Head-to-Head
Dify vs Flowise (2026)
Dify
Freemium★ 4.5
Flowise
Free★ 4.4
Dify and Flowise are both open-source, self-hostable platforms for building LLM-powered applications without writing a full backend. Both use a visual approach to chain models, retrieval systems, and tools into production-ready AI applications. The differences are in depth, polish, and target audience. Dify is the more complete product. It has a managed cloud option, a polished API layer for serving LLM applications at production scale, advanced RAG pipeline configuration, built-in prompt management with version history, and support for 100+ LLM providers and embedding models. For teams building applications they intend to put in front of real users, Dify offers the infrastructure layer that Flowise lacks. Flowise is the simpler and faster tool to get started with. Its drag-and-drop node canvas - based on LangChain abstractions - is easy to understand for developers who know LangChain, and self-hosting via Docker is straightforward. For prototyping, internal tools, and teams that want to experiment with LLM architectures without committing to a production platform, Flowise is fast to iterate with. Both are genuinely good tools. Choose Dify when you are building something that needs to serve external users or requires production-grade reliability. Choose Flowise when you are prototyping or building internal-facing tools where speed of iteration matters more than polish.
Feature Comparison
Ease of Getting Started
Flowise has a simpler setup and a more immediate visual experience. Dify has more configuration options that add onboarding friction but pay off for production use cases.
LLM and Model Support
Dify supports 100+ LLM and embedding models including OpenAI, Anthropic, Mistral, Llama, and dozens of open-source models. Flowise covers major providers with good community-contributed nodes.
RAG Pipeline Depth
Dify offers advanced RAG configuration including chunk size, retrieval strategy, reranking, and hybrid search. Flowise provides solid RAG through LangChain abstractions but with less fine-grained control.
Production API Layer
Dify generates a versioned, managed API for each application, making it straightforward to serve LLM features from an external product. Flowise can expose APIs but with less management tooling.
Prompt Management
Dify includes prompt versioning, testing, and annotation workflows. Flowise does not have a built-in prompt management system - prompts are edited directly in nodes.
Self-Hosting and Open Source
Both are fully open-source and self-hostable via Docker. Neither requires a cloud account to run in production. Both have active GitHub communities.
Prototyping Speed
Flowise's node canvas is faster for quickly wiring together a chain or agent to test an idea. Dify's more structured approach takes longer to configure but scales better once the architecture is defined.
Verdict
This comparison is context-dependent. Dify scores 32/35 and Flowise scores 29/35. Choose based on your specific workflow needs.
Bottom Line
Dify and Flowise are the two leading open-source platforms for building LLM apps without writing every prompt and chain by hand. Dify is the more product-oriented option - polished UI, prompt orchestration, dataset management, and a marketplace of templates. Flowise is closer to "LangChain visual editor" - drag-and-drop chains, more flexibility for power users, and a tighter LangChain mapping. Pick Dify if you want a fast path from "we need an LLM app" to "the app is live for customers." Pick Flowise if you want maximum flexibility and your team already thinks in LangChain abstractions.
Pick Dify
You want a polished platform for building LLM apps that includes UI, prompt management, RAG, dataset tools, and team collaboration out of the box. Dify (free OSS + cloud tiers) ships with a generous feature set and is closer to "production-ready LLM app studio" than Flowise. Best for product teams shipping internal LLM apps.
Pick Flowise
You want a visual canvas for LangChain chains and the freedom to wire any node to any node. Flowise (free OSS + cloud) maps cleanly to LangChain abstractions and gives technical builders fine-grained control over chain composition. Best for engineering teams who already use LangChain and want a visual layer.
Frequently asked
Which has better RAG out of the box?
Dify, marginally. The dataset and knowledge base UI is more polished, with visible chunking strategies and easier debugging. Flowise has RAG capabilities but you wire them yourself node-by-node, which is more flexible but slower to set up.
Can I self-host both?
Yes - both are open-source and run in Docker. Dify is heavier (more services, larger footprint); Flowise is lighter (single Node.js service). For a personal project, Flowise is easier to spin up.
Which integrates more LLMs?
Both support all major LLMs (OpenAI, Anthropic, Gemini, local models via Ollama, open-weight models via Together/Replicate). Dify has more polished multi-model routing for production use; Flowise has more raw flexibility.
Are they alternatives to LangSmith?
Partial overlap. LangSmith is observability and tracing for LLM apps - it does not build apps, it monitors them. Dify and Flowise build apps. For a complete stack, you might run Flowise (build) + LangSmith (observe).
Which has better team collaboration?
Dify has stronger multi-user collaboration and workspace features in 2026. Flowise is more single-builder-oriented but has been adding team features. For a 5+ person product team, Dify is the smoother choice.