Dify vs Flowise vs Langflow: 2026 Head-to-Head

Both Flowise and Langflow build on LangChain under the hood. For a detailed comparison of the underlying frameworks, see LangChain vs LlamaIndex vs Haystack.

Not everyone building AI applications is a developer. And even developers don't want to wire up LLM chains, vector databases, and embedding pipelines from scratch every time. Visual AI workflow builders let you drag, drop, and connect components into working AI applications — chatbots, RAG pipelines, multi-step agents — without writing (much) code.

Three open-source platforms lead this space in 2026: Dify (the full-stack AI application platform), Flowise (the LangChain-based node builder), and Langflow (DataStax's visual IDE for LangChain/LangGraph). They all offer drag-and-drop interfaces for building AI workflows, but their architectures, target users, and pricing models differ in ways that matter.

We built the same RAG chatbot — ingest documents, chunk them, embed into a vector store, and serve answers with citations — in all three. Here's the real comparison.

Quick Comparison

Dify: The Full-Stack AI Application Platform

Dify isn't just a workflow builder — it's a complete platform for building, deploying, and operating AI applications. Think of it as the "app framework" approach: you get a knowledge base, prompt management, workflow orchestration, user analytics, and API publishing all in one package.

With 90,000+ GitHub stars and backing from a well-funded team, Dify has emerged as the most polished option for teams that want a production-ready AI application platform without gluing together six different tools.

Architecture

Dify separates AI applications into distinct types:

• Chatbot: Conversational AI with memory, knowledge retrieval, and tool use
• Text Generator: Single-input, single-output processing (summarization, extraction, translation)
• Agent: Autonomous reasoning with tool access (ReAct or function-calling patterns)
• Workflow: Multi-step visual pipelines with conditional logic, loops, and parallel execution

The workflow builder is where Dify shines. Each node represents an operation — LLM call, knowledge retrieval, code execution, HTTP request, conditional branch — and you connect them visually. Unlike Flowise's LangChain-centric approach, Dify uses its own abstraction layer. This means you're not limited to LangChain's component library, but you're also locked into Dify's way of doing things.

The Knowledge Base is Dify's strongest differentiator. Upload documents (PDF, Word, Notion pages, web URLs), and Dify handles chunking, embedding, and indexing automatically. You pick your embedding model and vector store, and Dify manages the pipeline. No manual chunk-size tuning or embedding configuration — it just works. For teams that need production-grade RAG pipelines, this removes a significant engineering burden.

Pricing

Dify offers both self-hosted (free) and cloud options:

• Sandbox (Free): 200 message credits, 5 apps, 1 member, 50 MB knowledge storage. Good for testing.
• Professional ($59/workspace/month): 5,000 credits, 50 apps, 3 members, 5 GB storage. For indie developers and small teams.
• Team ($159/workspace/month): 10,000 credits, unlimited apps, unlimited members, 20 GB storage. For growing teams.
• Enterprise (Custom): Dedicated infrastructure, SSO/SAML, audit logs, SLA, custom model integration.

The self-hosted version via Docker Compose gives you everything except enterprise features. For most teams, self-hosting is the obvious choice — you get the full platform at zero cost beyond compute.

Strengths

• Best debugging experience. Dify's workflow debugger shows execution time, input/output values, and token usage for every node. When a pipeline fails, you know exactly where and why. Neither Flowise nor Langflow matches this.
• Knowledge Base is production-ready. Upload files, and Dify handles the entire RAG pipeline — chunking strategy, embedding, indexing, retrieval ranking. Other platforms make you configure each step manually.
• Prompt versioning. Track changes to prompts over time, compare performance, and roll back. Essential for teams iterating on production AI applications.
• App publishing. One click to generate an API endpoint or embeddable chat widget. No separate deployment step.

Weaknesses

• Opinionated platform. Dify's abstractions don't always map to how LangChain or other frameworks work. If you outgrow Dify, migrating away means rewriting, not porting.
• Commercial license complexity. The Apache 2.0 license has additional terms for multi-tenant SaaS usage. If you're reselling AI applications built on Dify, read the license carefully.
• Cloud pricing adds up. At $59/month per workspace, teams with multiple projects pay more than expected. Self-hosting eliminates this but requires infrastructure management.
• Plugin ecosystem is young. Dify's marketplace has fewer third-party integrations than Flowise's LangChain-based component library.

