Dify is a web-based platform for building and running LLM applications, including agentic workflows, retrieval-augmented generation (RAG) pipelines, and tool-using agents. Product teams, developers, and enterprise AI groups use it to go from prototype to production without rebuilding their stack. Workflows, knowledge bases, model providers, and plugins all live in one shared workspace. The same platform can run as a managed cloud service, inside a customer's VPC, or self-hosted, which matters to regulated industries.
What it does
Dify gives teams a visual canvas for designing, testing, publishing, and monitoring AI applications. A typical build connects a model from a supported provider, attaches a knowledge base built from company documents, adds tools or plugins, and chains everything together as a multi-step workflow or an agent. The finished app can then be published and monitored from the same workspace.
The problem Dify targets is the gap between an AI proof of concept and a maintainable production system. Without a platform like this, teams often write custom glue code for retrieval, prompt orchestration, model switching, and observability. Dify combines those layers into a low-code environment that non-specialists can use while engineers keep control over models and deployment.
The vendor has industry solution pages for banking and financial services, logistics and supply chain, pharma and life sciences, manufacturing, semiconductors, public sector, education, professional services and audit, and internet and media. Common use cases include customer-service retrieval across multiple backend systems, email triage, document Q&A, and invoice or contract auditing.
Key capabilities
- Workflow Studio: A node-based canvas for building multi-step AI workflows. Model calls, logic, retrieval, and tool steps are connected visually instead of hand-coded.
- Agent configuration: Agents can be set up with tools and memory, so apps can take actions and keep context rather than only answer single prompts.
- Knowledge Pipeline: Turns uploaded documents into an indexed knowledge base for RAG. Storage and document quotas depend on the plan.
- Plugin marketplace: Model providers and tools are distributed as plugins. The pricing page names OpenAI, Anthropic, Gemini, xAI, and Tongyi models, and users can plug in their own API keys.
- Publishing and monitoring: Built-in options for publishing apps, plus a monitoring dashboard for tracking them in production.
- Flexible deployment and compliance: Runs on Dify Cloud, in a VPC, or self-hosted. The site lists GDPR, SOC 2, and ISO 27001 badges.
Pricing
Dify has two tracks: Dify Cloud and Self-Hosted & Enterprise. Directory data indicates a free entry point, but the pricing page excerpt doesn't describe that tier's limits. On Dify Cloud, the Professional plan is aimed at independent developers and small teams and costs $590 per workspace per year when billed annually (annual billing is advertised as saving 17%). It includes:
- 5,000 message credits per month for trying hosted models. After the credits run out, users switch to their own API key.
- 1 team workspace and 3 team members
- 50 apps
- 500 knowledge documents and 5GB of knowledge data storage
- A limit of 100 knowledge requests per minute
The excerpt doesn't show self-hosted and Enterprise pricing, and those tiers appear to go through sales. Taxes are extra.
Pricing page: View pricing
Editorial review
Dify's main strength is that it covers the full path of an LLM app in one tool: orchestration, RAG, agents, model and plugin management, publishing, and monitoring. Being able to run the same platform in the cloud, in a VPC, or on your own infrastructure is a real advantage for banks, pharma companies, and public-sector teams with data-residency rules. The vendor's logistics case study says Maersk replaced its in-house AI platform with Dify Enterprise. The cited figures come from Maersk's own internal assessment: about 90 production apps, roughly 25,000 active users, and one app delivered 16x faster than on the earlier self-built platform.
There are trade-offs. Cloud limits on team members, apps, documents, and knowledge request rates mean growing teams will likely outgrow the Professional plan and need Enterprise, which isn't publicly priced. The bundled message credits are meant for experimenting, so production usage will usually run on your own model API keys and costs. Non-technical users can build simple apps on the visual canvas, but complex agentic workflows that span several backend systems still need engineering work on integrations, evaluation, and governance.
Dify fits best for product and platform teams that want a standard way to build internal AI assistants, RAG apps, and workflow automation across many use cases. It's especially strong for organizations that need a self-hosted or VPC deployment option. Teams that only need a single chatbot may find it more than they need.
