voice agent works for a small business explains the moving parts, and my list of AI voice generators covers tools for voiceovers rather than live calls.
A one-afternoon latency test
You don't need a lab to compare speed fairly. You need the same conditions for every vendor on your shortlist.
- Pick twenty sentences your product sends, short and long, including names, numbers and any regional words.
- Run them from the server region your app uses, not from your laptop.
- Time the gap from sending text to receiving the first audio chunk, and separately to the last chunk.
- Repeat at a busy hour and a quiet hour, since load changes results.
- Listen to every clip, because a fast voice that mispronounces your product name won't help anyone.
Put the results in one sheet, side by side. Once the numbers come from the same test, the choice usually makes itself.
Do you make a text-to-speech or voice API? Tool makers can ask to be considered for a featured place on this list.
Want your tool on this list? Featured spots and reviews here are paid partnerships. Here's how to partner with me.
Frequently asked questions
What's the best alternative to Cartesia?
There isn't a single right answer. Pick by your reason for leaving, whether that's private deployment, more languages and voices, regional language strength or expressive control, then test the shortlist from your own servers.
Are vendor latency claims comparable?
Not reliably. Some vendors quote model processing time and others quote the full round trip, including network and streaming, so measure time to first audio yourself under the same conditions.
Can I run a text-to-speech model in my own cloud?
Some vendors offer VPC or on-prem deployment for companies with strict data rules. Ask each one what's included, and check the data protection rules that apply to you, since requirements vary by country.
Which voice APIs handle Indian languages well?
Look for a model trained on Indian languages and code-mixed speech, rather than one that lists them among many. One of the nine here is built specifically for that job.
How hard is it to switch voice API providers?
The API calls are usually the easy part. The work is in re-tuning voices, pronunciation of names and products, and timing in your agent, so keep the provider behind a wrapper and switch one flow at a time.
