Dify, Flowise, and Langflow compared for building AI-powered business tools. Open source options, pricing, and which platform fits your use case.
Dify vs Flowise vs Langflow: AI App Builders Compared (2026)
Three open-source platforms dominate the AI app building space right now: Dify, Flowise, and Langflow. All three let you build LLM-powered applications with visual interfaces. All three are open source. All three can be self-hosted.
But they solve different problems, and picking the wrong one will cost you weeks of rework.
We have deployed all three for clients building internal tools, customer-facing chatbots, and RAG-powered knowledge bases. This is what we have learned about where each one excels and where each one falls short.
The Short Version
Dify is a full-stack AI application platform. It handles everything from prompt management to API deployment to conversation tracking. If you want the most polished experience with the least configuration, Dify is the answer.
Flowise is a drag-and-drop LangChain builder. It is the lightest of the three, the easiest to self-host, and the fastest path from zero to a working chatbot. If your use case is straightforward and you want to move fast, Flowise delivers.
Langflow is a Python-native visual IDE backed by DataStax. It gives developers the most control, the deepest customization options, and the best path to production-grade systems that need to scale. If your team writes Python and wants a visual layer on top of real code, Langflow is the right pick.
Platform Architecture
Understanding the architectural differences matters because it determines what you can build and how much work it takes to get there.
Dify
Dify is the most opinionated platform of the three. It is not just a flow builder. It is a complete backend-as-a-service for AI applications. Out of the box, you get:
- A visual workflow editor with five app types (chatbots, text generators, agents, chatflows, and workflows)
- Built-in knowledge base management with hybrid search (dense + sparse vectors)
- Prompt versioning and A/B testing
- API gateway with built-in endpoint generation
- Conversation logging and analytics
- Human-in-the-loop review nodes
- MCP (Model Context Protocol) support for connecting to external tool servers
The trade-off is complexity. Dify's minimum deployment requires around 4 GB of RAM, a PostgreSQL database, Redis, and a Celery worker queue. It is a real application stack, not a lightweight tool.
With over 100,000 GitHub stars and more than a million deployed applications, Dify has the largest community of the three platforms.
Flowise
Flowise takes the opposite approach. It is a single Node.js application that runs on 1 GB of RAM. You install it with npx flowise start and you are building flows in under a minute.
The entire UI is a drag-and-drop canvas where you connect LangChain nodes: models, prompts, memory, tools, vector stores, and output parsers. If you have used LangChain in code, the Flowise canvas is the same concepts with a visual interface.
Key architectural points:
- Built on LangChain JS, so every LangChain component is available as a node
- Over 150 tool integrations including databases, APIs, code execution, and web search
- AgentFlow SDK (added in v3.1.0) for programmatic flow management
- Lightweight enough to run on a $5/month VPS
- Version 3.0+ includes HTTP security validation enabled by default
Flowise currently has around 36,000 GitHub stars. The community is smaller but focused, and the documentation is straightforward.
Langflow
Langflow is the most developer-oriented of the three. Originally an open-source project, it was acquired by DataStax in 2024 and has since received significant investment in enterprise features.
What sets Langflow apart:
- Python-native architecture, so custom components are written in Python, not YAML or JSON configs
- Deep LangChain and LangGraph integration
- Native support for custom Python nodes, meaning you can drop arbitrary Python code into any point in your flow
- Desktop application available (Langflow 1.9 Desktop, released April 2026)
- MCP-based interoperability for connecting flows to IDEs and external agents
- Backed by DataStax, with native Astra DB vector database integration
Langflow hit 100,000 GitHub stars in March 2026, putting it neck-and-neck with Dify in community adoption. The DataStax backing gives it the most secure financial future of the three platforms.
Visual Flow Builder Comparison
All three platforms have visual flow builders, but they work differently.
Dify's builder feels like a product. The UI is clean, the node types are clearly labeled, and non-technical users can navigate it without a tutorial. You can switch between a simple prompt interface and a full workflow canvas depending on complexity. The five app types (chatbot, text generator, agent, chatflow, workflow) give you structured starting points instead of a blank canvas.
Flowise's builder feels like a developer tool. The canvas is a direct representation of LangChain's component model. If you know LangChain, you will be productive in minutes. If you do not, the learning curve is steeper because the node names and connection types map to LangChain abstractions. The trade-off is power: you can build anything LangChain supports.
