Dify — Build production-ready agentic workflows with drag-and-drop ease. Best for Teams building production-ready AI agents and workflows quickly, Developers who want a visual approach to orchestrate LLMs and tools, Organizations needing to connect AI to external systems via MCP. Free to start; paid plans from $59/mo.
Teams building production-ready AI agents and workflows quicklyDevelopers who want a visual approach to orchestrate LLMs and toolsOrganizations needing to connect AI to external systems via MCPRAG-based applications requiring easy data ingestion and retrievalRapid prototyping and deployment of AI applications
Not ideal for
Users who need full code-level control over every pipeline detailTeams requiring extensive customization beyond provided pluginsApplications with very specific or unsupported LLM providersSingle-purpose apps that don't need workflow orchestrationUsers preferring a fully managed cloud service without self-hosting options
A top pick for teams needing a visual, production-ready agentic workflow builder. Its native MCP integration and RAG pipelines stand out. Best for those who value rapid prototyping and deployment without heavy coding.
Dify hits the sweet spot for teams that want to move fast with AI agents and workflows without getting bogged down in infrastructure. Its drag-and-drop interface is genuinely intuitive, and the native MCP integration (supporting protocol 2025-03-26) is a forward-looking feature that simplifies connecting to external APIs and databases. The ability to publish workflows as universal MCP servers is also a unique capability for interoperability. However, if you need extreme customization or have very niche LLM requirements not covered by the supported models, you might find limitations. Compared to LangChain, Dify offers a more visual and managed experience, but you trade off some low-level control. Real-world usage: the RAG pipeline setup is straightforward, but heavy document processing may require tuning. For teams that want to iterate quickly and deploy AI features, Dify is a strong choice. Pass if you need purely custom code or are already deeply invested in another framework.
Skip Dify if Skip Dify if you need manual control over every low-level prompt and parameter or prefer coding from scratch over visual builders.
Latest from Dify
Updated 2 days ago
Across the latest 7 updates: 3 feature updates, 2 launches, 1 changelog entry and 1 news mention.
How likely is Dify to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
80
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Dify
Dify is an open-source agentic workflow builder designed for teams of any scale to develop, deploy, and manage autonomous agents, RAG pipelines, and AI applications. It provides a visual drag-and-drop interface to create sophisticated workflows in minutes, making AI accessible to both developers and non-technical users. Key features include native MCP integration for bridging external systems, RAG pipelines to get your data LLM-ready, and the ability to publish workflows as universal MCP servers. Dify also supports multiple global large language models (open-source and proprietary) and offers flexible publishing options with Backend-as-a-Service. Compared to other AI development platforms, Dify emphasizes ease of use and production readiness, with a strong community and marketplace for sharing and building upon existing creations.
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Key Features
Visual drag-and-drop workflow builder
Agentic workflow creation and management
RAG pipeline for data indexing and retrieval
Native MCP integration (HTTP-based)
Publish workflows as universal MCP servers
Support for multiple global LLMs (open-source and proprietary)
Backend-as-a-Service for easy deployment
Flexible publishing options (API, app, etc.)
Plugin system to expand capabilities
Observability and monitoring tools
Community marketplace for sharing workflows
Real-world workflow fit
Concrete scenarios for the personas Dify actually fits — and what changes day-one when you adopt it.
Developer building a RAG chatbot
You need to answer customer questions from internal documentation. Using Dify, you upload PDFs into the Knowledge Base, configure an OpenAI embedding model, and connect to a chat frontend via the Backend-as-a-Service API.
Outcome: Within a few hours, you have a working RAG chatbot deployed and accessible to customers, with full observability into queries and logs.
Business analyst automating email routing
Your support team receives hundreds of emails daily. You design a workflow that classifies emails by intent using a GPT model, then routes them to the appropriate Slack channel with a summary.
Outcome: Emails are automatically classified and assigned, reducing manual triage by 80% and improving response times.
Claude (Anthropic)Gemini (Google)Grok (xAI)Llama 2 (Meta)Azure OpenAIHugging Face modelsReplicate models
Limitations
Community edition has SSO and branding restrictions—read the Apache 2.0 carve-out license carefully before enterprise deployment. UI has grown complex; onboarding non-technical users still requires a training session. Workflow debugging is serviceable but less transparent than code. Community support is strongest in Chinese. Subject to cloud service message credits; exceeding monthly credits requires upgrading tiers.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
Free
Over 12 months
Effective monthly
Free
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Dify tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Sandbox
$0/mo
Ideal for
Hobbyists and solo developers exploring AI workflows with minimal usage (200 messages/month).
What this tier adds
Free entry point; limited to 200 message credits, 5 apps, and basic logs.
Professional
$59/mo (billed annually)
Ideal for
Independent developers and small teams needing higher throughput and priority processing.
What this tier adds
Adds 5,000 message credits, priority document processing, and unlimited log history vs. Sandbox.
Team
$159/mo (billed annually)
Ideal for
Medium-sized teams requiring collaboration, higher throughput, and SSO.
What this tier adds
Expands to 50 members, 10,000 message credits, top priority processing, and unlimited trigger events vs. Professional.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Professional plan at $59/mo includes 5,000 message credits; additional overages may apply.
•Team plan at $159/mo includes 10,000 message credits; unlimited trigger events but higher base price.
•Sandbox free plan limited to 200 message credits; beyond that you must upgrade to paid tiers.
•Self-hosted Community Edition has license restrictions that may require commercial licensing for certain enterprise uses.
Where the pricing makes sense
The company stage and team size where Dify's pricing actually pencils out — and where peers do it cheaper.
For independent developers and small teams, the Professional plan at $59/mo (annual) delivers 5,000 message credits and team collaboration. Medium-sized teams can scale to the Team plan at $159/mo with higher throughput. Competing platforms like Flowise charge similar but lack native MCP integration and enterprise compliance (SOC 2, ISO 27001). The free Sandbox tier (200 credits) lets you experiment risk-free, while the self-hosted Community Edition is free but with licensing caveats.
Setup time & first value
How long it actually takes to get something useful out of Dify — broken out by persona, not the marketing-page minute.
A developer can build and deploy a simple RAG chatbot in under an hour using the drag-and-drop builder and pre-built templates. Business analysts may need a half-day with a tutorial to learn the interface. The Sandbox cloud tier gets you started instantly without installation; the self-hosted Community Edition requires Docker setup (about 30 minutes for an experienced admin).
Switching to or from Dify
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From Flowise: Export workflows as JSON and re-import into Dify; may need to reconfigure LLM connections.
→From LangChain: Dify's visual builder can replicate multi-agent chains through its node-based design; porting logic may require manual refactoring.
→From custom code: Use Dify's API endpoints to replace custom endpoints; map your existing data sources to Knowledge Bases.
Migrating out
↗To LangChain: Export workflow logic as code; Dify supports raw JSON export that can be adapted.
↗To Flowise: Similar visual builder; may require reconfiguring integrations and node structures.
↗To custom deployment: Use Dify's public API to export knowledge bases and workflow configurations.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•2026-05-19: Dify launches Creator Center and Template Marketplace for publishing and discovering workflow templates with affiliate earnings.
•2026-05-13: Dify 1.14.1 moves workflows from built to continuously reused, integrating AI into enterprise processes.
•2026-03-10: Dify raises $30M in Series Pre-A funding to expand open-source community and enterprise adoption.
•2026-03-10: Dify releases v1.13.0 with Human Input node for human-in-the-loop workflow review.
