Self-hosted open-source RAG framework

If your priority is on-premise + open source + easy self-hosting, my top recommendation is RAGFlow.

My ranking

Framework	Self-hosting	Ease	RAG capability	Best fit
RAGFlow	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Enterprise document RAG
Dify	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	RAG + workflows/apps
LlamaIndex	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Developer-built RAG
Haystack	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	Production/custom pipelines
LangChain	⭐⭐⭐⭐⭐	⭐⭐	⭐⭐⭐⭐	Complex agents/orchestration

🥇 RAGFlow — my choice for your use case

RAGFlow documentation

RAGFlow is particularly attractive if you're building an internal enterprise knowledge system rather than merely embedding a RAG library into an application.

It gives you:

• Docker-based deployment
• Web UI for uploading/managing knowledge bases
• Document parsing and chunking
• Hybrid retrieval
• Reranking
• Citations/grounding
• Support for complex documents
• APIs for integrating with your applications
• Ability to use local LLMs through Ollama, Xinference, or LocalAIragflow.cc

The important distinction is that RAGFlow is closer to a complete RAG platform, whereas LlamaIndex and Haystack are primarily developer frameworks.

The current RAGFlow quickstart lists roughly 4 CPU cores, 16 GB RAM and 50 GB disk as a starting point, with Docker Compose deployment.ragflow.cc

🥈 Dify — easiest if you want an internal AI platform

Dify GitHub repository

I'd choose Dify if your requirements are broader than RAG:

"We want an internal ChatGPT-like platform where employees can build/use RAG applications."

Dify combines RAG, workflows, agents, model management, APIs and observability. Its Docker Compose deployment is straightforward, and its documented minimum is only 2 CPU cores / 4 GiB RAM for the basic server.github.com

The tradeoff is that Dify is an LLM application platform, not a RAG engine specialized around document retrieval.

🥉 LlamaIndex — best if your engineering team wants control

LlamaIndex documentation

I'd pick LlamaIndex if you're building your own backend/API and want to control the architecture.

It gives you building blocks for:

Documents
   ↓
Parsing / chunking
   ↓
Embeddings
   ↓
Vector / hybrid index
   ↓
Retriever
   ↓
Reranker
   ↓
LLM
   ↓
Your application/API

LlamaIndex has a particularly strong data/RAG ecosystem and supports local-model workflows.github.com

The downside: you have to assemble and operate more of the system yourself.

What about Haystack?

Haystack documentation

Haystack is my choice over LlamaIndex when the team values explicit, testable production pipelines. Its component/pipeline architecture gives you granular control over retrieval, routing, generation and evaluation.docs.haystack.deepset.ai

What I'd deploy on-prem

For a typical enterprise deployment, I'd seriously consider:

                   ┌─────────────────┐
                   │  Internal Users │
                   └────────┬────────┘
                            │
                     ┌──────▼──────┐
                     │ RAGFlow     │
                     │ Web / API   │
                     └──────┬──────┘
                            │
             ┌──────────────┼──────────────┐
             │              │              │
        ┌────▼────┐    ┌────▼────┐    ┌────▼────┐
        │ Parser  │    │ Search  │    │ Reranker│
        └─────────┘    └─────────┘    └─────────┘
                            │
                     ┌──────▼──────┐
                     │ Vector/Full │
                     │ Text Index  │
                     └──────┬──────┘
                            │
                     ┌──────▼──────┐
                     │ Local LLM   │
                     │ Ollama/vLLM │
                     └─────────────┘

For strictly air-gapped/on-prem, I'd keep all four layers local:

• RAGFlow
• embedding model
• reranker
• LLM

That way documents, embeddings and prompts never leave your network.

My recommendation by scenario

"We have PDFs, Word docs, policies, manuals, SharePoint exports, etc. and want an internal chatbot."
→ RAGFlow

"We want a self-hosted platform for employees to build AI apps and workflows."
→ Dify

"We're engineers building a custom RAG product/API."
→ LlamaIndex

"We need highly controlled, observable production pipelines."
→ Haystack

One important caveat: the framework isn't usually the biggest determinant of RAG quality. Document parsing, chunking, metadata, hybrid retrieval, reranking and evaluation tend to matter more.github.com+2

If you tell me your approximate document volume (e.g. 100K PDFs / 10 TB), GPU(s), number of users, and whether the environment is fully air-gapped, I can propose a concrete on-prem architecture—including RAGFlow vs LlamaIndex, vector DB, embedding model, reranker, local LLM, Kubernetes/Docker, and sizing.