Langflow's builder feels like an IDE. It exposes more configuration per node, supports inline Python editing, and gives you granular control over data flow between components. The 1.9 release added AI-assisted component generation, which lets you describe what a node should do and have the platform write the Python code for you.
LLM Provider Support
All three platforms support the major model providers. Here is how they compare:
| Provider |
Dify |
Flowise |
Langflow |
| OpenAI (GPT-4o, o1, o3) |
Yes |
Yes |
Yes |
| Anthropic (Claude 4) |
Yes |
Yes |
Yes |
| Google (Gemini 2.0) |
Yes |
Yes |
Yes |
| Mistral |
Yes |
Yes |
Yes |
| Meta (Llama) |
Yes |
Yes |
Yes |
| DeepSeek (V3/R1) |
Yes |
Yes |
Yes |
| Ollama (local models) |
Yes |
Yes |
Yes |
| LM Studio |
Yes |
Yes |
Yes |
| OpenAI-compatible APIs |
Yes |
Yes |
Yes |
| Custom/self-hosted models |
Yes |
Yes |
Yes |
Dify has over 100 model configurations built in. Flowise and Langflow both support any OpenAI-compatible endpoint, which covers most providers. The practical difference is minimal here. All three give you full model flexibility.
RAG Capabilities
Retrieval-augmented generation is where the platforms start to diverge meaningfully.
Dify has the most complete built-in RAG system. You upload documents directly into Dify's knowledge base, configure chunking strategies, choose embedding models, and run hybrid search (combining dense vectors with keyword matching). The reranking pipeline and parent-document retrieval are built in. There is also a Q&A extraction mode that generates FAQ pairs from your documents. For teams that want RAG without managing a separate vector database, Dify is the fastest path.
Flowise handles RAG through LangChain's document loader and vector store nodes. You connect a document source, a text splitter, an embedding model, and a vector store (Pinecone, Weaviate, Chroma, Qdrant, and others). It works well, but you are assembling the pipeline yourself from individual components. The upside is flexibility in choosing exactly which vector store and chunking strategy to use.
Langflow provides dedicated RAG components for document loading (PDF, text, web pages, databases), text chunking (recursive, character-based, semantic), multiple embedding models (OpenAI, Cohere, HuggingFace), and vector store connections (Pinecone, Weaviate, Astra DB, Chroma, FAISS). The OpenRAG project, built on top of Langflow, offers a complete RAG platform using Langflow, Docling, and OpenSearch. For teams that want a production-grade RAG pipeline with custom Python processing at each stage, Langflow gives the most control.
Pricing Comparison
|
Dify |
Flowise |
Langflow |
| Self-hosted |
Free (open source) |
Free (open source) |
Free (open source) |
| Cloud free tier |
Sandbox (200 messages/day) |
2 flows, 100 predictions/month |
Free tier available |
| Cloud starter |
$59/month (Professional) |
$35/month (Starter) |
~$25/month |
| Cloud pro/team |
$159/month (Team) |
$65/month (Pro) |
Custom pricing |
| Enterprise |
Custom |
Custom |
Custom (DataStax) |
| Self-host RAM |
4 GB minimum |
1 GB minimum |
2-4 GB recommended |
| Self-host complexity |
Moderate (PostgreSQL, Redis, workers) |
Low (single Node.js process) |
Moderate (Python, database) |
The cost difference is significant. Flowise is the cheapest to run in every configuration. Dify costs the most but includes the most built-in functionality. Langflow falls in between, with the DataStax cloud option adding enterprise features that the self-hosted version does not include.
For self-hosting, your real costs are infrastructure plus LLM API spend. A typical deployment runs $30-100/month for a solo developer and $500-2,000/month for a team, regardless of which platform you choose. The LLM API costs will dwarf the hosting costs in most production scenarios.
Deployment Options
Dify supports Docker Compose deployment (the recommended approach), Kubernetes via Helm charts, and managed cloud. The Docker Compose setup includes PostgreSQL, Redis, the API server, the worker, and the web frontend. It is well-documented but involves multiple services.
Flowise is the simplest to deploy. A single Docker container or npx flowise start gets you running. It works on Railway, Render, Digital Ocean, AWS, Azure, GCP, and most hosting platforms with one-click deploy templates. For small teams that want to self-host without DevOps overhead, Flowise is the clear winner.
Langflow deploys as a Python application with a database backend. Docker deployment is available, and managed hosting through DataStax or third-party providers (Elestio, Railway) simplifies the process. The Desktop application (new in 1.9) lets developers run Langflow locally without any server setup, which is useful for prototyping before deploying to production.
