ExLlama vs RAGFlow
A side-by-side comparison of two self-hosted self-hosted ai options — licensing, setup difficulty, resource needs, and what each one replaces.
Not the right match-up?
ExLlama
Memory-efficient inference library for quantized Llama models
VS
RAGFlow
RAG engine with deep document understanding
| Feature | ExLlama | RAGFlow |
|---|---|---|
| Category | Self-Hosted AI | Self-Hosted AI |
| License | MIT | Apache-2.0 |
| Language | Python | Go |
| Setup difficulty | Hard | Medium |
| Min. RAM | 8,192 MB | 8,192 MB |
| Deployment | source | docker, kubernetes |
| GitHub stars | ★ 2,937 | ★ 87,057 |
| First released | 2023 | 2024 |
| Replaces | OpenAI API | NotebookLM |
Why pick each one
Choose ExLlama if…
- Released under the MIT license
- Active community (2.9k GitHub stars)
- Written in Python
Choose RAGFlow if…
- Released under the Apache-2.0 license
- First-class Docker support for quick deployment
- Kubernetes-ready with Helm charts available
- Mature project with 87.1k GitHub stars
Frequently asked questions
Is ExLlama or RAGFlow better?
RAGFlow is the stronger all-round pick: it has both the larger community and the simpler medium setup. Consider ExLlama if its specific feature set fits your needs better.
Are ExLlama and RAGFlow free and open-source?
Yes. ExLlama is licensed under MIT and RAGFlow under Apache-2.0. Both can be self-hosted at no software cost.
Can I run ExLlama and RAGFlow with Docker?
ExLlama: check the project docs for container support. RAGFlow: yes.