Flowise vs Dify: which to choose?

Flowise is closer to LangChain — ideal for developers who want to build complex LLM pipelines. Dify is more oriented toward the finished product, with user management, monitoring, and deployment. The two are complementary.

How much RAM for Flowise?

Flowise itself runs with 1 GB of RAM. In practice, 2 GB is recommended for comfortable use, 4 GB if you pair it with a local vector database.

Does Flowise support Ollama?

Yes. You can connect a local Ollama instance — on the same VPS or a separate server — and use any open-source model in your flows.

Can Flowise flows be exported?

Yes. Flows can be exported as JSON. You can save them, share them, or import them into another Flowise instance.

Automate business processes.

Flowise

Build LLM pipelines and AI chatbots with drag-and-drop — visual LangChain.

2 GB RAM 1 vCPU Port 3000 Available

Source code

Tech stack

DockerNode.jsSQLite / PostgreSQL

Minimum RAM2 GB

Minimum CPU1 vCPU

Default port3000

Compatible OSubuntu-24.04

Flowise is an open-source tool that lets you build LLM applications (chatbots, agents, RAG pipelines) through a visual interface based on LangChain. Without writing any code, you can chain components — LLMs, vector databases, document parsers, API tools — to create functional AI applications.

Self-hosted on your VPS, Flowise gives you access to the full power of LangChain without the complexity of Python code. It supports many LLMs (OpenAI, Anthropic, Mistral, Ollama) and vector databases (Pinecone, Chroma, Qdrant). Ideal for teams who want to prototype AI applications quickly.

Key features

Drag-and-drop interface based on LangChain

Support for 20+ LLMs and 10+ vector databases

Visual RAG: connect your documents in a few clicks

One-click API deployment and chatbot widget

Flow sharing between team members

Marketplace of ready-to-use templates

When to use this solution?

1

Document chatbot

Build, in a few hours, a chatbot able to answer questions about your internal PDFs.

2

Analysis pipeline

Automate the extraction of structured information from unstructured text (emails, contracts, reports).

3

Rapid AI prototype

Test complex LLM architectures without code before rewriting them cleanly in your application.

Deploy Flowise on your VPS

Guide optimized for ServOrbit Cloud VPS.

01

Deploy Flowise

VPS with 2 GB RAM minimum. Run docker run -d -p 3000:3000 flowiseai/flowise. Flowise starts with its built-in SQLite database.

02

Configure persistence

In production, mount a Docker volume at /root/.flowise to persist your flows and data across restarts.

03

Expose over HTTPS

Nginx reverse proxy + Certbot. Enable authentication in the environment variables (FLOWISE_USERNAME, FLOWISE_PASSWORD).

04

Create your first flow

In the editor, drag in an LLM, a conversation memory, and a prompt — connect them and test the chat in a few minutes.

Frequently asked questions

Flowise is an open-source tool that provides a visual interface for LangChain. It lets you build LLM applications (chatbots, RAG, agents) without writing any code.

Complementary solutions

Automation & Workflows

Activate Flowise on your infrastructure.

Dedicated Cloud VPS — IPv4 included, European datacenter, support included. Your data never leaves your server.

Recommended configuration: 2 GB RAM · 1 vCPU