Dify vs Ollama
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Run large language models locally with a simple CLI and REST API |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | OpenAI API, ChatGPT |
| GitHub stars | 58k | 175k |
| Language | Python | Docker |
| License | Apache-2.0 | MIT |
| Self-host difficulty | 3/5 Moderate | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes | Docker Manual |
| Managed hosting | ||
| Last updated | 1 month ago | 5 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
Ollama
- No built-in chat UI; requires a separate front-end like Open-WebUI
- Fine-tuning and model training are not supported; inference only
- Multi-GPU distributed inference is limited compared to commercial inference APIs
- No built-in authentication, rate-limiting, or multi-tenant access control
Bottom line
Choose Ollama if you want the lower-effort setup; choose Ollama for the larger community and ecosystem. Ollama 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