Dify vs Langfuse
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Open-source LLM observability and evaluation platform for tracing AI application calls |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | OpenAI API |
| GitHub stars | 152k | 33k |
| Language | Python | TypeScript |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | Docker Compose Kubernetes | Docker Compose Kubernetes |
| 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
Langfuse
- Some advanced evaluation and annotation features are cloud-only
- ClickHouse dependency adds significant infrastructure overhead
- No built-in alerting or on-call integrations
Bottom line
Both are a similar lift to self-host; choose Dify for the larger community and ecosystem. Open each guide below for deploy steps and the full feature gap.