Dify

What is Dify?

Dify is a generative AI application development platform designed for developers and business teams. Users can connect different large language models within a visual interface, create prompts, build chatbots and multi-step workflows, import corporate data to establish RAG knowledge bases, and then publish the applications as web pages or APIs.

It brings model invocation, knowledge retrieval, Agent tools, log monitoring, evaluation, and operational analysis together on a single platform, making it suitable for quickly testing AI products; it can also be deployed on one’s own infrastructure via the community version or the enterprise version.

Core functions

• Multiple application types:It supports Chatbot, text generation, Agents, Chatflow, and Workflow; you can choose based on whether dialogue memory, tool selection, or deterministic processes are required.
• Visual workflow:Complex logic can be orchestrated using LLMs, knowledge retrieval, conditional branching, iteration, code, HTTP, templates, and output nodes, with the possibility to debug the results of each step.
• RAG knowledge base:Import files or connect to external data sources to carry out parsing, segmentation, vectorization, retrieval, and rearrangement; the knowledge Pipeline allows for the reuse of standard processing workflows.
• Model management:It allows connection to OpenAI, Anthropic, Gemini, DeepSeek, Tongyi, as well as local models through plugins; it enables the configuration of inference, Embedding, and Rerank services, and supports multi-key load balancing.
• Agents and tools:The Agent can select plugin tools, MCP Servers, OpenAPI services, or published workflows based on the target, and proceed with further inference based on the results.
• Triggers and automation:External events, plans, or plugin triggers can initiate workflows, making it suitable for integrating AI processing into business systems.
• Publishing and APIs:The application can be released as a hosted Web App, an embedded page, or a backend API, and it provides interfaces for handling conversations, files, workflows, and logs.
• Monitoring and Operations:View call logs, tokens, delays, feedback, and exceptions, as well as connect to external monitoring platforms such as Langfuse and LangSmith.

Dify Cloud prices

Message credits are used for some of the models provided by the official team, and the amount deducted per response varies depending on the model; once these credits are exhausted, it is possible to use one’s own API key for those models.

The Token fees from model providers, as well as costs related to Embedding, Reranking, vector databases, object storage, and servers, may incur additional expenses.

When Professional reaches its storage or seat limit, an upgrade is usually required, rather than simply purchasing additional resources on a ad-hoc basis.

Community Edition, Licenses, and Deployment

Dify Community Edition can be hosted using Docker; its main repository is governed by the Dify Open Source License with certain conditions, which is based on Apache 2.0. These additional terms restrict the unauthorized use of its code for running multi-tenant SaaS applications similar to Dify, as well as prohibit the removal of the branding elements from the user interface.

Therefore, the fact that the source code is available and can be deployed internally free of charge does not mean that it can be used without any restrictions under any business model; if you intend to offer platform-based services to external customers or to modify the brand, you should read the current license and obtain authorization first.

Self-hosting requires the maintenance of components such as the Web interface, APIs, Workers, databases, Redis, vector storage, plugin Daemons, and file storage. Free software does not exempt one from the costs associated with model calls, GPUs, backups, security updates, and operational maintenance.

The enterprise version includes commercial licensing, professional support, and more comprehensive governance.

Dify Usage Tutorial

Complete a basic task.

1. Clarify the code tasks to be completed in Dify, the scope of the repository, and the acceptance criteria.
2. Connect to or import the test project, and first back up the current branch;
3. First, generate a plan using various application types, and then confirm the files that need to be modified;
4. Use visual workflows to make minor changes;
5. Run tests, static checks, and builds; unverified code is not accepted directly.
6. Manually check permissions, keys, dependencies, and handle exceptions before merging;

Create reusable professional workflows

1. Select a low-risk, real-world project as a template;
2. Fix the order of use for various application types, visual workflows, and the RAG knowledge base;
3. Record the environment, model, prompts, and failure conditions;
4. Set up manual approval for write, deploy, and delete actions;
5. Compare speed, cost, test pass rate, and the amount of rework;
6. Expand to a team or production environment only after stability has been verified;

Which users are it suitable for

• A team for rapidly developing enterprise Q&A, customer service, content, and data processing applications
• Developers who need to switch between multiple models and create a RAG knowledge base
• AI product teams that aim to involve business professionals in the configuration of workflows
• Enterprises that need privatization as well as proper management of logs and permissions

Safety and restrictions

• Knowledge base responses may still contain incorrect references, missing information, or unfounded conclusions; therefore, it is necessary to evaluate strategies for recall, rephrasing, referencing, and refusal to answer.
• The code, HTTP, plugins, and MCP components within workflows have access to external systems; it is necessary to use keys with the minimum required permissions, restrict the networks and tools in use, and implement approval processes for operations such as writing, deleting data, making payments, and sending information outward.
• Plugins can be installed from the Marketplace, GitHub, or local packages. Administrators should restrict permissions for installation and debugging, lock down specific versions, and review the code.
• Self-managed teams need to update images and dependencies promptly, ensure backups of databases and files, and should not expose the default deployment directly to the public internet.

Frequently Asked Questions

Is Dify free?

Cloud offers a free Sandbox option, and the Community Edition can be hosted freely; however, models, servers, and maintenance services are not free.

Is Dify completely open source?

The source code is available publicly, but it is licensed under a proprietary license with commercial restrictions; it is not a standard Apache 2.0 project without any conditions.

Can Dify connect to local models?

Services such as Ollama and vLLM can be connected through model plugins or compatible APIs; the actual performance depends on the models used and the hardware available.