Flowise: The Fastest Path to a Working Chatbot

Flowise takes the opposite approach from Dify: instead of building a comprehensive platform, it gives you a visual canvas for connecting LangChain components. If you know what LangChain nodes you need, Flowise lets you wire them together without writing Python.

It's the most approachable tool of the three. Install via npm, open the browser, drag in a ChatOpenAI node, connect a vector store, add a retrieval chain — and you have a RAG chatbot in fifteen minutes. The learning curve is essentially zero for anyone who's seen a node-based editor.

Architecture

Flowise maps directly to LangChain's component model. Every node in the visual canvas corresponds to a LangChain class:

• Chat Models: ChatOpenAI, ChatAnthropic, ChatOllama, etc.
• Embeddings: OpenAI Embeddings, HuggingFace, Cohere
• Vector Stores: Pinecone, Chroma, Qdrant, Weaviate, FAISS
• Chains: Conversational Retrieval, LLM Chain, Sequential Chain
• Agents: ReAct, OpenAI Functions, Structured Chat
• Tools: Calculator, Web Search, Custom API, Code Interpreter

You drag components onto the canvas, connect inputs to outputs, and hit "Run." Flowise handles the LangChain instantiation behind the scenes. This 1:1 mapping between visual nodes and LangChain classes means that anything you can build in LangChain, you can build in Flowise — with the caveat that complex chains sometimes need manual configuration that the UI doesn't expose.

Pricing

Flowise offers a generous self-hosted option alongside cloud plans:

• Free (Self-hosted & Cloud): 2 flows, 100 predictions/month, 5 MB storage, community support. For testing.
• Starter ($35/month): Unlimited flows, 10,000 predictions/month, 1 GB storage. For solo builders.
• Pro ($65/month): 50,000 predictions/month, 10 GB storage, 5 users (+$15/user), RBAC, priority support. For teams.
• Enterprise (Custom): On-prem, SSO/SAML, LDAP, audit logs, SLA. For regulated environments.

The self-hosted version is 100% free with no feature restrictions. Install it with npx flowise start and you're running. This makes Flowise the cheapest option for teams comfortable managing their own infrastructure.

Strengths

• Fastest time to prototype. From npm install to working chatbot in under 15 minutes. No framework of the three is faster for getting something running.
• LangChain compatibility. If a LangChain component exists, Flowise probably supports it as a visual node. The ecosystem coverage is broad.
• Embeddable chat widget. One line of JavaScript to embed your chatbot in any website. The widget is customizable and production-ready out of the box.
• Self-hosted simplicity. Single Node.js process, no database required (uses SQLite by default), runs on a $5/month VPS. The lowest infrastructure overhead of the three.

Weaknesses

• Limited beyond chatbots. Flowise excels at conversational RAG applications. But complex multi-step workflows, conditional logic, or non-chat use cases push against its boundaries quickly.
• No workflow orchestration. Unlike Dify's workflow builder or Langflow's LangGraph support, Flowise doesn't offer visual pipeline orchestration for multi-step processes.
• Debugging is basic. Logs show what happened, but there's no node-level trace with timing and token usage like Dify provides.
• Prediction-based pricing is unpredictable. "Predictions" as a billing unit is vague — a simple greeting costs the same as a complex RAG query with multiple retrievals. Cloud costs can surprise you.

Langflow: The Visual IDE for LangChain Power Users

Langflow positions itself as a visual IDE — not just a canvas, but a development environment for building, testing, and iterating on LangChain and LangGraph applications. Acquired by DataStax in 2024, Langflow has enterprise backing and deep integration with Astra DB (DataStax's vector database).

Where Flowise maps LangChain components 1:1 onto visual nodes, Langflow goes further: it supports LangGraph for multi-agent orchestration, custom Python components, and a playground for interactive testing. It's the most powerful visual builder of the three — and the most complex.

Architecture

Langflow's visual editor works with flows composed of connected components:

• Inputs/Outputs: Chat input, text input, file upload, message history
• Models: Any LangChain-supported LLM (OpenAI, Anthropic, Ollama, local models)
• RAG components: Document loaders, text splitters, embeddings, vector stores, retrievers
• Agents: LangChain agents with tool access, LangGraph multi-agent flows
• Processing: Python code nodes, conditional routing, iterators, data transforms
• Custom components: Write Python functions and expose them as visual nodes

The LangGraph integration is Langflow's key differentiator. While Flowise stops at LangChain chains and agents, Langflow lets you build graph-based workflows with conditional edges, cycles, and state management — the same patterns you'd use in production multi-agent orchestration. This makes Langflow the natural choice for teams that need visual tooling for complex agent architectures.

