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Dify

Open Source

About Dify

Dify is a platform for building production-ready agentic workflows. It allows building agentic workflows and RAG pipelines with AI model and tool support in a collaborative workspace. It can be deployed on cloud, VPC, or self-hosted infrastructure. Users define how an app thinks, retrieves data, makes decisions, uses tools, asks for human input, and completes tasks using visual building blocks and AI prompts. Apps can be published as hosted experiences, API endpoints, embeds, or MCP-compatible tools.

Products

  • Workflow Studio: A visual builder for agentic workflows.
  • Dify Enterprise: Self-hosted or VPC deployment built for procurement, security, and audit.
  • Community Edition: Open-source, self-deployable version using Docker.

Used for

  • Designing and deploying natural language processing pipelines
  • Building an agent by chatting or configuring it manually
  • Creating repeatable pipelines for files, websites, online documents, and drives
  • Installing model providers, tools, data sources, and MCP integrations from the Marketplace
  • Publishing apps as hosted experiences, API endpoints, embeds, or MCP-compatible tools

Who it is for

  • Independent developers and small teams building production AI apps.
  • Medium-sized teams needing collaboration and higher throughput.
  • Open-source enthusiasts, individual developers, and non-commercial projects.

From Dify's own website, read 2026-10-23. How we build listings.

How to evaluate LLM Infrastructure & APIs

This is how the CIOPages Research Team evaluates this category. It is not an assessment of Dify. It comes from our Generative AI & LLM Platforms buyer guide.

25%
Model Capability & Task Fit
Reasoning and instruction-following on your tasks (not public leaderboards), multimodal coverage (text, image, audio, video) where you need it, context-window length for your documents, breadth of the model menu (frontier, mid, small), and measured quality on a held-out set of your real prompts
20%
Deployment, Data Residency & Governance
Where inference physically runs (multi-tenant API, your VPC, on-prem, air-gapped), explicit no-training-on-your-data and retention commitments, regional residency, SOC 2 / ISO 27001 / HIPAA / FedRAMP and EU AI Act alignment, and DLP, content filtering, and full prompt/response audit logging
15%
Build & Customization Tooling
First-class retrieval-augmented generation and grounding, managed fine-tuning and (where relevant) continued pre-training, an integrated evaluation and regression-testing harness, prompt/version management, and native or tightly integrated vector search over your corpora
15%
Agentic & Orchestration Readiness
Reliable tool/function calling, support for open agent protocols (MCP, A2A), a managed agent runtime with memory and state, multi-agent orchestration, and guardrails — permissions, human-in-the-loop checkpoints, and step-level tracing for autonomous workflows
15%
Portability & Lock-in Risk
How model-agnostic the API is, whether multiple model families are reachable through one interface, the real cost of swapping providers (prompt, tool-schema, and eval rework), reliance on open standards, and whether you can take fine-tuned weights or your data with you on exit
10%
Cost, Throughput & Operability
Token economics at your projected volume, batch and provisioned-throughput options, prompt-caching support, rate limits and quota headroom for production, p95 latency under load, regional availability and uptime SLAs, and built-in usage, cost, and quality observability

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

dify.ai Pricing page
CategoryAI & ML Platforms
SubcategoryLLM Infrastructure & APIs

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