Dify
Verified ProfileDify is an open-source LLM application platform for building AI agents, chatbots, RAG knowledge-base apps, workflows, model integrations, and production AI operations.
Open Source
Deployment
RAG
Knowledge Apps
Workflow
Agent Builder
Cloud
Hosted Option
Last synced June 10, 2026. Official data may change on the provider website.
Visual References
Official preview, product capability, and workflow context
Overview
Dify is an open-source platform for building LLM applications, AI agents, knowledge-base chatbots, RAG workflows, and internal AI tools with a visual product layer.
It is strongest for teams that want a practical bridge between no-code AI app building and developer-controlled deployment. Dify can run as a hosted cloud product or be self-hosted, which makes it useful for startups, agencies, internal tools teams, and AI product builders.
This profile summarizes the tool before users continue to the official website.
Ratings & Evaluation
Directory-level scoring for comparison
User Rating
4.7/ 5
★★★★★
Editor evaluation based on open-source availability, agent workflow depth, RAG support, and deployment flexibility
Editor Score
4.8/ 5
Agent Builder Fit94%
RAG Workflow93%
Deployment Flexibility96%
Non-technical Ease82%
Key Features
1
Visual LLM app builder
Create chatbots, assistants, completion apps, and workflow-based AI applications without starting from raw backend code.
2
Agent and workflow orchestration
Build multi-step workflows with model calls, conditions, tools, knowledge retrieval, and structured outputs.
3
Knowledge base and RAG
Connect documents and datasets to retrieval workflows for support bots, research assistants, and internal knowledge tools.
4
Model provider integrations
Use multiple LLM providers and model choices depending on cost, latency, context length, and output quality needs.
5
Observability and operations
Review prompts, logs, user interactions, evaluation signals, and production behavior for ongoing improvement.
6
Cloud and self-hosted options
Use hosted Dify for convenience or self-host when data control, customization, and infrastructure ownership matter.
Pros & Cons
Pros
- ✓ Strong balance of visual app building and developer-friendly deployment control
- ✓ Open-source option is useful for teams that need self-hosting or customization
- ✓ Good fit for RAG bots, internal AI tools, workflow apps, and agent prototypes
- ✓ Supports production-oriented iteration through logs, prompt management, and workflow testing
Limitations
- • Self-hosting still requires infrastructure, security, and model-cost management
- • Complex agent workflows can become hard to debug without clear design discipline
- • Non-technical users may still need developer help for integrations and deployment
- • Cloud plan limits, seats, and usage should be checked before committing a team workflow
Pricing Overview
Dify has an official or provider-controlled pricing/product page available for current plan review. The table below records public pricing signals where they could be confirmed.
| Plan | Price Signal | Best For | Notes |
|---|---|---|---|
| Official pricing page | Published by provider | Current purchase decision | Open the official pricing page to confirm plan names, limits, billing period, taxes, and region. |
Pricing rechecked on June 9, 2026. Amounts, billing periods, regional taxes, credits, and plan limits should be verified on the official pricing page before purchase.
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FAQ
Is Dify open source?+
Yes. Dify is available as an open-source LLM application platform, and it also offers hosted cloud plans.
What is Dify best for?+
Dify is best for building LLM apps, AI agents, knowledge-base chatbots, RAG workflows, and internal AI tools with a visual builder plus developer deployment options.
Does Dify require coding?+
Simple apps can be built visually, but custom integrations, self-hosting, security, and production workflows often benefit from developer support.
How should teams choose between cloud and self-hosted Dify?+
Use cloud for speed and lower maintenance; choose self-hosting when data control, customization, infrastructure ownership, or compliance requirements are more important.
