Choose Dify when
You are shipping an AI product and need RAG pipelines, model management, prompt versioning, usage visibility, and APIs your application can call in production.
View Dify hosting →Comparison · AI application platforms
Dify vs Langflow product platform versus flow prototyping.
Both let you assemble AI applications on a visual canvas, and both need 4 GB of memory and 1 vCPU as a floor on Darwa — noticeably more than the chat interfaces. The split is in intent: Dify is oriented toward shipping and operating a production AI product, Langflow toward composing and testing flows from reusable Python components.
- 4 GB
- Minimum memory, both
- Product
- Dify's orientation
- Prototyping
- Langflow's strength
- Python
- Langflow's extension path
Building the thing versus running the thing.
Both cover the same ground on paper. In practice one is better at getting an idea working and the other at keeping a product running.
Choose Langflow when
You want to compose and test agent flows quickly from drag-and-drop components, and drop into Python whenever a component does not exist yet.
View Langflow hosting →RAG pipelines
Dify's document ingestion, chunking, and retrieval are built as product features with a management interface around them.
View Dify hosting →Extensibility
Langflow is Python-native, so a custom component is a Python class. Dify favours configuration and its own extension points.
View Langflow hosting →Operating it
Dify carries more of the operational surface a live product needs: model keys, usage, and versioned prompts in one place.
View Dify hosting →Resources
Both want 4 GB memory and 1 vCPU minimum. Dify asks for 20 GB storage against Langflow's 10 GB, because ingested documents and vectors accumulate.
See plan resources →What actually changes between the two.
These two overlap more than most comparisons on this site. The differences are real but narrower.
Production readiness01
Dify is built around operating a deployed AI application. Langflow is built around composing and iterating on flows.
Python02
Langflow's components are Python and extending it means writing Python. Dify is more configuration-driven.
Document handling03
Dify's RAG tooling is more of a managed product feature; Langflow gives you the pieces to assemble.
Resource floor04
Both need 4 GB and 1 vCPU — double the chat interfaces. Neither is a candidate for the smallest plan.
Storage growth05
Dify's 20 GB against Langflow's 10 GB reflects ingested documents and vector data accumulating over time.
Overlap06
For a straightforward retrieval chatbot either will work. The choice matters as the application grows toward being a product.
Products, plans, and regional guidance.
Dify hostingManaged Dify with one-click deploy.→Langflow hostingManaged Langflow with one-click deploy.→n8n vs ZapierIf the job is workflow automation, not AI apps.→All AI agentsEvery supported agent application.→LibreChat vs Open WebUIIf you want a chat interface instead.→PricingPlans, terms, and renewal pricing.→
Answers before you deploy.
Is Dify or Langflow better?+
They aim at different stages. Dify suits shipping and operating a production AI application, with RAG pipelines, model management, and APIs as managed features. Langflow suits composing and testing agent flows from reusable Python components. Both need 4 GB memory and 1 vCPU minimum on Darwa.
Which is better for RAG?+
Dify, for most teams, because document ingestion, chunking, retrieval, and their management interface are built as product features. Langflow can build the same pipeline from components, which is more flexible and more assembly.
Can I write custom code in either?+
Langflow is Python-native: a custom component is a Python class, which is the fastest path when what you need does not exist yet. Dify leans on configuration and its own extension points rather than open-ended code.
How much memory do they need?+
Both need at least 4 GB of memory and 1 vCPU — double what the chat interfaces require. Dify asks for 20 GB of storage against Langflow's 10 GB. Neither fits the smallest plan, so budget for a Startup plan or higher.
Can either replace n8n?+
Only partially. Dify and Langflow are for AI application and flow building. n8n is general workflow automation across hundreds of business systems. Teams commonly run n8n for integrations and Dify or Langflow for the AI application itself.
Do they need a separate database?+
Both keep state that must persist, and Dify in particular accumulates documents and vector data. On Darwa the deployment includes persistent storage; pair with a managed database when the application's own data should outlive the instance.