Dify capabilities
9 mapped capabilities, each graded and dated. The map shows what Dify can do; the audit shows whether it’s worth consolidating — and a guide shows how to move.
Capabilities
Agent node with autonomous reasoning
provisionalverified ~2 months agoEmbeds an AI agent inside a workflow that can independently decide which tools to call and in what order to complete a subtask, without hardcoded branching.
App observability and monitoring
provisionalverified ~2 months agoBuilt-in analytics and logging that tracks how deployed AI applications are performing, including message volumes, latency, errors, and annotation review queues.
Flexible deployment options
provisionalverified ~2 months agoLets teams choose between Dify's managed cloud, a self-hosted open-source install, or enterprise-managed deployments on AWS or Azure, depending on their data residency and IT constraints.
Multi-model LLM support
provisionalverified ~2 months agoConnect any major cloud or self-hosted language model to power Dify workflows, letting teams swap or mix models across apps without rebuilding pipelines.
Plans and pricing
provisionalverified 26 days agoFour tiers from a free Sandbox up to Enterprise, priced per workspace per month, covering different team sizes, app counts, knowledge storage, and message credit allowances.
Plugin and tool integrations
provisionalverified ~2 months agoMarketplace of 800+ pre-built plugins that let AI workflows interact with external services such as search engines, vector databases, and automation platforms, with native MCP server publishing support.
RAG knowledge pipeline
provisionalverified ~2 months agoIngests and indexes enterprise documents so AI apps can retrieve relevant context at query time, enabling question-answering over internal data.
Security and compliance
provisionalverified ~2 months agoEnterprise-grade security certifications and runtime safeguards that let regulated teams deploy AI workflows while meeting data-protection obligations.