Dify vs text-generation-webui
| Tagline | Open-source LLM app development platform with visual workflow, RAG, and agent builder | Feature-rich web UI for running large language models locally with multiple backends |
| Category | AI & LLM Tools | AI & LLM Tools |
| Replaces | ChatGPT, OpenAI API | ChatGPT, OpenAI API |
| GitHub stars | 152k | 48k |
| Language | Python | Python |
| License | Apache-2.0 | AGPL-3.0 |
| Self-host difficulty | 3/5 Moderate | 2/5 Easy |
| Deploy options | Docker Compose Kubernetes | Docker Manual |
| Managed hosting | ||
| Last updated | today | 2 months 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
text-generation-webui
- No built-in team/multi-user auth; single-user by default
- Model management UI is less polished than commercial offerings
- No native document RAG pipeline (requires extensions)
Bottom line
Choose text-generation-webui if you want the lower-effort setup; 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
text-generation-webui
Feature-rich web UI for running large language models locally with multiple backends