Weam

The reason to look at Weam is the deployment model, and it is worth being clear-eyed about what that buys and costs. What it buys: control of the data path. Every chat, document, and agent run happens inside your Docker or Kubernetes environment, with role-based access control and multi-LLM policies on top, so governance questions get answered by your own infrastructure rather than a vendor's trust center. Cost scales with model usage instead of headcount, which matters once a team passes a handful of seats. And because the code is open, an agency can white-label the whole workspace and hand clients a product it presents as its own. What it costs: operations. Weam expects Docker and basic server administration — the docs are explicit that self-hosting requires both. That is a real line item in engineering hours, and it is the first thing to price honestly before you commit. Where it is unusually complete for an open-source workspace: the app layer. Rather than leaving you a chat UI and a framework, Weam ships a lead-capturing chatbot, a voice hire assistant that runs initial candidate screening, a copy generator, a script-to-video tool, and an AI Editor that edits documents without disrupting layout. It also ships the collaboration primitives teams actually reuse — forking a conversation to pass an insight to a colleague, Brains and Workspaces as shared knowledge containers, and usage reports that show whether anyone is using the thing. The multi-model story is genuinely broad rather than performative: GPT-5, Claude, Gemini, DeepSeek, Perplexity, and Sarvam AI's sarvam-m (added February 2026). Switching mid-conversation means you are not locked to one provider's roadmap or outage window. Honest weaknesses. Support is community-first — Discord and GitHub are the primary channels, with official and Enterprise support tiers above them, so a team that needs a guaranteed response time should price the Enterprise Edition. The public documentation is strongest on setup, features, and API reference and thinner on the per-model usage economics you will want when budgeting; treat model spend as something you measure after deployment rather than forecast from the site. And this is a platform, not a toaster: a solo user who wants one chatbot on a phone is not the audience. The verdict follows from that split. If owning the AI stack is strategic — compliance, client-facing products, agency margin — Weam is one of the more complete open-source starting points available. If owning infrastructure is a chore you would rather outsource, choose a managed service and spend the difference on something else.