Dify vs Onyx Community Edition
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Enterprise-grade AI chat with 40+ connectors, agents, and deep research |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | ChatGPT, OpenAI API |
| GitHub stars | 150k | 31k |
| Language | Python | Docker |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 3/5 Moderate | 4/5 Involved |
| Deploy options | Docker Compose Kubernetes | Docker Docker Compose Kubernetes |
| Managed hosting | ||
| Last updated | 6 days ago | 6 days ago |
| 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
Onyx Community Edition
- Self-hosted stack is resource-heavy (Postgres + Vespa + Redis + multiple services)
- Some enterprise connectors and features are gated behind the paid cloud tier
- Initial connector sync for large knowledge bases can take hours
- SAML/SSO configuration requires manual setup and is not well-documented for self-hosters
Bottom line
Choose Dify 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.
Dify
Open-source LLM app development platform with visual workflow, RAG, and agent builder
Onyx Community Edition
Enterprise-grade AI chat with 40+ connectors, agents, and deep research