Pricing

Langflow's pricing model is unique: the open-source version is fully featured (MIT license), and DataStax offers a hosted version through their Astra platform.

• Open Source (Self-hosted): Free. MIT license. Full feature set including LangGraph support.
• DataStax Langflow (Cloud): Free tier included with Astra DB. Managed hosting, authentication, and scaling. Pay-as-you-go for database storage and compute.
• Enterprise (Custom): SOC 2, SSO, dedicated infrastructure, DataStax support.

The MIT license makes Langflow the most permissive of the three for commercial use. No commercial license caveats, no SaaS restrictions. Build whatever you want.

Strengths

• LangGraph support. The only visual builder that supports graph-based multi-agent workflows natively. If you need cycles, conditional routing, or complex state machines in a visual editor, Langflow is the only option.
• Custom Python nodes. Write arbitrary Python and expose it as a visual component. This bridges the gap between no-code and full-code when you need custom logic.
• MIT license. No commercial restrictions whatsoever. Build, sell, embed, white-label — the license permits everything.
• DataStax backing. Enterprise support, Astra DB integration, and a well-funded team ensuring continued development.

Weaknesses

• Steeper learning curve. The flexibility comes at a cost. New users face more options and less guidance than Flowise's straightforward "connect these nodes" approach.
• DataStax ecosystem pull. The cloud version steers you toward Astra DB as your vector store. The OSS version works with anything, but the managed experience is optimized for DataStax's stack.
• Fewer built-in templates. Dify's template library and Flowise's marketplace have more ready-to-use starting points than Langflow.
• Documentation gaps. The OSS docs are decent but lack the depth of Dify's documentation, particularly for advanced LangGraph patterns.

Head-to-Head: The Same RAG Pipeline

We built a document Q&A system in all three platforms to compare the actual experience:

Task: Upload a 50-page PDF, chunk it, embed into a vector store, and serve answers with page citations through a chat interface.

Dify: 10 minutes, zero code

Upload the PDF to Knowledge Base. Select embedding model. Create a Chatbot app, enable Knowledge Retrieval. Done. The knowledge base handles chunking strategy, embedding, and indexing automatically. Citations appear in responses with source references. Debugging shows retrieval scores, chunk content, and LLM reasoning at every step.

Flowise: 15 minutes, zero code

Drag in a PDF loader, text splitter, embeddings node, Chroma vector store, conversational retrieval chain, and ChatOpenAI. Connect them in order. Configure chunk size and overlap manually. Adjust retrieval parameters. The pipeline works, but you're making decisions that Dify handles automatically (chunk size, overlap, retrieval strategy). Citations require custom prompt engineering.

Langflow: 20 minutes, minimal code

Similar to Flowise but with more configuration options. The component library is larger, and you can add custom Python nodes for citation formatting. LangGraph support means you could add a verification agent that checks retrieved chunks for relevance — but for a basic RAG pipeline, this extra power isn't needed.

Verdict for RAG: Dify wins for speed and polish. Flowise wins for simplicity and understanding what's happening. Langflow wins when you need the RAG pipeline to evolve into something more complex.

Self-Hosting and GPU Requirements

All three platforms support self-hosting, which is critical for teams handling sensitive data or wanting to run local LLMs. Connect any platform to Ollama for private, on-premise inference and your data never leaves your network.

The platforms themselves are lightweight — a basic VPS handles Flowise easily, and Dify/Langflow run fine on 4 GB RAM instances. The GPU question arises when you self-host the LLM layer too.

Running local inference for a visual AI builder means your LLMs need to handle multiple concurrent requests (one per active user or workflow execution). A high-VRAM GPU like the RTX 4090 with 24 GB VRAM serves quantized models at interactive speeds while handling the concurrent load that visual workflow builders generate. For multi-user deployments, this avoids per-API-call costs entirely — your team gets unlimited AI queries at a fixed hardware cost.

For teams that prefer cloud GPUs for inference scaling, see our GPU cloud platform comparison for the best price-per-token options.

