Both respect your infrastructure — Dify and Hedy can each run on your own machines. The difference: Dify is a workshop for building AI apps; Hedy is the employee who walks out of the box already trained.
Hedy and Dify both self-host, but they answer different questions. Dify is an LLM app development platform: a canvas where your engineers build, wire, and maintain chatbots and RAG pipelines. Hedy is a finished AI employee that shows up in Feishu/Lark and Slack, does the work, and enforces its own governance. Dify hands you a builder; Hedy hands you a hire. If you want one worker that replaces a team's output, not a toolkit to assemble one, choose Hedy.
| Hedy | Dify | |
|---|---|---|
| Category | Finished AI employee | LLM app development platform |
| Time to value | Minutes: deploy, connect, delegate | You design, build and maintain each app |
| Self-hosting | Yes — Compose / Helm / air-gap | Yes — open-source core |
| Lives in chat | Feishu / Lark / Slack as a colleague | Apps get embedded or exposed as APIs |
| Autonomy | Scheduled jobs, initiative, follow-ups | What your built flows define |
| Governance | Audit, approvals, allowlists, conduct tests included | Assembled per app by your team |
Based on public information as of July 2026. Corrections welcome: hello@hedy.one
Dify gives you a canvas. You get nodes, prompt orchestration, RAG retrieval, and an API to call — then your engineers design the flows, tune the prompts, wire the tools, and own the result. That is real power if building AI apps is your goal. But a canvas is not a coworker. Nobody on the canvas checks who is allowed to see a document, refuses a risky write, or leaves an audit trail you can defend. That work is yours to build and keep building.
Hedy is the opposite starting point: a shipped employee, not a builder. It arrives as an AI employee that lives in Feishu/Lark as a first-class citizen (Slack too), reads and answers inside the channels your team already uses, and produces the output you'd otherwise staff a small team for. The domain says it plainly — one Hedy is meant to cover a team's worth of work, not add another seat to a chat tool. You configure a persona, skills, and cron schedules; you do not assemble the agent from primitives.
The deepest difference is governance. On an app platform, safety is whatever your builders remember to add. In Hedy, governance is the product: append-only audit, tiered approval (L0/L1/L2), write operations default-deny with an explicit allowlist, retrieval scoped by the asker's ACL, red-line behavior tests, and an evidence gate that forces every answer to cite its sources verbatim. These are enforced in config and code — not written into a prompt and hoped for.
Both run on your infrastructure, and both let you bring your own model keys — including local vLLM or Ollama. Hedy adds an operational floor: ~30-minute Docker Compose deploy, no sub-processors, and an offline Ed25519 license so nothing has to phone home. So the real choice is scope of ownership. Pick Dify when you have engineers who want to build and own LLM applications. Pick Hedy when you want to hire the finished worker and govern it, not build one from parts.