Dify vs Flowise: Which LLM Builder Wins?

Updated 2026-10-18 · 1077 words

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Quick take: if you want a guided path from a document upload to a shippable chatbot, with conversation logs, user management, and an API or embeddable widget included, Dify's backend-as-a-service layer gets you there with less assembly. If you want to see and control every node in the pipeline, the LLM call, the retrieval chain, the agent tools, as separate, rewireable pieces, Flowise's canvas is the more flexible foundation. We self-hosted both via Docker and built the same document-grounded support chatbot and a tool-calling agent in each to see where the experience actually diverges.

What Dify and Flowise actually are

Dify, from LangGenius, is an LLM-application platform built around shipping a complete app, not just a pipeline. It bundles a visual workflow builder for agent and RAG logic, a built-in knowledge base with automatic chunking and embedding, a prompt IDE for testing and versioning prompts, and a backend-as-a-service layer that turns whatever you build into a callable API or an embeddable chat widget, complete with conversation logs and basic user management, with almost no extra work beyond the flow itself.

Flowise is an open-source, drag-and-drop builder specifically for LLM applications, built on LangChain.js concepts. Every node on its canvas represents a piece of the AI stack, an LLM provider, a vector store, a document loader, a memory module, an agent tool, and every flow is exposed as an API endpoint the moment you save it. It launched in 2023 and has a document store feature of its own for managing ingested files, but it stays closer to being a pipeline-building canvas than a full application platform with built-in conversation management.

Comparison table

Building a RAG chatbot

In Dify, we uploaded our support documents directly into its knowledge base feature, which handled chunking and embedding automatically with sensible defaults we could still tune. Wiring that knowledge base into an agent, writing the system prompt, and testing it in Dify's built-in prompt playground took about 20 minutes, and the result was already a deployable app with conversation history and basic access controls, not just a pipeline we'd still need to wrap in our own interface.

In Flowise, the same job meant assembling the pipeline ourselves as visible nodes: a document loader, a text splitter, an embeddings node, a vector store, and a conversational retrieval chain, wired together on the canvas in about 25 minutes. The result was a working chatflow exposed as an API endpoint, which is genuinely useful, but turning that into a managed app with conversation logs and user accounts the way Dify delivers out of the box meant building that layer ourselves on top of the Flowise API.

Where Flowise pulled ahead was transparency: when a retrieval result looked off, we could inspect exactly which node was underperforming, the splitter's chunk size, the embedding model, the retriever's top-k setting, and adjust that one piece directly. Dify's guided knowledge base setup is faster to a working result, but digging into why a specific retrieval went wrong took more exploration of settings that are more abstracted away by design.

Licensing and self-hosting

Both are free to self-host with no artificial cap on flows, documents, or users, limited only by your own server. The licenses differ in the same shape as n8n's fair-code model: Dify's source is public and free for internal use, but the license restricts offering a hosted, multi-tenant version of Dify itself as a competing commercial product. Flowise's Apache-2.0 license carries no such restriction, which matters specifically if your plan involves reselling access to the platform itself rather than just using it internally.

Pricing reality check

Dify's cloud sandbox tier is genuinely free for light experimentation, with paid tiers scaling as usage grows. Flowise Cloud has no equivalent free tier and starts around $35/month, though self-hosting it costs nothing beyond your own server. Neither platform fee includes the LLM API calls your app actually makes, which is billed separately by whichever model provider you connect and is usually the larger cost once you're past prototyping on either tool.

When NOT to use Dify

Skip Dify if you want full visibility into every step of the pipeline and plan to customize deeply at the node level. Its guided knowledge base and app layer are faster to a working result, but they're more abstracted than Flowise's canvas, and digging past the guided defaults takes more effort than Flowise's node-first design allows for from the start.

When NOT to use Flowise

Skip Flowise if the deliverable is a complete, shippable app and you don't want to build the conversation-management and user-access layer yourself. Flowise gets you a working pipeline exposed as an API fast, but Dify's built-in conversation logs, user management, and widget deployment save real time that Flowise leaves for you to assemble.

Which should you pick?

Choose Dify if you want the fastest path from a document upload to a deployable app with conversation logs and user management included. Choose Flowise if you want full, node-level visibility into the pipeline and plan to customize deeply rather than accept guided defaults. If Flowise is your pick, our Flowise vs n8n comparison covers how it stacks up against a general automation platform, and our Langflow vs Flowise guide covers the closest competing canvas if you're still narrowing the field. Our guide to self-hosting Flowise covers the production Docker setup we used here, and our roundup of open-source AI agent frameworks covers the code-first end of the spectrum if neither visual builder is quite what you need.

Read the source docs before you commit either way: Dify and Flowise both publish self-hosting guides that go deeper than either vendor's marketing pages.

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