When to Use Each Platform

Choose Dify if:

• You want a complete AI application platform, not just a workflow builder
• RAG applications are your primary use case and you want the knowledge base handled for you
• You need prompt versioning, user analytics, and one-click API publishing
• Your team includes non-technical stakeholders who need to understand what the AI is doing
• Use case: customer support chatbots, internal knowledge bases, document Q&A products

Choose Flowise if:

• Speed matters most — you need a working prototype today, not next week
• Your use case is primarily conversational (chatbots, RAG, Q&A)
• You're comfortable with LangChain concepts and want visual tooling for them
• Budget is constrained — self-hosted Flowise on a $5 VPS is the cheapest path to production
• Use case: embedded chatbots, lead qualification, simple document retrieval

Choose Langflow if:

• You need multi-agent workflows with LangGraph support in a visual editor
• Custom Python logic is part of your pipeline (data transforms, custom retrievers, validation)
• You want the most permissive license (MIT) for commercial products
• You're building complex pipelines that go beyond simple chat or retrieval
• Use case: multi-agent research systems, complex document processing, enterprise AI pipelines

Beyond Visual Builders: When to Graduate

Visual AI builders are starting points, not endpoints. As your application grows, you'll hit limitations:

• Version control is awkward for visual flows. You can't diff a drag-and-drop canvas the way you diff Python code.
• Testing and CI/CD don't integrate naturally with visual editors. Unit tests for individual nodes? Not straightforward.
• Custom logic that doesn't fit the node model requires workarounds that defeat the purpose of no-code.

When you hit these walls, the natural next step is framework-level orchestration — CrewAI, AutoGen, or LangGraph as code. The visual builder taught you the patterns. The framework gives you production control.

For teams combining visual AI workflows with traditional automation, n8n, Make, or Zapier handle the non-AI parts of your pipeline: triggers, scheduling, data routing, and third-party integrations. The most effective production systems use a visual AI builder for the intelligence layer and an automation platform for everything around it.

The Honest Recommendation

For most teams in 2026, Dify is the best starting point. Its knowledge base, debugging, and app publishing features save weeks of engineering time. The free self-hosted version has no meaningful limitations.

Flowise is the right choice when simplicity wins. If your use case is "chatbot with document retrieval" and you want the fastest, cheapest path to production, Flowise delivers without the complexity overhead.

Langflow is for teams that need power now and will need more later. The LangGraph integration and custom Python nodes mean you won't outgrow it as fast as the other two. But you pay for that power in learning curve.

The good news: all three are open source, all three support self-hosting, and all three work with local LLMs. Try all three in an afternoon, pick the one that clicks, and start building. The context engineering principles that make AI applications effective don't change regardless of which builder you use.

*For multi-agent framework comparisons beyond visual builders, see our CrewAI vs AutoGen vs LangGraph deep dive. For connecting your AI builder to local LLMs, check our Ollama production config guide.*

*Disclosure: Links above are affiliate links. ToolHalla may earn a commission at no extra cost to you. We only recommend hardware we'd actually use.*

Frequently Asked Questions

What is the difference between Dify, Flowise, and Langflow?

Dify is a full-stack AI application platform with built-in RAG, prompt versioning, and app publishing. Flowise is the simplest option — fastest path to a working chatbot. Langflow is the most powerful, supporting multi-agent LangGraph workflows and custom Python nodes.

Which is better for beginners: Dify or Flowise?

Flowise is easier to start with — working RAG chatbot in under an hour. Dify has more features but a steeper learning curve. For non-technical teams, Dify's knowledge base UI and analytics make it the better long-term choice.

Can all three run locally (self-hosted)?

Yes. All three are fully open-source and self-hostable with local LLMs via Ollama. Flowise runs on a $5/month VPS. Dify and Langflow need 4GB+ RAM. No external API required.

Does Dify support local LLMs?

Yes — Dify integrates directly with Ollama. Configure your Ollama endpoint and any local model (Qwen, Llama, Mistral, etc.) becomes available in your applications.

Is Langflow better than LangChain?

Langflow is a visual interface for LangChain, not a replacement. It makes LangChain patterns accessible without code. For production, you'll eventually move to pure LangChain for version control. Langflow is the fastest way to learn and prototype.

What is the best free no-code AI builder?

For simplicity: Flowise. For features: Dify. Both are MIT-licensed, free to self-host, and run on any VPS or local machine. Try Flowise first — working prototype in 30 minutes.