AnythingLLM vs Dify: pricing, features and which to choose

AnythingLLM is an AI workspace built by Mintplex Labs for document chat, retrieval-augmented generation, agents, embeddings, vector databases, permissions, and a developer API. Desktop runs on macOS, Windows, and Linux, while Docker supports servers and organizational deployments. The company presents it as local-first, with support for local inference and hosted model providers. The core repository is MIT licensed. Its audience includes people who want private document work, developers connecting several model services, and teams that need multi-user workspaces on owned infrastructure. The product is broader than a chat interface because it combines document collections, model configuration, agent tools, administration, and deployment options in one application rather than requiring separate services for each function.

A typical workflow starts with installing the desktop application or deploying Docker, selecting a local model or external provider, creating a workspace, and uploading documents. Users then ask questions against indexed content and receive answers with citations when retrieval finds relevant passages. Workspaces keep collections, instructions, and conversations separate. Agents can use tools, browse the web, call configured services, and run task-oriented workflows. Administrators can manage users, permissions, providers, widgets, and the developer API. The setup is flexible, although that flexibility shifts responsibility to the operator. Model selection, embedding configuration, vector storage, hardware, provider accounts, latency, retention, and token billing can all affect the result. Hosted services also introduce their own terms and data handling practices.

AnythingLLM’s main limitation is operational choice: performance and cost vary with hardware, model, embeddings, vectors, and external providers. Cloud users must supply an LLM API key, and hosted instances lack a GPU, which can affect CPU-bound embedding work. ChatGPT is a managed hosted assistant, Open WebUI centers on a configurable interface for local and remote models, and Dify focuses on visual application and workflow building. AnythingLLM is the better pick for document workspaces with local inference, self-hosting, and provider choice. Choose a managed assistant for faster setup, or a workflow builder when application orchestration matters more than document-centric deployment.

Dify is an application platform from LangGenius, founded in March 2023 according to the company. Its visual workflow canvas connects language models with business systems and covers application building, deployment, monitoring, and knowledge retrieval. LangGenius said in March 2026 that Dify ran on more than 1.4 million machines, reached users in more than 175 countries and regions, and had more than 2,000 teams and 280 enterprises. The announcement also reported a $30 million Series Pre-A led by HSG, with GL Ventures, Alt-Alpha Capital, 5Y Capital, Mizuho Leaguer Investment, and NYX Ventures participating. These figures describe company-reported scale and funding. Dify’s central appeal is control over the application layer, model connections, deployment location, and operational interface.

A typical Dify project begins with an app type such as Chatflow, Workflow, Agent, or text-generation application. The builder connects prompt and LLM nodes, conditional logic, code, HTTP requests, tools, and human-input steps on a canvas. Knowledge bases ingest documents, create embeddings, retrieve passages, and can rerank results for RAG responses. Teams add a provider key, test runs in the visual interface, inspect logs, annotate answers, and publish a web app or call the application through a REST API. Plugins and marketplace integrations add providers and tools. The API documentation covers chat messages, workflow execution, file uploads, conversations, feedback, annotations, knowledge bases, and streaming events. This makes Dify useful for prototypes that need a defined path toward production deployment.

Dify’s main trade-off is operational choice: Cloud reduces administration, while self-hosting puts upgrades, security, storage, and provider credentials under the customer’s control. Community use must comply with the Dify Open Source License, whose source-available terms can restrict some commercial deployments. Langflow and Flowise are closer visual builders, while n8n focuses on broad automation and event integration. Dify fits teams needing a model-flexible LLM application layer, RAG, application APIs, and cloud or self-hosted deployment. Choose n8n when AI is one step inside a wider business process, or Langflow when a developer-oriented orchestration canvas is the priority. Pick Dify if your team needs to build and operate AI applications with visible workflows, knowledge bases, and provider choice.