Agent and Tool Support
Building AI agents that can call tools and take actions is where these platforms are heading in 2026.
Dify introduced a Supervisor agent mode that coordinates multiple sub-agents for complex tasks. It supports parallel tool execution, tool-call retry logic, and MCP server connections. You can connect agents to GitHub, Slack, databases, file systems, and web browsers through MCP.
Flowise uses LangChain's agent framework with visual configuration. Agent nodes can call tools, search the web, query databases, and run code. The AgentFlow SDK in v3.1.0 added programmatic control over agent flows. RLHF (Reinforcement Learning from Human Feedback) pipelines let you fine-tune agent behavior based on user feedback.
Langflow leverages LangGraph for multi-step agent workflows with state management. The Python-native approach means you can write custom agent logic that goes beyond what the visual builder supports. MCP-based interoperability in v1.9 allows external agents and IDEs to create and execute Langflow flows programmatically.
Community and Ecosystem
|
Dify |
Flowise |
Langflow |
| GitHub stars |
100K+ |
36K+ |
100K+ |
| Backing |
VC-funded startup |
Independent/community |
DataStax (enterprise) |
| Plugin ecosystem |
Growing marketplace |
LangChain components |
LangChain + custom Python |
| Documentation |
Comprehensive |
Good, concise |
Comprehensive |
| Community activity |
Very active (daily commits) |
Active |
Very active |
| Enterprise support |
Available |
Limited |
Available (DataStax) |
Dify and Langflow lead in raw community size. Flowise has a smaller but dedicated community. For enterprise buyers, Langflow's DataStax backing and Dify's growing enterprise tier provide the most structured support options.
Integration with Existing Business Systems
If you are building AI tools for a service business, integration with your existing stack matters. All three platforms connect to CRMs, databases, and communication tools, but the approach differs.
Dify's API-first design means you build an AI app in Dify and expose it as an API that your other tools call. This works well when you want to embed AI capabilities into existing workflows. If you are already running systems like the ones we cover in our tech stack breakdown for service businesses, Dify slots in as the AI layer.
Flowise integrates directly through its 150+ tool nodes. Need to query PostgreSQL, hit a REST API, or send a Slack message? There is a node for that. For businesses working on streamlining their operations, Flowise's node library covers most common integration scenarios without custom code.
Langflow's Python-native approach means any Python library is available inside your flows. If your integration requires custom logic, API authentication, data transformation, or error handling beyond what pre-built nodes offer, Langflow gives you the most flexibility. This matters most for teams scaling complex operations where the AI component needs to interact with multiple internal systems.
When Each Platform Wins
Choose Dify When:
- Your team includes non-technical operators who need to build and manage AI apps
- You want built-in RAG with knowledge base management (no separate vector database to configure)
- You need conversation analytics, prompt versioning, and A/B testing out of the box
- You are building customer-facing AI products that need a polished API layer
- You are willing to run a heavier infrastructure stack in exchange for more features
Choose Flowise When:
- You want the fastest path from zero to a working AI tool
- Self-hosting simplicity is a priority (single process, 1 GB RAM)
- Your use case is primarily chatbots, document Q&A, or straightforward agent workflows
- Budget is tight and you want the lowest-cost hosted option ($35/month starter)
- Your team is familiar with LangChain concepts and wants a visual interface for them
- You do not need built-in analytics or prompt management
Choose Langflow When:
- Your team writes Python and wants full control over component logic
- You need custom processing at every stage of your AI pipeline
- You are building production systems that will grow in complexity over time
- Enterprise support and long-term platform stability matter (DataStax backing)
- You want to use LangGraph for advanced agent state management
- You need a desktop application for local development and prototyping
The Bottom Line
There is no single best platform. The right choice depends on who is building, what they are building, and how much control they need.
For most service businesses getting started with AI tools, we recommend Dify if you want a complete platform or Flowise if you want speed and simplicity. Langflow is the right call when your technical team needs the flexibility that only real code provides.
The platforms are converging in features but diverging in philosophy. Dify is becoming more of a product platform. Flowise is staying lean and LangChain-native. Langflow is pushing deeper into developer tooling and enterprise readiness.
Pick the one that matches your team's skills and your project's complexity. Start with a single use case, deploy it to production, and expand from there. The worst outcome is spending weeks evaluating platforms instead of shipping something that works.
Building AI-powered tools for your business? We help service companies choose the right platforms and implement them. Book a discovery call and we'll scope it together.