Compare · Waterr vs Gemini Live
Waterr vs Gemini Live API
Google’s Gemini Live API is a realtime streaming primitive: a stateful WebSocket that takes continuous audio (and video frames) and returns natural spoken responses, priced per token. It ships no meeting room, no personas, no recordings, no transcripts as deliverables, and no evaluation — those are layers you build. Waterr is that finished layer: scenarios with personas and goals, live video meetings joined by link, and transcript, recording, and goal scores returned by the API. Engine versus vehicle, and the docs of both make the split unambiguous.
vs
Gemini LiveAt a glance
Waterr and Gemini Live, side by side
| Waterr | Gemini Live | |
|---|---|---|
| What it is | AI meeting platform — full lifecycle | Realtime streaming model API over WebSocket |
| Meeting surface | Live video meeting via join link | None — raw PCM audio in/out; you build transport and UX |
| Session length | Configurable per scenario (default 35 min) | 15 min audio / 2 min audio+video without compression; reconnect plumbing past ~10 min per connection |
| Personas & scenarios | Yes — stored objects with scripts, goals, avatars | No — system instructions and voice presets |
| Goal scoring | Yes — per-session scores and written feedback | No — no evaluation of any kind |
| Recording & transcript storage | Yes — durable artifacts behind one GET | No — nothing persisted; live transcription fragments you assemble |
| Tool calling | Signed webhooks or client-side, managed lifecycle | Function calling with manual response handling in client code |
| Vision | Persona reads camera and screen share in real time | Image frames at up to 1 FPS as input |
| Pricing shape | Platform pricing per meeting usage | Per-token audio, video, and text I/O |
| Best for | Evaluated meetings without platform engineering | Developers building their own realtime experience on Google’s stack |
Claims verified against ai.google.dev Live API docs (overview, capabilities, session management, tools, pricing) as of July 2026. Live API models are preview-status and may change.
The honest split
Which one should you pick?
Choose Waterr when…
- You need the meeting, its recording, its transcript, and its scores — as deliverables, not streams.
- Participants should join a video call from a link, with consent, camera, and screen-share controls handled.
- Sessions must run a full interview or roleplay length without WebSocket reconnect engineering.
- Your system should be notified by signed webhooks when meetings end and analyses complete.
Choose Gemini Live when…
- You are building your own realtime experience and want Google’s model as the engine.
- Your interaction is short, embedded, and conversational — not a structured meeting.
- Per-token pricing fits your usage pattern better than platform pricing.
- You are already deep in the Google AI stack and its partner transport integrations.
The details
What the Live API actually ships
Gemini Live is a well-built streaming primitive: continuous audio in, natural speech out, automatic voice-activity detection, interruption handling, and function calling — over a stateful WebSocket, with ephemeral tokens for client-side auth. Google’s own docs are clear about the altitude: inputs are raw 16 kHz PCM and image frames at up to one per second; outputs are 24 kHz audio. Everything around that loop is yours to build.
The session model shows it most clearly. Without context compression, audio-only sessions cap at 15 minutes and audio-plus-video at 2 — and individual connections live about 10 minutes, so anything longer means resumption tokens and reconnect handling. Nothing is persisted server-side: no recordings, no stored transcripts, no post-session artifacts. Transcription arrives as streamed fragments you stitch together yourself. Google even points you at partner integrations — LiveKit, Pipecat, Voximplant — for the transport layer, which tells you where the API ends.
The meeting layer, itemized
To turn that primitive into an interview or a sales roleplay, you would build: a client audio pipeline, a backend minting ephemeral tokens, reconnect and session-longevity plumbing, the manual tool-execution loop, recording capture and storage, transcript assembly, and an evaluation pipeline with its own LLM passes. On Waterr each of those is either an API object or a platform behavior: scenarios carry personas and goals, meetings produce join links, sessions default to 35 minutes and are configurable, tools execute over signed webhooks with filler behavior while they run, and session.analysis_complete arrives on your webhook with scores attached.
Same conclusion as the Realtime comparison
Like OpenAI’s Realtime API, Gemini Live is an engine choice for teams building their own platform — and a strong one, especially inside Google’s ecosystem. The build-versus-buy question is not which model API is best; it is whether you should be building the meeting layer at all. If meetings are a feature of your product rather than the product itself, buying the finished layer is usually the better trade.
FAQ
Common questions
No. It is a WebSocket streaming API — raw PCM audio in and out, image frames as input. Google’s docs point to partner integrations like LiveKit and Pipecat for transport. A joinable video meeting with participant UX is a layer you build, or the layer Waterr provides.
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.