Dify vs LLM Harbor
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Containerized LLM toolkit: manage backends, APIs, and frontends via one CLI |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | OpenAI API, ChatGPT |
| GitHub stars | 150k | 3.1k |
| Language | Python | Docker |
| License | Apache-2.0 | Apache-2.0 |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | Docker Compose Kubernetes | Docker Docker Compose |
| Managed hosting | ||
| Last updated | 5 days ago | 12 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
LLM Harbor
- Niche tool primarily aimed at power users; limited documentation for beginners
- No built-in UI beyond what the composed services provide
- Community is small; issues may go unanswered compared to larger projects
- Not suitable for production multi-user deployments without significant additional hardening
Bottom line
Both are a similar lift to self-host; choose Dify for the larger community and ecosystem. Dify has seen more recent development. 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
LLM Harbor
Containerized LLM toolkit: manage backends, APIs, and frontends via one CLI