Spec comparison
| Spec | Dify | LangFlow |
|---|---|---|
| License | Apache 2.0 + commercial add-ons | MIT |
| Self-host | Yes (Docker, K8s) | Yes (Docker, K8s) |
| Cloud entry price | Free Sandbox; Professional $590 per year per workspace (billed annually) | No published price. DataStax removed its hosted Langflow from Astra on 9 April 2026; IBM lists a Langflow Cloud option without a price |
| Backed by | LangGenius | IBM (via its DataStax deal) |
| 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 the open-source edition | Software is free; you pay for your own infrastructure | Software is free; you pay for your own infrastructure |
| Hosted paid tier | Professional $590 per year per workspace; Team $1,590 per year (billed annually, per Dify's pricing page) | Not published. Hosted Langflow on DataStax Astra was removed on 9 April 2026; IBM lists Langflow Cloud without pricing |
| Enterprise tier (SSO, audit) | Custom (commercial) | Custom, via IBM; not published |
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 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 IBM now presents Langflow, with a Langflow Cloud option whose pricing is not published (DataStax’s own hosted Langflow was removed in April 2026). 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.
Frequently asked questions
Is Dify or LangFlow better for production AI apps?
Dify, for most teams. It is built as a deployment target for LLM apps and includes RAG, multi-tenant SaaS deployment, A/B testing, observability, and an embeddable chat widget out of the box. LangFlow is better suited for prototyping and research where the full LangChain ecosystem matters.
Which is more open source?
Both are open source. LangFlow is MIT-licensed; Dify is Apache 2.0 with some enterprise add-ons (SSO, audit logs) gated behind a commercial license. For most teams the OSS feature surface is enough; only enterprise deployments hit the gating.
Can I deploy Dify or LangFlow on my own infrastructure?
Yes for both. Dify ships a Docker Compose stack and a Helm chart for Kubernetes: most production deployments use the latter. LangFlow has similar deployment artifacts. Dify's docs are more thorough on multi-tenant deployment.
How does each integrate with the LangChain ecosystem?
LangFlow is a LangChain-native visual builder; every component maps to a LangChain class. If you already commit to LangChain, the path is direct. Dify uses its own abstraction layer. You can call LangChain inside custom Python nodes but the platform does not assume it.
Which is better for RAG knowledge bases?
Dify. The knowledge-base feature is first-class: upload documents, choose embeddings, configure retrieval, ship. LangFlow can do RAG but requires composing the components yourself. For a non-research RAG product, Dify is faster to ship.