Building the right AI workflow isn’t just about choosing the best model—it’s about how easily you can connect, test, and scale it. This benchmark compares Dify and Langflow, two open-source platforms that let developers and non-developers alike turn ideas into working AI systems without writing reams of code. If you’re evaluating which tool to invest in—whether for internal tools, customer-facing bots, or research prototypes—look beyond flashy demos. Pay attention to how deeply each platform supports real-world needs: Can you run it offline? Does it let you version your workflows? Can your team collaborate without friction? Are security and scalability built in, or bolted on? We’ve tested these platforms side by side to show you what matters when the rubber meets the road—not just what looks good on a slide.
| Feature | Dify | Langflow |
|---|---|---|
| Category | AI Development Platform | AI Development Platform |
| Open Source | Yes (Dify Open Source License) | Yes (Open Source) |
| Visual Workflow Builder | Drag-and-drop canvas with multi-step logic, parallel execution, conditional branching | Canvas-style drag-and-drop interface with reusable components |
| LLM Model Support | Hundreds including GPT, Mistral, Llama3, Claude, OpenAI-compatible, Ollama local models | llama-3.2, L3, Ollama, NVIDIA NIM, Hugging Face, OpenAI |
| RAG Capabilities | Document ingestion (PDF, PPT, TXT), vector embedding, hybrid search; integrates with Milvus, Weaviate, PostgreSQL | Supports Astra DB, MongoDB, Pinecone, Oracle AI Vector Search, Chroma |
| Agent Capabilities | Built-in agent system with 50+ tools, custom plugins, and tool orchestration | Supports single and fleet of agents with tool access, conversation management, retrieval |
| Model Control Protocol (MCP) | Native MCP support; can publish workflows as universal MCP servers | Built-in MCP server and client; turn flows into tools for external clients |
| Customization & Extensibility | Plugin marketplace; custom plugins supported; backend APIs for integration | Full Python access to modify or build custom components; extensible via code |
| Deployment Options | Cloud, Self-hosting (Docker, Kubernetes), Enterprise, AWS Marketplace, Alibaba Cloud, Elestio | Local, Docker, Desktop, Cloud (Enterprise), AWS, Kubernetes |
| Offline Capability | Yes (via self-hosting and local models) | Yes, full offline operation with local models and Ollama |
| Observability & Monitoring | Integrated with Langfuse for traces, metrics, prompt versioning, feedback loops | LangSmith, LangFuse integrations |
| API Deployment | Full RESTful APIs for all features; backend-as-a-service | Deploy flows as REST APIs |
| Interactive Playground | Prompt IDE with real-time testing | Yes, real-time testing with step-by-step debugging |
| Template Library | No explicit template library mentioned | Yes (travel agents, resume assistants, Notion expanders, RTX Remix) |
| Security Features | Enterprise-grade security, data isolation, secure API key management, compliance-ready | Enterprise-grade security, API key management, local execution for privacy, HTTPS via reverse proxy |
| Security Advisories | No public CVEs listed | CVE-2025-3248 (>=1.3), CVE-2025-57760 (>=1.5.1) |
| Multi-Tenancy | Yes (enterprise multi-tenant with logical isolation) | Not specified |
| Scalability | Scale-to-zero; handles thousands of concurrent users; Kubernetes-ready | Kubernetes and cloud deployment options; no explicit scaling metrics |
| GPU Acceleration | Via local models (Ollama), no explicit NVIDIA RTX mention | Full integration with NVIDIA RTX AI, RTX PRO GPUs, Ollama |
| Collaboration & Sharing | No explicit real-time collaboration mentioned | Share flows, real-time collaboration, deploy shared workflows |
| Version Control | No explicit mention | Yes, flows can be versioned and shared via GitHub |
| Export Formats | No explicit export format listed | JSON, Python code, API endpoint, MCP server |
| Cloud Pricing | Free tier (200 GPT-4 calls), pay-as-you-go, enterprise subscription | Free enterprise-grade cloud deployment available |
| Target Users | Developers, AI teams, enterprises, startups, citizen developers, non-technical users | AI developers, data scientists, no-code users, AI enthusiasts, NVIDIA RTX modders |
| Primary Use Cases | Enterprise Q&A Bots, AI Podcasts, Document Assistants, Customer Support, Marketing, Research Summarization | Chatbots, RAG, Multi-agent systems, Document analysis, Content generation, Local AI agents |
| Integration Ecosystem | Zapier, Slack, Notion, Google Workspace, TiDB, AWS Bedrock, Alibaba Tongyi | Oracle OCI, NVIDIA RTX, Hugging Face, AWS, LangSmith, LangFuse, MCP |
| Community & Support | Active GitHub, Discord, Twitter; GitHub Discussions, Issues, Email | Open source GitHub; active community; documentation available |
| Known Limitations | Limited Weaviate feature utilization; advanced deployments require Kubernetes/Helm knowledge; some plugins need external keys | Requires swap memory on EC2; installation issues on NixOS; pip OOM without swap |
Choose Dify if you’re building enterprise-grade AI applications that need robust scalability, secure multi-tenancy, and seamless backend integration—especially if you’re managing complex workflows for teams or customers. It’s the quieter, more polished engine for production systems where reliability and control matter more than flashy templates.
Choose Langflow if you’re tinkering at the edge—building local AI agents, experimenting with NVIDIA RTX hardware, or want to version, export, and share your flows like code. It’s the open workshop for developers who want to dig into Python, tweak components, and keep everything offline or on their own terms.
The difference isn’t just features—it’s philosophy. Dify puts structure around complexity so you can scale without chaos. Langflow gives you raw access so you can rebuild things your way. Pick the one that matches how you want to work, not just what you want to build.
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