Dify vs Flowise vs Langflow

Dify

Freemium/Subscription + Open Source self-hosted · From Free (Sandbox); self-hosted Community edition also free

Best for: Teams that want an all-in-one, model-agnostic LLM app platform combining workflow building, retrieval-augmented generation, and agents without stitching together separate tools.

Flowise

Freemium (free open-source self-hosting plus a paid Flowise Cloud with free, Starter, Pro, and Enterprise tiers) · From Free (self-hosted or Cloud Free plan); paid Cloud plans start at $35/month

Best for: Developers who want the broadest catalog of LLM, embedding, and vector store integrations, plus human-in-the-loop multi-agent flows.

Langflow

Open Source · From Free (self-hosted, MIT license); Langflow Cloud from about $25/month

Best for: Developers who want to prototype visually and then export clean Python code, backed by a large community and IBM's long-term support.

Dify

Flowise

Langflow

Dify

Pros

• Genuinely open-source and self-hostable core, avoiding vendor lock-in
• Model-agnostic support across all major LLM providers
• Integrated RAG and agent stack reduces the need to stitch together separate tools
• Accessible visual builder usable by both developers and less technical team members

Cons

• Cloud plan message-credit limits can be restrictive for high-volume production apps
• Open-source license includes restrictions on certain multi-tenant SaaS resale use cases
• Enterprise-grade governance features require custom, quote-based pricing
• Younger company (founded 2023) with a smaller support ecosystem than established players like LangChain

Flowise

Pros

• Free and open source under the permissive Apache 2.0 license, with no-cost self-hosting available on any infrastructure.
• Visual, drag-and-drop interface lowers the barrier to building RAG chatbots and multi-agent systems compared to writing LangChain code by hand.
• Broad integration coverage across more than 100 LLMs, embedding models, and vector databases.
• Supports both simple chatbots and more complex, coordinated multi-agent workflows with human-in-the-loop review.

Cons

• Flowise Cloud subscription prices do not include LLM token usage, so real production costs can run well above the listed plan price once model API bills are added.
• Self-hosting requires comfort with Docker and cloud infrastructure, which adds setup and maintenance overhead for non-technical teams.
• Enterprise pricing is not published and must be negotiated directly with sales, making budgeting harder for larger deployments.
• As a very small, now Workday-owned team historically, long-term open-source roadmap priorities may increasingly reflect Workday's enterprise product needs rather than the broader community's.

Langflow

Pros

• Fully open source and free under the MIT license, with no forced paid tier for core functionality
• Visual canvas makes RAG and agent prototyping accessible to non-expert developers
• Ability to export to raw Python code bridges the gap between prototyping and production
• Large and fast-growing GitHub community with over 150,000 stars
• Backed by IBM's resources following the DataStax acquisition, aiding long-term maintenance

Cons

• Real-world costs still come from LLM API usage, vector databases, and infrastructure, which are not included in the free software
• Self-hosted deployments require managing servers, scaling, and monitoring without a managed cloud plan
• Visual flows can become difficult to manage for very complex, large-scale agent architectures
• Ownership changes (Logspace to DataStax to IBM) may raise questions about long-term product direction for some adopters
• Langflow Cloud does not publish a detailed public pricing page beyond a reported starting price

Dify

• Internal knowledge-base chatbots — Teams build RAG-based assistants that answer questions grounded in private company documents.
• Customer support automation — Companies build AI agents and chatbots that handle customer inquiries using Dify's workflow builder.
• AI SaaS product development — Independent developers and startups build and ship AI-powered SaaS products on top of Dify's orchestration layer.

Flowise

• Rapid RAG chatbot prototyping — Developers connect a document loader, vector store, and chat model on the Flowise canvas to stand up a retrieval-augmented chatbot over internal knowledge bases in minutes rather than writing custom LangChain code.
• Internal enterprise AI assistants — Product and IT teams use Flowise's Chatflows and Agentflows to build customer-support bots, internal knowledge assistants, and multi-step business process agents that plug into existing enterprise data sources.
• Governed, large-scale agent deployment — Large organizations use Flowise's Enterprise tier, with SSO/SAML, RBAC, audit logging, and on-premises deployment, to design, launch, and govern AI agents at scale across regulated business units.

Langflow

• Rapid RAG chatbot prototyping — Developers visually assemble retrieval-augmented generation pipelines to quickly prototype chatbots grounded in custom data.
• Multi-agent workflow design — Teams build coordinated multi-agent systems where specialized agents handle different parts of a complex task.
• Low-code to production handoff — Organizations prototype AI workflows visually, then export the generated Python code for hardening into a production application.