Compare · Waterr vs OpenAI Realtime
Waterr vs OpenAI Realtime API
The OpenAI Realtime API is a speech-to-speech model interface: low-latency voice in, voice out, with native function calling — a model primitive, priced per token. Waterr is an AI meeting platform: scenarios define personas and goals, participants join live video meetings by link, and the API returns transcript, recording, and goal scores. The Realtime API is a strong engine choice if you are building your own meeting platform. Waterr is the platform, so you do not have to.
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At a glance
Waterr and OpenAI Realtime, side by side
| Waterr | OpenAI Realtime | |
|---|---|---|
| What it is | AI meeting platform — full lifecycle | Speech-to-speech model API (gpt-realtime family) |
| Meeting surface | Live video meeting via join link | None — you build the room, client, and session plumbing |
| Session length | Configurable per scenario (default 35 min) | Hard cap: 60 minutes per Realtime session |
| Personas & scenarios | Yes — stored API objects with scripts and goals | No — system prompts you manage yourself |
| Goal scoring | Yes — every session scored, feedback written | No — build your own evaluation pipeline |
| Recording & transcript storage | Yes — durable artifacts behind one GET | No — capture and store streams yourself |
| Tool calling | Yes — signed webhooks or client-side, managed lifecycle | Yes — native function calling, you run the loop |
| Vision | Yes — persona reads camera and screen share | Model-dependent; no meeting-video pipeline provided |
| Webhooks & events | Yes — signed, retried, full catalog | No session-event webhook system for calls |
| Pricing shape | Platform pricing per meeting usage | Per-token audio and text I/O |
| Best for | Teams that need evaluated meetings, not model plumbing | Teams building their own voice platform who want this model |
Claims verified against platform.openai.com/docs as of July 2026. Model capabilities and pricing change frequently — check current docs.
The honest split
Which one should you pick?
Choose Waterr when…
- You need a meeting, not a model — join links, personas, goals, and results without building the platform.
- Sessions must be scored: interviews, screening, roleplay, discovery calls with defined goals.
- You want recordings, transcripts, and analytics as durable artifacts, not streams to capture.
- Webhooks should tell your system when meetings end and analyses complete.
Choose OpenAI Realtime when…
- You are building your own voice or meeting platform and choosing the engine underneath it.
- You need raw model access with maximum control over the conversation loop.
- Voice interaction is embedded deep inside your own product surface, not a standalone meeting.
The details
A model is not a meeting
The Realtime API is genuinely impressive as a model interface — low-latency speech-to-speech with function calling, over WebRTC or WebSocket. But it has no opinion about anything above the audio loop. There is no meeting room, no join link, no participant experience, no recording storage, no transcript deliverable, no evaluation. OpenAI is selling you the engine; the vehicle is your job.
That job is bigger than the demo suggests: client audio capture and playback, session and reconnect handling, the tool-execution loop, recording capture and storage, transcript assembly, and — if outcomes matter — an entire evaluation pipeline with its own LLM calls. Our build-vs-buy essay walks through what that actually costs in engineering months.
What the platform layer adds
On Waterr, the persona is a stored object with a name, background, and demeanor; the meeting script drives behavior; goals carry scoring instructions the participant never sees. A session produces a recording, a transcript, goal scores, and speech analytics — retrievable by API, delivered by signed webhooks. Tools are defined once and attached per scenario, executed over your webhook with HMAC signing and configurable filler behavior while they run. None of that is model work — and all of it is the difference between a voice loop and a meeting your ops team can act on.
Complement, not competitor
The honest framing: if you are building a voice platform, the Realtime API is one of the best engines you can pick, and platforms like ours are the kind of thing you would be building. If you are not in the business of building meeting platforms, buying the finished layer and spending your engineering on your actual product is usually the better trade.
FAQ
Common questions
Not by itself. The Realtime API provides the speech-to-speech model loop; a meeting requires transport and client UX, session management, recording and transcript storage, and any evaluation — all layers you build. Waterr ships those layers as the product.
Run your first AI meeting today.
One POST creates the scenario. One link runs the meeting. The API hands back the transcript, goal scores, and recording.