Dify vs LangFlow (May 2026): Side-by-Side Comparison
TL;DR: Dify wins for production LLM apps and RAG (1M+ deployed). LangFlow wins for LangChain-native prototyping and research. Both are self-hostable open source.
Spec comparison
| Spec | Dify | LangFlow |
|---|---|---|
| License | Apache 2.0 + commercial add-ons | MIT |
| Self-host | Yes (Docker, K8s) | Yes (Docker, K8s) |
| Cloud entry price | $59/mo (Pro) | $0 (DataStax tier free) |
| Stars on GitHub | ~75K | ~50K |
| Production deployments | 1M+ apps deployed | ~250K active flows |
| Backed by | Independent (LangGenius) | DataStax (LangChain ecosystem) |
| Workflow editor | Visual + code DSL | Visual (LangChain canvas) |
| RAG out of the box | Yes (vector store + embedding) | Yes (via LangChain components) |
| Best for | Production apps + RAG knowledge bases | LangChain prototyping + research |
Feature matrix
| Capability | Dify | LangFlow |
|---|---|---|
| Visual workflow builder | ✓ | ✓ |
| Self-host (open source) | ✓ | ✓ |
| Native RAG / knowledge base | ✓ | ~ |
| Built-in vector store | ✓ | ~ |
| API auto-deploy from flow | ✓ | ✓ |
| Embedded chat widget | ✓ | ✗ |
| Marketplace / templates | ✓ | ~ |
| LangChain ecosystem native | ~ | ✓ |
| Custom Python nodes | ✓ | ✓ |
| Multi-tenant SaaS deployment | ✓ | ~ |
| Built-in observability/logs | ✓ | ~ |
| A/B testing framework | ✓ | ✗ |
| User & team management | ✓ | ~ |
| Native MCP support | ✓ | ✓ |
| Cron / scheduled triggers | ✓ | ~ |
Cost analysis (deployment + hosting)
| Setup | Dify | LangFlow |
|---|---|---|
| Self-host on $20 VPS | $20/mo | $20/mo |
| Cloud Pro tier | $59/mo | Free (DataStax) or $25/mo |
| Production K8s (small) | ~$150/mo | ~$120/mo |
| Enterprise tier (SSO, audit) | $$$$ (commercial) | DataStax custom |
When Dify wins
Dify wins for shipping production LLM apps. The platform was designed from day one as a deployment target, not a notebook — it includes RAG knowledge bases, multi-tenant SaaS, embeddable chat widgets, user/team management, A/B testing, and observability all in the OSS distribution. The marketplace of pre-built apps (Dify reports 1M+ deployments) means most common patterns — internal Q&A bot, customer support assistant, document summarizer — are templates you fork rather than build. Self-hosting on Kubernetes is well-documented and battle-tested. The visual workflow editor is faster to ship in than LangFlow because the abstraction is "LLM app" not "LangChain DAG." For any product team trying to put a chatbot or RAG assistant in front of end users in under a month, Dify is the right default. The commercial enterprise tier handles SSO, audit, and advanced compliance.
When LangFlow wins
LangFlow wins for LangChain-native teams and research workflows. Every node maps directly to a LangChain class, so what you prototype visually translates to LangChain Python with no abstraction loss. If your team has already committed to the LangChain ecosystem — including LangSmith for observability — LangFlow is the visual layer on top. It is also better for research-style prototyping where you want to swap retrievers, rerankers, and chains rapidly without committing to a deployment shape. The MIT license is cleaner than Dify's split (Apache 2.0 + commercial), and DataStax backing means production hosting is one click away. For teams whose primary need is "a faster way to draft a LangChain pipeline," LangFlow wins. For teams whose primary need is "a deployed LLM product," Dify wins.
The common combination
Some teams prototype in LangFlow, then port the working flow to Dify (or to native LangChain Python) for production. The two platforms target different stages of the lifecycle. If you are routing LLM traffic from either platform through a provider-agnostic gateway, the Swfte router works as a single endpoint inside both. Many production deployments also pair Dify with n8n for the "wire LLM outputs into the rest of the business" layer.
How to choose
- Decide your end goal. Production LLM product? Dify. Research/prototype that may turn into LangChain Python? LangFlow.
- Audit the LangChain dependency. If you are committed to LangChain in production, LangFlow has lower abstraction friction.
- Estimate user-facing volume. Dify's multi-tenant SaaS deployment is more mature for 100+ users.
- Check the templates. Dify's marketplace will save you weeks if your use case has a template.
- Self-host first; only move to managed cloud once you have established your traffic profile.
- Pick a single workflow, ship it on each, measure time-to-first-deploy. The winner is whichever shipped faster.