Compare · Waterr vs LiveKit
Waterr vs LiveKit
LiveKit is realtime infrastructure: an open-source WebRTC stack plus an agents framework for building voice and video AI in Python or Node — the infrastructure that, per LiveKit’s own homepage, powers ChatGPT’s Advanced Voice. Waterr is the meeting layer above that altitude: a hosted API where a scenario defines the persona and goals, participants join by link, and the API returns transcript, recording, and goal scores. With LiveKit you build and operate the agent; with Waterr you configure the meeting and consume the result.
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At a glance
Waterr and LiveKit, side by side
| Waterr | LiveKit | |
|---|---|---|
| What it is | Hosted AI meeting API — scenario in, scored meeting out | Open-source realtime stack + agents framework + cloud |
| What you write | API calls and webhooks — no agent code | Your agent, in Python or Node.js |
| Persona & scenario management | Yes — first-class API objects | No — you encode personas in system prompts and code |
| Goal scoring of real meetings | Yes — every session scored against your goals | No — evals exist, but they test your agent code in CI, text-only |
| Transcript + recording as deliverables | Yes — API deliverables, plus scores | Primitives exist (egress, insights); you assemble the pipeline |
| Tool calling | Webhook or client-side, defined once per account | Yes — functions you write inside your agent code |
| Telephony / SIP | No — video meetings via join link | Yes — full SIP support, phone numbers, warm transfers |
| Self-hosting | No — hosted platform | Yes — open source, self-hostable |
| Pricing shape | Platform pricing per meeting usage | Infra metering: session minutes, inference, transfer, transcode |
| Best for | Shipping evaluated AI meetings this week | Teams building a custom realtime agent product on owned infra |
Competitor claims verified against livekit.com and docs.livekit.io as of July 2026, including their agents framework, evals, and pricing pages.
The honest split
Which one should you pick?
Choose Waterr when…
- You want meetings running this week without writing or operating agent code.
- You need each session scored against goals — interviews, roleplay, screening — with results delivered by API and webhook.
- Personas, scenarios, avatars, and meeting scripts should be configuration, not code you maintain.
- Transcript, scores, and recording should arrive as API deliverables — no egress pipelines to assemble.
Choose LiveKit when…
- You are building a custom realtime product and want to own every layer, possibly self-hosted.
- You need telephony — SIP trunks, phone numbers, warm transfers to humans.
- Your team is comfortable writing and operating Python or Node agents and wants that control.
- You need infrastructure-level scale economics across millions of call minutes.
The details
Infrastructure vs product
LiveKit deserves its reputation. The open-source WebRTC stack is excellent, the agents framework handles turn detection and interruptions well, and LiveKit’s homepage credits ChatGPT’s Advanced Voice as built on LiveKit Cloud. If you are building a realtime AI product and want to own the stack, it is a great choice — that is why it sits in our own build-vs-buy analysis as the serious DIY path.
But LiveKit sells infrastructure primitives, and the distance between primitives and a working evaluated meeting is real engineering: you write the agent, encode the persona in prompts, wire STT-LLM-TTS, build the join experience, set up egress for recordings, and then build the entire evaluation layer — because scoring a participant against goals is not something the framework does.
The evals nuance most comparisons miss
LiveKit Agents does have a testing and evals system — pytest and Vitest helpers with LLM-as-judge assertions. Read the docs closely, though: it is designed for text-only behavioral tests of your agent code, run at development time. It answers "does my agent behave correctly in CI?"
Waterr’s goals answer a different question: "how did this participant perform in this real meeting?" Every session is scored against per-scenario goals — communication clarity, objection handling, whatever you define — producing scores and written feedback delivered through the API. One tests software; the other evaluates people and outcomes. Most teams comparing the two actually need the second one.
Cost shape: metering vs meetings
LiveKit Cloud meters like infrastructure: agent session minutes, concurrent session caps, inference per model-minute, data transfer, transcode minutes for recording. Fair pricing for infrastructure — but you are also paying the engineering time to build and operate everything above it. Waterr prices the finished meeting. Which is cheaper depends entirely on whether the meeting layer is your product or just something your product needs.
FAQ
Common questions
No. Waterr runs live video meeting sessions participants join via a link, with its own hosted meeting engine. LiveKit is one of the strongest options if you choose to build a custom agent stack yourself instead.
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.