Agenta vs Dify
| Tagline | LLMOps platform for prompt management, evaluation, and LLM observability | Open-source LLM app development platform with visual workflow, RAG, and agent builder |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | OpenAI API, ChatGPT | ChatGPT, OpenAI API |
| GitHub stars | 4.4k | 150k |
| Language | Docker | Python |
| License | MIT | Apache-2.0 |
| Self-host difficulty | 3/5 Moderate | 3/5 Moderate |
| Deploy options | Docker Docker Compose | 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.
Agenta
- Observability depth is shallower than dedicated tools like LangSmith or Arize for large-scale production
- No built-in model fine-tuning or training pipelines
- Evaluation framework requires custom code for complex domain-specific metrics
- Self-hosted deployment documentation is less polished than the cloud onboarding
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
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.
Dify
Open-source LLM app development platform with visual workflow, RAG, and agent builder