Dify vs Langflow vs FastGPT
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Dify | Langflow | FastGPT |
|---|---|---|---|
| Pricing | Free self-hosted (Docker/K8s); Cloud: Free tier (limited resources); Pro: $59/mo; Team: $159/mo; Enterprise: custom | Free self-hosted (Docker/Desktop); Cloud: Free tier; Starter: $10/mo; Professional: $99/mo; Enterprise: custom | — |
| Ease of Use | Visual workflow editor, but steeper learning for complex LLM ops | Intuitive drag-and-drop flow builder with 200+ components, easier for non-developers | — |
| Deployment Options | Self-host (Docker/K8s) or cloud; deploy as chat UI or REST API | Self-host (Docker/Desktop) or Langflow Cloud; deploy as REST API | — |
| Key Integrations | OpenAI, Anthropic, Gemini, Grok, Azure OpenAI, Ollama, Hugging Face, Pinecone, Weaviate, Chroma | Airbyte, Anthropic, Amazon Bedrock, Azure OpenAI, Chroma, Composio, Datastax, Google Gemini, Groq, Hugging Face | — |
| Advanced Features | Agent builder with tools, RAG with dataset management, prompt versioning/A/B testing, model routing, RBAC, MCP bridge, SSO on paid tiers | Multi-agent support, MCP server builder, visual state flows, custom Python components, flow DevOps toolkit, 200+ pre-built components | — |
| Best For | Teams building production-grade LLM apps with ops and collaboration | Rapid prototyping, non-developers, and educational use | — |
Dify is the stronger choice for teams needing production-ready LLM app deployment with ops features like RBAC, prompt versioning, and multiple deployment options. Langflow excels for rapid prototyping and non-developers due to its simpler drag-and-drop interface and 200+ components. For serious business apps, pick Dify; for quick experimentation and education, Langflow.
Open-source enterprise AI agent builder with hybrid RAG for accurate knowledge retrieval.
Visit WebsiteFeature-by-feature
Dify and Langflow both provide visual builders for LLM applications, but Dify focuses on full LLMOps lifecycle management. Dify's visual workflow editor includes an Agent builder with tools, dataset management for RAG, prompt versioning with A/B testing, and model-agnostic routing across OpenAI, Anthropic, Gemini, Grok, etc. It also offers deployment as a chat UI or REST API, role-based access control (RBAC), SSO on paid tiers, multi-workspace isolation, and observability with logs and analytics. Langflow, by contrast, centers on a drag-and-drop flow builder with 200+ pre-built components and custom Python components. It supports multi-agent flows, MCP server builder, visual state flows, and a marketplace of community flows. Langflow's January 2026 release adds Langflow Assistant for AI-assisted component generation and Flow DevOps Toolkit. While Dify integrates with vector stores like Pinecone, Weaviate, and Chroma, Langflow adds Airbyte, Composio, and Cassandra. Both support major LLMs and self-hosting, but Dify's MCP bridge and RBAC make it more enterprise-ready.
Pricing compared
Dify offers free self-hosting (Docker/K8s) with cloud tiers: Free (limited resources), Pro ($59/mo), Team ($159/mo), and Enterprise (custom). Langflow also offers free self-hosting (Docker/Desktop) with cloud: Free, Starter ($10/mo), Professional ($99/mo), and Enterprise (custom). For small teams scaling up, Dify's Pro ($59) provides more advanced features like RBAC and SSO compared to Langflow's Starter ($10) but at a higher price. Langflow's free tier is more generous for prototyping, while Dify's paid tiers include operational features essential for production. Enterprise plans for both are custom, but Dify's self-hosted option is more robust with K8s support and role-based access control.
Who should pick which
- Solo founder building a customer support chatbotPick: Dify
Dify's app templates library and deployment as chat UI make it quick to launch, plus RBAC and observability help as you grow.
- Non-developer educator teaching RAG conceptsPick: Langflow
Langflow's intuitive drag-and-drop with 200+ components is easier for students to understand without coding.
- Enterprise team requiring SSO and role-based accessPick: Dify
Dify offers SSO on paid tiers and RBAC, which are critical for enterprise compliance.
- Prototyping AI features for a hackathonPick: Langflow
Langflow's pre-built components and fast flow builder enable quick iteration without heavy setup.
- Startup deploying multiple LLM apps for clientsPick: Dify
Dify's multi-workspace isolation, model routing, and API deployment support client-specific configurations at scale.
Benchmarks
| Metric | Dify | Langflow | FastGPT |
|---|---|---|---|
| GitHub stars | ~75kgithub.com/langgenius/dify | ~40kgithub.com/langflow-ai/langflow | ~22kgithub.com/labring/FastGPT |
| Built-in RAG | Yes multi-sourceDify docs | Partial component-basedrequires retriever config | Yes enterprise-docFastGPT docs |
| Self-host deploy time | ~10 mindocker-compose up | ~5 minpip install + run | ~15 mindocker-compose + mongo |
| Multi-tenant SaaS allowed | Nosource-available license clause | YesApache 2.0 | Yes with attributionFastGPT OSS license |
| Native observability | Built-inapp analytics panel | Basicnode-level traces | Basicworkflow logs |
Frequently Asked Questions
Can I self-host both Dify and Langflow?
Yes, both support self-hosting. Dify uses Docker and K8s; Langflow uses Docker and Desktop installers.
Which tool has more pre-built components?
Langflow offers 200+ pre-built components, while Dify focuses on a curated library of app templates.
Do both support RAG?
Yes. Dify has dataset management and integrated RAG with vector stores like Pinecone and Chroma. Langflow supports RAG via components and integrations with Chroma, Cassandra, etc.
Which is better for production?
Dify is more production-ready with RBAC, prompting versioning, observability, and multiple deployment options. Langflow is better for prototyping.
Can I deploy as a chat UI?
Dify can deploy as a hosted chat UI or REST API. Langflow deploys as a REST API only, but you can build a custom chat UI.
Which has more LLM integrations?
Dify supports OpenAI, Anthropic, Gemini, Grok, Azure OpenAI, Ollama, Hugging Face, Replicate, etc. Langflow supports Anthropic, Amazon Bedrock, Azure OpenAI, Google Gemini, Groq, Hugging Face, etc. Both are broad.
Is there a free cloud tier?
Both offer free cloud tiers with limited resources. Dify's free tier is more restrictive; Langflow's free tier is generous for prototyping.
Which tool is easier for non-developers?
Langflow's intuitive drag-and-drop flow builder with 200+ components is generally easier for non-developers. Dify has a steeper learning curve for complex operations.