Dify: AI Workflows, Knowledge Retrieval and Plan Limits
What does Dify build?
Dify provides tools for creating agents, chatbots and workflows using models and supplied knowledge. Its documentation describes web-app publishing, API integration, managed cloud access and a self-hosted Community Edition. The resulting application still needs an owner who defines its data, permissions and expected behavior.
Who should consider it?
Product teams prototyping a document assistant or structured AI workflow may benefit from a visual builder. It is less appropriate when the main requirement is editing an existing application codebase. Start with one bounded workflow instead of a general agent with access to every team system.
What are the pricing and capacity limits?
Dify documents a free cloud Sandbox. The pricing page inspected shows Professional at $590 per workspace per year, including 5,000 monthly message credits, three members and 50 apps. Model type affects credit consumption. Document counts, knowledge storage and trigger allowances are separate limits; your own model API keys can introduce additional provider charges.
What is a useful first application?
Build a synthetic employee-policy assistant from three short documents with explicit dates. Include one superseded policy and one unanswered question. Ask the app to identify the supporting source and date for each answer. Test retrieval separately from final wording so an indexing problem is not mistaken for a prompt problem.
What should you verify?
Check that obsolete policy text is not presented as current, unsupported questions remain unresolved and the app uses only permitted knowledge sources. Test empty inputs, long inputs and model-service errors. Record the complete cost of ingestion and repeated queries. A polished chat interface should not hide missing citations or contradictory documents.
What operating work remains?
Connected tools and plugins introduce their own data flows and permissions. Review every action before enabling writes to external systems. Self-hosting requires updates, backups and credential management; it does not automatically keep model requests local. Review applicable software terms before embedding or redistributing a hosted version.
Which alternatives should you compare?
Compare Langflow for a Python-oriented visual AI workflow. Compare n8n when the task is mainly connecting business systems and triggering actions. Use the same synthetic documents and failure cases to compare maintenance effort as well as initial setup.
Sources checked October 27, 2026: Official documentation · Pricing and access conditions.