Building AI applications without writing code used to mean accepting serious limitations. In 2026, Dify and Flowise both offer visual builders for LLM-powered apps — chatbots, RAG pipelines, AI agents, and workflow chains. They share a surface-level similarity, but underneath they’re designed for different audiences and different levels of complexity. Here’s what separates them.
Quick Verdict
- Choose Dify if you want an all-in-one platform for RAG, agents, and workflow orchestration with built-in monitoring and team features.
- Choose Flowise if you want a lightweight, drag-and-drop LangChain/LlamaIndex builder that you can self-host with minimal resources.
Pricing Compared
| Tier | Dify Cloud | Flowise Cloud |
|---|---|---|
| Free | 200 message credits/month | 100 predictions, 1 workflow |
| Starter | — | $35/month (unlimited predictions, 5 workflows) |
| Pro | $59/month (5,000 credits) | $65/month (unlimited, 10 workflows) |
| Team | $159/month (unlimited credits, 10 users) | $149/month (unlimited, 30 workflows) |
| Self-hosted | Free (open-source) | Free (open-source) |
Both are free to self-host, which is where most serious users end up. Cloud pricing is comparable at higher tiers, but the free tiers differ — Dify gives you 200 message credits (enough to test), while Flowise limits you to 100 predictions and a single workflow.
Note that “message credits” and “predictions” are counted differently, so direct price-per-use comparisons are tricky. Self-hosting eliminates this concern entirely — you pay only for compute and LLM API calls.
Architecture and Approach
Dify: The Integrated Platform
Dify positions itself as a complete LLM application development platform. It bundles:
- Prompt IDE — version, test, and iterate on prompts
- RAG pipeline — document upload, chunking, embedding, retrieval
- Agent framework — tool-use agents with function calling
- Workflow builder — multi-step chains with branching logic
- Monitoring — log every LLM call, track token usage and latency
- API publishing — expose any app as an API endpoint with one click
- Team collaboration — workspaces, roles, shared knowledge bases
Everything lives in one interface. You don’t need to stitch together separate tools for RAG, prompting, and deployment.
For a deeper look, read our Dify Review 2026.
Flowise: The Visual Chain Builder
Flowise started as a visual UI for LangChain and has expanded to support LlamaIndex components as well. The core idea is drag-and-drop: you pick nodes (LLMs, vector stores, tools, parsers), connect them on a canvas, and deploy the resulting chain as an API or chatbot embed.
Flowise is deliberately focused. It does chain/agent building well and leaves other concerns (monitoring, team management, advanced workflow orchestration) to external tools.
Features Head-to-Head
| Feature | Dify | Flowise |
|---|---|---|
| Visual builder | ★ Workflow canvas | ★ Drag-and-drop canvas |
| RAG (document Q&A) | ★ Built-in (upload, chunk, embed, query) | ★ Built-in (via LangChain/LlamaIndex nodes) |
| Supported LLMs | OpenAI, Anthropic, Google, Cohere, local (Ollama) | OpenAI, Anthropic, Google, Cohere, local (Ollama, LocalAI) |
| Vector stores | Qdrant, Weaviate, Pinecone, pgvector, Milvus | ★ 15+ (Pinecone, Weaviate, Chroma, Supabase, more) |
| Agent mode | ★ Function calling + ReAct | ★ LangChain agents (various types) |
| Multi-step workflows | ★ Native (branches, loops, conditions) | Basic (sequential chains) |
| Prompt management | ★ Versioned prompt IDE | In-node configuration |
| Monitoring/observability | ★ Built-in (logs, traces, usage) | External (LangSmith, Langfuse) |
| API deployment | ★ One-click | ★ One-click |
| Embed as chatbot | ★ Yes (iframe/widget) | ★ Yes (iframe/widget) |
| Team/workspace | ★ Yes (roles, sharing) | Basic |
| Marketplace/templates | ★ Community templates | ★ Community templates (Flowise Marketplace) |
RAG Capabilities
Both handle the core RAG workflow: upload documents, chunk, embed, query. The difference is in control.
Dify provides an integrated knowledge base manager where you upload files, configure chunking and embedding, and attach knowledge bases to apps. Flowise exposes individual LangChain/LlamaIndex components as nodes — you wire together your document loader, splitter, embedding model, and vector store manually. More flexible, but requires understanding how the pieces interact.
Workflow Orchestration
This is where Dify pulls ahead significantly. Dify’s workflow builder supports:
- Conditional branches (if/else based on LLM output or variables)
- Loops and iteration
- Parallel execution
- Human-in-the-loop approval steps
- Variable passing between steps
- Code execution nodes (Python/JavaScript)
Flowise’s flows are primarily sequential chains. You can build tool-using agents, but complex multi-branch workflows with conditional logic are harder to express. Simple RAG chatbot? Flowise handles it. Multi-step pipeline with branching? Dify is the better fit.
Self-Hosting
| Dify | Flowise | |
|---|---|---|
| Docker | ★ docker-compose (multi-container) | ★ Single container |
| Minimum RAM | ~2GB | ~512MB |
| Database | PostgreSQL + Redis | SQLite or PostgreSQL |
| Dependencies | More (web, API, worker, db, redis, sandbox) | Fewer (single Node.js app) |
| Setup time | 10-15 minutes | 5 minutes |
| npx quick start | No | ★ npx flowise start |
Flowise is dramatically easier to self-host. A single npx flowise start command gets you running with SQLite. Dify requires docker-compose with multiple services (web frontend, API server, worker, PostgreSQL, Redis, and a sandbox environment). Both work reliably once set up, but Flowise’s minimal footprint makes it better for quick experiments and edge deployments.
Community and Maturity
| Dify | Flowise | |
|---|---|---|
| GitHub stars | 90,000+ | 35,000+ |
| Founded | 2023 | 2023 |
| Release frequency | Weekly | Bi-weekly |
| Discord community | ★ Very active | ★ Active |
| Documentation | ★ Comprehensive | Good |
| Enterprise adoption | Growing (named customers) | Mostly individual/startup |
Both projects launched in 2023 and evolve quickly. Dify has more stars and a larger team, which shows in faster feature releases and better docs. Flowise benefits from its LangChain/LlamaIndex foundation — it automatically gains new integrations as those frameworks add them.
Who Should Choose What
Choose Dify if:
- You’re building production AI applications that need monitoring and team access
- Your workflows involve multi-step logic, not just simple Q&A chains
- You want prompt versioning and an integrated knowledge base manager
- You don’t mind a slightly heavier self-hosted deployment
Choose Flowise if:
- You want the fastest path from idea to working RAG chatbot
- Lightweight self-hosting on minimal infrastructure matters
- You’re familiar with LangChain/LlamaIndex and want a visual interface for those tools
- Your use case is a focused chatbot or agent, not a complex pipeline
For a look at how AI agent frameworks compare at the code level, see our CrewAI vs AutoGen 2026 breakdown.
The Verdict
Dify is the more complete platform. If you’re building AI features for a team or product, its integrated monitoring, workflow engine, and knowledge base management save you from cobbling together separate tools. Flowise is the faster, lighter option — ideal for solo developers, prototypes, and straightforward RAG applications where you don’t need the overhead of a full platform.
Start with what matches your complexity. A simple document Q&A bot? Flowise will have you running in minutes. A multi-agent workflow with conditional logic and production monitoring? Dify is worth the heavier setup.
Explore more → Dify Review 2026 | CrewAI vs AutoGen 2026