A speech-to-speech (STS) API takes spoken audio in and returns spoken audio out, so a person can hold a real-time spoken conversation with an AI system, without typing, tapping through screens, or reading a reply off a page.
This label, however, does cause some confusion, as three different products are each sold as a "speech-to-speech API". Because of this, teams end up comparing options that don't do the same job and committing to the wrong one.
This guide sorts out which is which, then works through the decision most engineering teams face: Build your own voice pipeline, adopt a single real-time API, or buy a managed platform that runs the whole thing. Which path fits comes down to your latency, cost, and compliance requirements.
What A Speech-To-Speech API Actually Means
Every speech-to-speech system solves the same problem: Getting spoken audio into an AI and spoken audio back out fast enough that the exchange feels like a real conversation.
The traditional way to do that chains three separate services. Speech-to-text (STT) transcribes what the caller said, a large language model (LLM) works out the response, and text-to-speech (TTS) speaks it back. Each handoff adds delay, and converting speech to text and back again strips out the tone, emphasis, and timing that make a voice sound human.
Modern STS APIs collapse or hide that chain behind a single endpoint. The catch is that "speech-to-speech API" now covers three products that work in fundamentally different ways, and picking the wrong category is the most expensive mistake you can make early.
Conversational Speech-to-Speech
Live, low-latency dialogue where the system listens and replies in real time. OpenAI's Realtime API, Google Gemini Live, Inworld, Hume, and xAI's Grok Voice all sit here. This is the category most buyers actually mean, and latency and model choice are what decide the experience.
Voice Conversion
Recorded audio re-voiced in a different voice while keeping the original emotion and delivery. ElevenLabs' Voice Changer is the best-known example, and the one that causes the most confusion because it was originally branded "speech-to-speech," even though it can’t hold a conversation. So, if you need a real-time agent, this isn't it.
Managed Orchestration Platforms
The full STT-LLM-TTS pipeline wrapped under one managed endpoint, so you consume the finished result instead of assembling the parts. Microsoft's Azure Voice Live is one example; Synthflow is another for telephony-native deployments, running the speech components on owned call infrastructure with the compliance and integration layer built around them.
Build It, Buy An API, Or Buy A Platform
Once you know which category you're in, the real decision is how much of the stack you want to own. There are three paths, and they trade off on the criteria that actually decide the call:
A native API gets you talking quickly, but you're still responsible for everything around the model: Telephony, integrations, retries, and the compliance layer. A managed platform takes on that surrounding work. Framed on outcomes, it drives the workflow to completion, coordinating systems, triggering actions, and following up across voice, SMS, email, and WhatsApp, which is the ground enterprise conversational AI is built to cover.
A managed platform can also embed vertical compliance logic directly, such as the cadence and consent rules a regulated workflow depends on, so that governance lives inside the platform instead of in your codebase.
The Main Speech-to-Speech APIs Compared
Here's how the main real-time providers line up on the four things that decide the build-vs-buy call, with a managed platform included as the separate path it actually is.
What Latency Numbers Actually Measure
Four different numbers all get quoted as "latency," and they measure different things:
- Time-to-first-byte (TTFB): When the first network packet comes back.
- Time-to-first-audio (TTFA): When audio actually starts playing.
- Model-inference latency: How long the model alone takes to respond.
- End-to-end P90 (microphone to speaker): What the caller actually feels.
Only end-to-end P90 describes the conversation, and the practical threshold for it is around 500ms, because human turn-taking runs on a 200–300ms rhythm. Since the LLM is the biggest contributor to total pipeline latency, TTS speed is the last thing worth optimizing, not the first.
This is why headline figures rarely compare. Cartesia's Sonic 3.5 quotes an ~82ms time-to-first-audio; the original Amazon Nova Sonic disclosed a ~1.09s customer-perceived figure. Those describe completely different parts of the pipeline, and only one is the number a caller would notice.
Synthflow runs sub-100ms processing on owned telephony infrastructure, so it isn't paying a middleware tax to a third-party carrier on every call. That's the transport layer, not a model-inference claim, and it targets under 500ms end-to-end.
⚠️Important: Natural turn-taking and clean interruptions come from endpointing (voice activity detection), not model speed. A fast model that can't tell when you've stopped talking still feels wrong.
Why Model Lock-In Matters
The APIs fall into two camps:
- Locked to one model. OpenAI Realtime runs on GPT-Realtime, and Gemini Live runs on Gemini. Switching away later means re-integrating the whole voice layer around a new provider, a real rebuild rather than an API-key change.
- Model-agnostic. Inworld's LLM Router reaches 200+ models, and Hume lets you bring your own, so you can change the reasoning layer without touching the voice stack. The trade is added architectural complexity, weighed against how likely you actually are to switch.