Best open-source RAG frameworks for on-premise deployment

If you want to deploy retrieval-augmented generation (RAG) on your own servers, I'd shortlist RAGFlow, Haystack, LlamaIndex, and Dify first. They cover different needs, from a ready-to-run document-chat platform to a highly customizable Python framework. All four have open-source offerings and support self-hosting, but they differ substantially in how much infrastructure and application code you must manage.vdf.ai+3

My top recommendations

1. RAGFlow

(Best turnkey RAG engine)

Best if your data consists of PDFs, reports, tables, scanned documents, or other complex files. RAGFlow emphasizes document understanding, retrieval, and answers with citations, and provides a web interface and Docker-based deployment.vdf.ai+4

• Deployment: Docker Compose
• Customization: Moderate
• Trade-off: More services and infrastructure to operate; check architecture compatibility, especially for ARM servers.

Project: RAGFlow on GitHub ·Deployment guide

2. Haystack

(Best for production engineering)

Best if you want explicit control over each step of your RAG pipeline: ingestion, chunking, embeddings, retrieval, reranking, generation, and evaluation. Its modular design fits teams building their own production services.vdf.ai+1

• Deployment: Python application, containers, or your existing infrastructure
• Customization: High
• Trade-off: You build and maintain more of the application yourself.

Project: Haystack on GitHub ·Documentation

3. LlamaIndex

(Best for custom document-centric applications)

Best if you are building a bespoke internal knowledge assistant or enterprise search service and want flexible indexing, retrieval, query engines, and data connectors. It is a Python toolkit rather than a complete application you deploy as-is.vdf.ai+6

• Deployment: Embed it in your own Python service or container.
• Customization: High
• Trade-off: You own the API, UI, authentication, persistence, and operations.

Project: LlamaIndex on GitHub ·Documentation

4. Dify

(Best for fastest low-code deployment)

Best if you want a working application platform with a visual workflow builder, knowledge bases, model configuration, and APIs without writing the entire application. The official project documents a Docker Compose installation path.vdf.ai+4

• Deployment: Docker Compose
• Customization: Moderate; visual workflows plus code integrations
• Trade-off: More platform conventions and dependencies than a lightweight library; review the project's license terms for your intended use.

Project: Dify on GitHub ·Documentation

How they compare for on-premise use

These are qualitative comparisons, not benchmark scores. Actual retrieval quality depends on your parsing strategy, embedding model, reranker, data, and evaluation methodology.vdf.ai+2

A practical on-premise architecture

For a production deployment, the RAG framework is only one piece of the stack. A typical setup looks like this:

Your internal data

PDFs · Office files · Wikis · Databases

RAG framework

Parsing · Chunking · Embeddings · Indexing

Search storage

Qdrant, Milvus, or PostgreSQL with pgvector

Model serving

Locally hosted embedding and LLM services

Internal API or chat application

Authentication · Access control · Citations · Monitoring

You can host all of these components on your own infrastructure. For sensitive environments, verify that model inference, embedding generation, telemetry, document parsing, and any external integrations stay within your approved network boundary.

Which would I choose?

• For the simplest path to a document-chat system: RAGFlow, especially if your documents have complex layouts.
• For a maintainable, custom production service: Haystack.
• For a Python application with sophisticated data ingestion and retrieval requirements: LlamaIndex.
• For a self-hosted platform that business users can configure: Dify.

One important distinction: if your real goal is an internal ChatGPT-like experience for employees, a complete self-hosted platform may be a better starting point than a framework library.

Let me narrow it down for your servers

1. How do you want to deploy?

Docker / Docker Compose on a Linux server

Kubernetes

Bare metal or VMs without containers

2. What kind of data will you index?

PDFs, Office files, and mixed documents

Databases, tables, and structured data

Source code and technical documentation

A mix of files, databases, and internal systems

3. What scale are you targeting?

Small pilot, under 10,000 documents

Department or business unit, 10,000–1 million documents

Enterprise scale, over 1 million documents or high concurrency

4. How strict is the network isolation requirement?

Fully offline / air-gapped; no external API calls

Private network; external calls prohibited for sensitive data

On-premise deployment, but approved external model APIs are acceptable

[Recommend my stack]