Open source alternative to Recall.ai, Otter.ai, Fireflies.ai, Fathom and Notta
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Host your own meeting bot API for Google Meet, Teams, Zoom, and Jitsi with real-time speaker-attributed transcription and Apache-2.0 licensing.
Vexa's REST and WebSocket API let you send a bot, receive a real-time transcript stream, and query accumulated meeting history in one self-hosted deployment. The MCP server means AI coding agents can access meeting context without custom connectors.
Your product does not need live meeting access and can work from pre-existing transcript files. A simpler speech-to-text API with offline audio processing is a lighter path in that case.
Legal, finance, and healthcare teams whose compliance policies prohibit sending meeting audio to third-party clouds can run the full Vexa stack inside their own infrastructure. Air-gap mode requires no outbound connections once deployed, including for transcription when a local GPU runs the STT service.
Your organization lacks an infrastructure team to maintain a Docker Compose or Kubernetes deployment. Hosted transcription services handle infrastructure on your behalf, at the cost of data residency.
Vexa's agentic runtime compiles meeting transcripts into a Markdown knowledge base that coding agents like Claude Code can read and write. Teams can automate post-meeting briefings, action item tracking, and knowledge base updates without building the capture pipeline themselves.
Your team only needs meeting summaries. Tools with simpler setup may be faster for summary-only use cases where a queryable knowledge base is not required.
The Helm chart in `deploy/helm` deploys the full control plane with `RUNTIME_BACKEND=k8s`, where each bot and agent runs as a separate Kubernetes Pod. Capacity scales with the cluster scheduler rather than a fixed compute allocation.
Your team is not yet familiar with Kubernetes operations. Start with the Docker Compose path (`make all`) and migrate to Kubernetes after validating the stack on Docker.
Most meeting transcription tools work the same way: a bot joins your call, audio travels to a third-party cloud, and you receive a transcript through their web app or API. Your conversations are processed on someone else's infrastructure, stored in their database, and subject to their pricing, privacy policy, and uptime decisions.
For teams in regulated industries or organizations handling confidential client discussions, sending meeting audio to a commercial platform is not acceptable. For developers building products on top of meeting data, depending on a proprietary API with per-seat or per-minute pricing creates costs that scale against you. The transcription market is also fragmented: tools that handle capture well are separate from tools that handle downstream knowledge work, leaving teams to stitch together multiple paid subscriptions.
Bot joins live meetings and streams speaker-attributed transcripts as they happen over a WebSocket API. Each transcript segment includes the speaker name, allowing downstream systems to process spoken content by participant without additional post-processing.
Supports Google Meet, Microsoft Teams, Zoom, and Jitsi from a single API. Send a bot with a `POST /bots` call, specifying the platform and the meeting ID from the join URL. All four platforms use the same request format.
Ships a Model Context Protocol (MCP) server so AI coding agents (Claude Code, Codex, and others) can query your meeting history directly, without a custom integration layer. This connects meeting transcription data to the agent's tool-call context natively.
Compiles meeting transcripts into Markdown files stored in a git repository using the Open Knowledge Format. Sandboxed agents run in isolated, ephemeral containers on Docker or Kubernetes to read and write that repository. This layer is self-hosted only.
Full-stack Docker Compose deployment with `make all`, or Kubernetes via the Helm chart in `deploy/helm`. The same `runtime.v1` workload runs bots and agents identically across Docker, process, and Kubernetes backends with no code changes.
Every component runs inside your network, including an optional self-hosted speech-to-text service based on faster-whisper. No meeting data leaves your infrastructure, which satisfies the zero-egress posture regulated environments require.
Fireflies.ai is a hosted SaaS meeting assistant that deploys bots to join calls, produces transcripts, and surfaces action items through a web dashboard. Both products join live meetings, but Fireflies routes all audio through its cloud.
| Feature | Vexa | Fireflies.ai |
|---|---|---|
| License | Apache-2.0 (open source) | Proprietary SaaS |
| Self-hosting | Yes | No |
| Data residency | Your infrastructure | Fireflies servers |
| Platforms | Meet, Teams, Zoom, Jitsi | Meet, Teams, Zoom, Webex |
Vexa is the better choice when data residency is a hard requirement, when you are building on top of meeting data via API, or when subscription costs at scale are a concern. Fireflies.ai is worth considering when your team wants a turnkey dashboard with no infrastructure to manage.
Otter.ai targets consumer and small-team transcription with a simple web and mobile interface. Vexa targets developers who need programmatic API access to meeting data.
| Feature | Vexa | Otter.ai |
|---|---|---|
| License | Apache-2.0 (open source) | Proprietary SaaS |
| API access | Full REST and WebSocket | Limited |
| Self-hosting | Yes | No |
| Platforms | Meet, Teams, Zoom, Jitsi | Meet, Teams, Zoom |
Vexa is the better fit for technical teams integrating transcription into a product. Otter.ai is simpler for non-technical users who want transcription without infrastructure setup and are comfortable with their meeting data on Otter's servers.
Recall.ai is a commercial meeting bot API for developers: the closest competitive comparison to Vexa. Both are API-first products targeting developers who need programmatic meeting access.
| Feature | Vexa | Recall.ai |
|---|---|---|
| License | Apache-2.0 (open source) | Proprietary API |
| Self-hosting | Yes | No |
| Data residency | Your infrastructure | Recall servers |
| Agentic knowledge layer | Yes (self-hosted) | No |
Vexa wins when self-hosting is required for compliance or when usage-based API costs at scale are a concern. Recall.ai is reasonable when you need the meeting bot capability without running your own infrastructure and can accept routing meeting data through a third party.
Self-hosting runs via Docker Compose on a Linux host with at least 8 vCPUs and 16 GB RAM.
```bash
git clone https://github.com/Vexa-ai/vexa.git && cd vexa
make all
make bot
```The GPU is required only if you self-host the speech-to-text service for a fully air-gapped deployment. By default, make all runs without a GPU and connects to a hosted transcription token from vexa.ai. To remove that external dependency, stand up the faster-whisper-based STT service from deploy/transcription on a GPU host and point the .env file at it.
Vexa supports Google Meet, Microsoft Teams, Zoom, and Jitsi. You send a bot by posting the platform name and the meeting ID from the join URL to POST /bots. All four platforms use the same API call format, and the platform value is google_meet, teams, zoom, or jitsi.
Vexa is not a hosted service like Fireflies.ai or Otter.ai. It is a self-hosted API you deploy on your own infrastructure. You get the same core capability (bots that join meetings and transcribe in real time), but transcripts and audio never leave your servers, and there is no per-seat or per-minute fee.
Clone the repository, run make all on a Linux host with at least 8 vCPUs and 16 GB RAM, then make bot to build the meeting bot image. When make all finishes it prints your API key and the local URLs for the Terminal UI and the API gateway. Use make lite on smaller machines for initial evaluation.
Beyond transcription, Vexa includes an optional runtime that compiles meeting transcripts into a Markdown knowledge base stored in a git repository. Sandboxed AI agents read and write that repository in isolated, ephemeral containers. This layer is self-hosted only and requires the full Docker Compose or Kubernetes deployment.
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