Orchestrators
Dify
A visual, low-code platform for building LLM apps, RAG systems, and agents — self-hosted or in the cloud
Overview
What is Dify and what can it do?
Dify is a complete open-source LLMOps platform built around a visual workflow editor, letting teams assemble chatbots, RAG pipelines, and agents without writing extensive code for every step. Built-in retrieval-augmented generation and a knowledge base manager handle the data-connection side of the equation directly within the same interface, while prompt management and observability tools give teams visibility into how their application behaves once deployed. Finished applications can be published directly as an API endpoint or an embeddable chat widget, and the platform can be self-hosted for full control or run through Dify's managed cloud.
Visual workflow builder for chatbots, RAG apps, and agents
Built-in retrieval-augmented generation and knowledge base management
Publish applications directly as an API or an embeddable widget
Self-hosted or cloud deployment options
Prompt management and observability tooling built in
A marketplace of community-published plugins and templates
Pricing
Dify plans and pricing in 2026
Open Source
$0
Self-hosted with no functional limitations
Cloud Pro
$59/mo
Managed cloud hosting with usage limits and team features
Most popular
💡
Our take on pricing
Self-hosting the open-source edition is the clear choice for technical teams — it's free with no functional limitations, and a modest VPS can run it comfortably. Cloud Pro at $59/month is worth it specifically for teams that want managed hosting and don't want to maintain their own server infrastructure.
Evaluation
Dify pros and cons
Pros
- Genuinely fast to get a working LLM application running without deep coding
- Full self-hosting option gives complete control and data privacy
- Built-in RAG and knowledge base tools remove the need for separate infrastructure
- Publishing as an API or embeddable widget is genuinely convenient
- Active plugin marketplace keeps adding ready-made building blocks
Cons
- Highly custom or unusual logic can bump up against the visual editor's limits
- Deep customisation ultimately still requires writing code
- Cloud plan's usage limits can be restrictive for high-volume production apps
- Less flexible than a code-first framework for genuinely novel agent architectures
Latest updates
Dify news and recent changes
May 2026
Dify Agent Nodes released
Full agent nodes with tool access became available directly inside the visual workflow editor.
Mar 2026
Plugin marketplace launched
The community began publishing ready-made tools and application templates for others to reuse.
Verdict
Is Dify worth it in 2026?
Dify is an excellent choice for teams that want to move quickly from an idea to a working LLM application — chatbot, RAG system, or agent — without committing to a heavy, code-first framework. The combination of a visual editor, built-in RAG, and one-click publishing as an API or widget covers a genuinely large share of common use cases with minimal engineering overhead. Once requirements get sufficiently custom, you'll eventually need to write code regardless, but for the majority of standard LLM application patterns, Dify gets you there faster than almost anything else.
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Dify Review 2026: The Complete Guide to Visual LLM App Building
Dify's pitch is straightforward: most teams building an LLM application are solving a fairly common set of problems — chat interface, retrieval over a knowledge base, some agentic tool use — and shouldn't need to write a full code-first framework from scratch every time. This review examines how well that visual, low-code approach holds up against writing custom code directly.
A visual editor with real depth
Dify's workflow builder lets you assemble an application by connecting nodes — a model call, a retrieval step, a conditional branch, a tool invocation — into a working pipeline, then immediately test and publish it. Unlike some simpler visual tools, Dify's node system extends into genuine agent behaviour through dedicated Agent Nodes, giving tool access and reasoning capability without requiring a separate code-first agent framework layered on top.
Built-in RAG removes a common integration headache
A large share of LLM applications need to answer questions using an organisation's own documents, which typically requires setting up a vector database, an embedding pipeline, and a retrieval strategy — meaningful infrastructure work before you've built any actual application logic. Dify folds this directly into the platform: upload documents to a knowledge base, and retrieval is available as a node in your workflow without separate infrastructure to provision or maintain.
Who should use Dify?
Teams wanting to move fast from an idea to a deployed chatbot, RAG app, or simple agent get real speed advantages from the visual editor and built-in RAG.
Organisations needing data control benefit from the fully self-hostable open-source edition, which imposes no functional limitations compared to the cloud offering.
Teams with highly custom or unusual logic requirements will eventually need to write code regardless, and should evaluate whether a code-first framework might be more direct for their specific case.
Dify vs. Flowise and code-first frameworks
Flowise offers a similar visual, node-based approach but is more directly a layer over LangChain and LlamaIndex's underlying components, whereas Dify positions itself as a more complete, standalone LLMOps platform with its own built-in RAG and observability tooling. Compared to writing directly against LangChain or LlamaIndex in code, Dify trades some flexibility for significantly faster time-to-working-application on standard use cases.
Conclusion
Dify in 2026 remains one of the most practical ways to go from an LLM application idea to a deployed, working product without a heavy code-first framework. Its visual editor, built-in RAG, and one-click API/widget publishing cover the large majority of common application patterns efficiently, and the fully free self-hosting option makes it accessible to teams at any budget.
