Dify vs Flowise: Self-Hosted LLM App Platform Comparison (2026)
Dify and Flowise both let you build LLM-powered applications through a visual interface without writing a full backend. But they are aimed at different maturity stages: Flowise for rapid experimentation, Dify for production-grade deployments where teams need RAG knowledge bases, API publishing, and multi-user workspaces.
TL;DR
| Dify | Flowise | |
|---|---|---|
| License | Apache-2.0 (with cloud-service restriction) | MIT |
| GitHub stars | ~100,000 | ~38,000 |
| Stack | Python (FastAPI) + Next.js | Node.js |
| Idle RAM | ~3 GB (full stack with Weaviate) | ~512 MB |
| RAG knowledge bases | ✓ (built-in, Weaviate-backed) | ✓ (via LlamaIndex / Chroma) |
| Workflow editor | ✓ (visual DAG) | ✓ (drag-and-drop flow) |
| Agent builder | ✓ | ✓ |
| API publishing | ✓ (one-click REST + widget) | ✓ (basic endpoint) |
| Team workspace | ✓ (roles, permissions) | ✗ |
| Annotation / eval | ✓ | ✗ |
| Best for | Production teams, RAG apps, governed workflows | Prototyping, solo developers, lightweight stacks |
What each platform does
Dify: the production LLM platform
Dify is a Python + Next.js platform for building, deploying, and monitoring LLM applications. It covers the full lifecycle:
- Workflow editor: visual DAG builder for chaining LLM calls, tool use, conditional branching, and loops
- RAG knowledge bases: upload PDFs, web pages, or CSV files; Dify chunks, embeds, and indexes them into Weaviate automatically
- Agent builder: configure tool-calling agents with web search, calculator, custom API tools, and more
- API publishing: every Dify app generates a REST endpoint and an embeddable chat widget in one click
- Team workspace: user roles (admin, editor, viewer), app permissions, and annotation tools for evaluating outputs
- Model provider management: connect OpenAI, Anthropic, Ollama, vLLM, or any OpenAI-compatible endpoint from the UI
License note: Dify is Apache-2.0 with a specific restriction — you may not use the source code to build a competing cloud service without a commercial agreement with Dify AI. Self-hosting for internal use is fully permitted.
Flowise: the fast-start flow builder
Flowise is a Node.js application that renders LangChain and LlamaIndex components as drag-and-drop UI nodes. You wire together an LLM, a memory, a retriever, and a tool in minutes without writing code. Key capabilities:
- Visual flow builder: LangChain chains and LlamaIndex workflows as a node graph
- RAG support: connect ChromaDB, Pinecone, Qdrant, Postgres pgvector, or other vector stores
- Agent flows: ReAct and Function Calling agents with configurable tool nodes
- Chatflow API: every flow exposes a REST endpoint
- Embed widget: drop an iframe onto any site to expose a chatbot
Flowise does not have user roles, annotation, or team workspaces in the core product. A single Flowise instance is a personal or small-team tool — not a governed platform for a department with multiple developers.
Feature comparison
| Feature | Dify | Flowise |
|---|---|---|
| Visual workflow editor | ✓ (DAG, branching, loops) | ✓ (linear chains, some branching) |
| RAG knowledge base UI | ✓ (upload, chunk, embed in UI) | Partial (manual vector store setup) |
| Built-in vector DB | ✓ (Weaviate, bundled) | ✗ (external, self-managed) |
| Agent builder | ✓ | ✓ |
| Tool use / function calling | ✓ | ✓ |
| REST API publishing | ✓ | ✓ |
| Embeddable chat widget | ✓ | ✓ |
| Team workspace + roles | ✓ | ✗ |
| Annotation + eval | ✓ | ✗ |
| Usage analytics | ✓ | Basic |
| Model provider management | ✓ (UI-based) | ✓ (config-based) |
| LangSmith / tracing | ✓ (LangFuse integration) | ✓ (LangSmith) |
RAM and infrastructure
Dify ships a multi-container Compose stack:
| Container | Idle RAM |
|---|---|
| dify-api (Python FastAPI) | ~600 MB |
| dify-worker (Celery) | ~400 MB |
| dify-web (Next.js) | ~300 MB |
| Weaviate (vector DB) | ~900 MB |
| PostgreSQL | ~200 MB |
| Redis | ~30 MB |
| Nginx | ~20 MB |
| Total | ~2,450 MB |
A Liquid Web 8 GB Managed VPS is the comfortable production minimum. On 4 GB, Dify will run but with little headroom for model inference traffic spikes.
Flowise:
| Container | Idle RAM |
|---|---|
| flowise (Node.js) | ~300 MB |
| PostgreSQL (optional) | ~150 MB |
| Total | ~450 MB |
Flowise can run on a 2 GB VPS with room to spare. The Node.js binary is lean.
The license difference
Flowise is MIT — no restrictions on use, including building a commercial SaaS product on top of it.
Dify is Apache-2.0 with a cloud-service restriction — you cannot resell Dify as a hosted service (e.g., "Managed Dify for your team") without a commercial license from Dify AI. Self-hosting for your own team's internal use is fully permitted under the open-source terms. This distinction matters if you are building a product, not just an internal tool.
Which should you choose?
Choose Dify if:
- You are building a production RAG application or internal knowledge base for a team
- You need user roles, API key management, and annotation for output quality review
- You want a complete RAG pipeline (upload → chunk → embed → query) without configuring Weaviate manually
- Your team has multiple developers who need separate workspaces and permissions
- You can spare 8 GB of RAM on the server
Choose Flowise if:
- You are prototyping an LLM workflow and want to iterate quickly
- You are a solo developer or small team without multi-user workspace needs
- RAM is a constraint (2–4 GB VPS)
- You need MIT licensing without any use restrictions
- You want to wire together LangChain and LlamaIndex nodes without writing Python
A practical path: Start with Flowise to validate your LLM application concept. When you need production features — RAG knowledge bases, team roles, API publishing, or annotation — migrate to Dify. Both expose OpenAI-compatible endpoints, so switching the backing platform does not require rewriting your client applications.
Further reading
- Self-Host Dify — Docker Compose setup, Weaviate configuration, model provider connection, and RAG knowledge base guide
Yes. Flowise has a built-in Ollama node. You configure the Ollama base URL (e.g., http://ollama:11434) in the node settings, and all locally-loaded Ollama models are available. This makes Flowise + Ollama a fully self-hosted, no-API-cost LLM workflow stack that fits on a single 4 GB VPS.
Dify's RAG knowledge base feature requires a vector database. The default Docker Compose stack bundles Weaviate, which contributes the largest share of idle RAM (~900 MB). You can configure Dify to use an external Qdrant, Pinecone, or pgvector instance instead, which reduces the Compose stack footprint. The tradeoff is additional infrastructure to manage.
Self-hosting Dify means all prompts, retrieved documents, and model outputs stay on your own infrastructure — no data leaves your server to a third party (beyond the LLM provider API you configure). This data-residency property is the core compliance benefit of self-hosting. Actual HIPAA or SOC 2 compliance depends on your server's security controls, access management, and audit logging — not Dify itself. Liquid Web offers HIPAA-compliant server configurations with BAA available.