For regulated or cost-sensitive teams, the stakes run higher than convenience. The model you're tied to decides where your data is processed, which certifications apply, and what a migration would cost if pricing or compliance forces your hand later.
Synthflow treats this as model choice with professional guidance rather than open agnosticism: OpenAI is the primary provider through a direct relationship, with the platform team advising on model selection per use case, balancing latency against complexity.
Why Pricing Rarely Compares Cleanly
Every provider bills in a different unit, which is why headline rates sitting side by side tell you almost nothing:
- Per audio token: OpenAI, at roughly $32 / $64 per million tokens in and out.
- Per character: xAI text-to-speech ($15 per million) and ElevenLabs.
- Per minute or per hour: xAI transcription and several realtime voice APIs.
- Tiered by model: Azure Voice Live, across Pro, Standard, Lite, and BYO.
The only way to compare is to normalize to one representative workload, say a five-minute support call, and price each option against it, because no vendor publishes a per-conversation figure. Token-billed realtime APIs make that harder still: Cost climbs with every turn as the conversation buffer grows, a charge that stays invisible until you run at volume.
Free tiers are worth using for a prototype, but they rarely carry the rate limits, uptime, or compliance a production deployment needs. Treat one as a sandbox, not a plan.
Synthflow is focused on outcome-based pricing, where the bill tracks the work an agent completes, and today it bills bundled usage: one cost covering telephony, orchestration, and the model layer, so there are no separate line items to assemble and reconcile yourself.
Compliance, Data Residency, And Audit Trails
In a regulated industry, compliance can decide the whole thing. It's worth checking each option against the certifications that matter – SOC 2, HIPAA, GDPR, and ISO 27001, and where the vendor is allowed to process and store your data. The comparison table above lays this out, and the gaps between vendors are bigger than most buyers expect.
It also decides build versus buy, because in a regulated field compliance is something the platform has to handle on every single call:
- How often you can contact someone, and at what times of day.
- Whether that person has agreed to be contacted.
- A complete, reviewable record of every conversation.
- Clear rules for when to hand a call over to a human.
A raw speech-to-speech API gives you the voice and none of that governance layer, so your engineering team builds and owns all of it.
Synthflow is certified for SOC 2, HIPAA, GDPR, PCI DSS V4.0.1, and ISO 27001:2022, with regional data tenants across the US and EU. The more useful point is what it does with that foundation.
Its consumer-lending product, Vector, shows the argument in practice: It's built to run borrower conversations across the lifecycle, from application through servicing, renewals, and collections, with the lending compliance logic sitting inside the platform by design.
Regulated-outbound reality check: Collections and renewal outreach are outbound and heavily regulated (US state AI-calling rules, FDCPA and Reg F, and their German equivalents), so that built-in cadence, consent, and audit layer is exactly where a managed platform earns its place.
That vertical orchestration is the work a managed platform absorbs and a raw API leaves sitting on your team. It's the clearest case where "buy a platform" wins over "buy an API."
Choosing The Right Path For Your Team
The right choice comes down to how much of the work you want to own versus how much you want handled for you:
If the managed platform is where you land, talk to Synthflow's team about enterprise deployment. For consumer-lending teams specifically, Vector is the lending-native version of that same path.
Common Questions About Speech-To-Speech APIs
Is There A Free Speech-To-Speech API I Can Use For Prototyping?
Several providers offer free or trial tiers that are fine for a proof of concept. They rarely carry the rate limits, uptime, or compliance a production deployment needs, so treat a free tier as a sandbox and budget for the build-vs-buy decision before you ship.
Should I Connect A Speech-to-Speech API Over Webrtc Or Websockets?
WebSocket is the usual choice for server-side voice agents, WebRTC suits browser-based clients where packet loss matters, and SIP is the protocol for telephony and PSTN. Choose by where the audio originates, not by which protocol is newest.
How Do I Build A Voice-to-Voice App With The Gemini Live API?
Gemini Live streams audio in both directions and is tied to Gemini models, so the build is workable, but the model is fixed. For a production agent, weigh that lock-in against the flexibility you'll want later.
How Does ElevenLabs' Speech-to-Speech Work For Voice Cloning And Emotion?
That's ElevenLabs' Voice Changer, which re-voices recorded audio while keeping the original emotion. It's voice conversion, not a conversational agent, so don't reach for it expecting real-time dialogue.
What Languages And Accents Do Speech-to-Speech APIs Support?
Coverage varies widely by provider and thins out for lower-resource languages and regional accents. Verify the specific languages and accents you need rather than trusting a headline "100+ languages" claim.
