Flowise vs Dify with Ollama: No-Code Local AI App Builders Compared

Flowise and Dify are two of the most capable no-code platforms for building AI applications with local models. Both connect to Ollama, both let you build RAG pipelines and chatbots visually, and both run self-hosted with no data leaving your infrastructure. But they have meaningfully different approaches and strengths — this guide covers what each does well, how to connect them to Ollama, and when to choose one over the other.

What They Are and Why They Matter

If you’ve used LangChain or LlamaIndex to build RAG applications, you know how much code is involved even for straightforward pipelines. Flowise and Dify abstract that complexity into visual builders — you drag and drop components (document loaders, vector stores, LLM nodes, chains) and connect them graphically rather than writing Python. The result is much faster iteration on application design, and lower barriers for building AI tools that non-developers can use.

Both tools support Ollama as an LLM backend, which means you can build sophisticated AI applications — chatbots with memory, RAG systems over your documents, multi-step agent workflows — without any cloud API keys and with all inference running locally.

Flowise: The Visual LangChain Builder

Flowise is essentially a visual interface for LangChain. Each node in Flowise corresponds to a LangChain component — document loaders, text splitters, vector stores, LLM chains, agents. If you’re familiar with LangChain concepts, Flowise will feel immediately intuitive. If you’re not, the visual representation is often clearer than reading LangChain documentation.

npx flowise start
# Or with Docker:
docker run -d -p 3000:3000 flowiseai/flowise

Open http://localhost:3000 to access the Flowise UI. To connect it to Ollama, add an Ollama node from the LLMs section. Set the base URL to http://localhost:11434 and select your model. That’s the only configuration needed — Flowise uses this Ollama connection wherever you wire the LLM node into your pipeline.

Flowise’s strengths:

Flexibility. Because it maps directly to LangChain, almost anything LangChain can do, Flowise can do visually. Complex multi-step chains, custom memory configurations, advanced retrieval strategies — all available. The extensive component library covers every serious LangChain use case.

Developer-friendly. Flowise exposes each completed chatflow as an API endpoint automatically — you can embed it in a web app or call it from a script without additional work. It also supports custom JavaScript code nodes for logic that doesn’t fit a pre-built component.

Active community. Flowise has a large community and extensive tutorials. If you hit an issue, someone has probably solved it and written about it.

Dify: The Application Builder

Dify takes a higher-level approach than Flowise. Where Flowise thinks in terms of LangChain components, Dify thinks in terms of application types: chatbots, text generators, agents, workflows. You choose an application type, configure it, and Dify handles the underlying implementation. The visual workflow builder is cleaner and more polished than Flowise, and the built-in prompt engineering tools are more developed.

git clone https://github.com/langgenius/dify.git
cd dify/docker
docker compose up -d

Access Dify at http://localhost after the containers start (takes a minute). To add Ollama as a model provider: go to Settings → Model Provider → Ollama → Add Model. Enter your model name (e.g. llama3.2) and the API endpoint (http://host.docker.internal:11434 from Docker, or http://localhost:11434 if running Dify natively). You can add multiple Ollama models and switch between them per application.

Dify’s strengths:

Polished UX. Dify is designed to produce applications that end users interact with directly, not just internal tools. The chat interface, user management, and conversation history features are more production-ready out of the box than Flowise’s.

Prompt engineering tooling. Dify has a built-in prompt editor with variable support, system prompt testing, and version management for prompts. For teams iterating heavily on prompt design, this is genuinely valuable.

Knowledge base management. Dify’s document management and chunking pipeline is more user-friendly than Flowise’s — you upload documents through a clean UI rather than configuring document loader nodes visually. For non-technical users managing a knowledge base, it’s significantly easier.

Building a RAG Chatbot in Flowise with Ollama

A basic RAG chatbot in Flowise involves connecting five node types: a document loader, a text splitter, an embedding model, a vector store, and a conversational retrieval chain backed by your Ollama model. The visual canvas makes the dataflow clear — documents flow into the splitter, get embedded, land in the vector store, and the retrieval chain queries the store before passing context to the LLM.

