Dify vs Jan
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Offline-first, privacy-focused desktop app to run LLMs locally on any hardware |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | ChatGPT, OpenAI API |
| GitHub stars | 152k | 44k |
| Language | Python | TypeScript |
| License | Apache-2.0 | AGPL-3.0 |
| Self-host difficulty | 3/5 Moderate | 1/5 Effortless |
| Deploy options | Docker Compose Kubernetes | Manual |
| Managed hosting | ||
| Last updated | today | today |
| View repo | View repo |
Where each falls short
The honest trade-offs — what you give up with each, versus the proprietary tools they replace.
Dify
- Self-hosted community edition lacks SSO and audit logs (cloud-only)
- Requires multiple services (Postgres, Redis, Weaviate/Qdrant) increasing ops burden
- Plugin marketplace is smaller than commercial AI platforms
Jan
- Desktop-only; no headless server deployment mode
- Multi-user collaboration not supported
- Limited to llama.cpp-compatible model formats
Bottom line
Choose Jan if you want the lower-effort setup; choose Dify for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.