The key nodes to add in Flowise for a RAG setup:

1. PDF Loader or Text File node — loads your documents
2. Recursive Character Text Splitter — chunks documents into 1024-token pieces
3. Ollama Embeddings node — set to nomic-embed-text or mxbai-embed-large
4. In-Memory Vector Store or Chroma node — stores embeddings
5. Conversational Retrieval QA Chain — the main chain node
6. Ollama node — connected as the LLM for the chain
7. Buffer Memory node — for conversation history

Wire these together and click “Save Chatflow”. Flowise creates an API endpoint and a shareable chat UI automatically. You now have a working RAG chatbot backed by your local Ollama model — accessible via the Flowise UI or as an API call from any application.

Building a Knowledge Base App in Dify with Ollama

Dify’s knowledge base feature is where it really shines. Create a new Knowledge Base in Dify’s UI, upload your documents (PDFs, Word docs, Markdown, web pages), and Dify handles chunking and embedding automatically using your configured Ollama embeddings model. Then create a Chatbot application that references this knowledge base as its context source.

The configuration is mostly point-and-click: select the knowledge base, choose the retrieval strategy (semantic, full-text, or hybrid), set the number of references to retrieve, and pick your Ollama LLM for generation. Dify handles the retrieval augmentation prompt construction internally. For teams where non-technical members will manage the knowledge base — adding and removing documents, keeping content current — Dify’s clean UI is a meaningful advantage over Flowise’s more technical approach.

Dify also has a built-in annotation system that lets you mark good and bad responses, which feeds back into improving retrieval over time. This kind of feedback loop tooling is rare in self-hosted tools and is genuinely useful for production knowledge base applications where quality improves with use.

Which Should You Choose?

The decision is cleaner than it might seem once you know your use case. Choose Flowise if you’re a developer building custom AI pipelines, want the full flexibility of LangChain, need a simple one-container setup, or are building something that will be consumed via API rather than used directly through the Flowise UI. Choose Dify if you’re building applications that non-technical users will use directly, need polished knowledge base management, want better prompt engineering tooling, or are building a production-grade chatbot or assistant that will be the front-facing interface for a team or organisation. Both can be run on the same machine as Ollama with minimal overhead, and both are open source with active development. If you’re uncertain, Flowise is faster to get running and easier to start experimenting with — try it first and move to Dify if you find you need its more polished user-facing features.

Performance and Hardware Considerations

Both Flowise and Dify add modest overhead on top of Ollama — the Docker containers themselves are lightweight (a few hundred MB RAM each). The performance bottleneck is always Ollama inference, not the application layer. On machines with 8–16GB RAM, running Ollama alongside either tool is practical as long as you’re not running multiple large models simultaneously. A typical setup with a 7B Ollama model, Flowise or Dify, and a ChromaDB vector store uses about 6–8GB RAM total — leaving adequate headroom on a 16GB machine. For teams with multiple users hitting the application simultaneously, the main scaling consideration is Ollama’s single-worker default — set OLLAMA_NUM_PARALLEL=2 or higher to handle concurrent requests without queuing. Both Flowise and Dify handle their own request queueing and can manage multiple concurrent users cleanly; the bottleneck at scale is always the inference layer.

The Broader No-Code Local AI Landscape

Flowise and Dify are the most mature self-hosted options, but the landscape is growing. AnythingLLM is a simpler all-in-one option that combines document management, vector storage, and a chat UI in a single container — worth knowing about if you want the simplest possible setup at the cost of customisation. LibreChat is another alternative focused specifically on the chat interface, supporting Ollama as a backend with a polished multi-user interface. The common thread across all of these is that the local AI application layer is maturing rapidly — the gap between self-hosted tools and polished cloud products has closed significantly in the past two years, and the remaining gap is primarily in integration breadth (cloud products connect to more third-party services) rather than core capability. For teams with privacy requirements or significant inference volume, the economics and privacy guarantees of self-hosted Ollama-backed applications have become genuinely compelling.