> Source: https://meetrix.io/store/flowise/
> Markdown copy of that page. Cite the URL above, not this file.

[Store](https://meetrix.io/store/)  Flowise

Open-source AI agent builder

# Self-hosted Flowise AI agent builder on AWS

Build AI chatbots, RAG pipelines and agents by connecting nodes on a canvas. Flowise runs from a pre-configured AMI in your own AWS account, so your flows, API keys and conversation logs stay with you.

 [Launch on AWS](https://aws.amazon.com/marketplace/pp/prodview-qjh7ir2wpfdok)

-   Ready in 5-10 minutes
-   Bring your own LLM keys
-   No platform fee

![Flowise AI agent builder deployed on AWS by Meetrix](https://meetrix.io/assets/store/flowise/flowise.webp)

## What is Flowise?

Flowise is an Apache 2.0-licensed, low-code platform for building LLM applications with a visual drag-and-drop interface built on LangChain.js. You wire together a model, a data source, memory and an output, and Flowise handles the orchestration.

-   ### Visual canvas

    Build chatbots, RAG pipelines and agents without application code.

-   ### Many LLM providers

    OpenAI, Anthropic, Hugging Face, Cohere, Vertex AI, Azure OpenAI and Ollama.

-   ### API for every flow

    Each flow gets an endpoint you can call from any front end.

-   ### Embeddable chat widget

    Drop a chatbot into your website without building a UI.

-   ### Templates

    Clone official templates for chatbots, RAG and multi-agent flows.

-   ### Custom code nodes

    Extend a flow with code when drag-and-drop is not enough.

## What's in the Meetrix Flowise AMI

The AMI ships Flowise ready to use, with login and SSL handled at launch.

-   Flowise with a username and password set at launch
-   Security group rules for SSH, HTTP and HTTPS
-   Automatic SSL when your domain is hosted on Route 53
-   A certificate script for issuing SSL manually otherwise
-   A CloudFormation template for your own AWS account
-   Email support from Meetrix at aws@meetrix.io

## How to set up a self-hosted Flowise server

1.  ### Launch the stack

    Subscribe on AWS Marketplace and launch the CloudFormation stack with your domain and Flowise login.

2.  ### Point your domain at it

    After 5-10 minutes, create a DNS record for your domain with the PublicIp from the stack outputs.

3.  ### Sign in

    Open the DashboardUrl and log in with the Flowise username and password you set.

4.  ### Build your first flow

    Add your LLM API key, start from a template, and embed the chatbot or call its API.

## Deploy on AWS

### Flowise on AWS

An AMI on AWS Marketplace, launched into your own AWS account with CloudFormation or from EC2.

Deploys with

CloudFormation or EC2

Recommended size

t3a.medium

Ready in

5-10 minutes

#### Setup guides

-   [**Developer guide** Launch, DNS, SSL, troubleshoot](https://meetrix.io/blogs/flowise-developer-guide/)
-   [**Flowise on AWS Marketplace** Who it suits and how it compares](https://meetrix.io/blogs/flowise-aws-marketplace/)
-   [**Video walkthrough** Watch Flowise on YouTube](https://www.youtube.com/watch?v=6Za5bpbXQAE)

 [Launch on AWS](https://aws.amazon.com/marketplace/pp/prodview-qjh7ir2wpfdok)

## Flowise server ports

The vendor-recommended security group opens these ports.

| Service | Ports | Protocol |
| --- | --- | --- |
| HTTP and HTTPS | 80, 443 | TCP |
| SSH (administration) | 22 | TCP |

## Flowise vs Langflow vs n8n

|  | Flowise | Langflow | n8n |
| --- | --- | --- | --- |
| Licence | Apache 2.0 | MIT | Sustainable Use License |
| Free for commercial embedding | Yes | Yes | No, needs a paid agreement |
| Stack | Node.js, LangChain.js | Python | Node.js automation engine |
| Focus | RAG pipelines, chatbots, agents | LLM chains and agents | General workflow automation |

Pick by team language and by how you plan to use the tool commercially. [Read Flowise vs Langflow vs n8n →](https://meetrix.io/blogs/flowise-vs-langflow-vs-n8n/)

## Video: Flowise on AWS

## Flowise FAQ

What is Flowise used for?

Building LLM applications visually: AI chatbots, document Q&A, RAG pipelines and autonomous agents, by connecting nodes on a canvas.

How is Flowise different from LangChain?

LangChain is a library you use in code. Flowise is a visual interface built on LangChain concepts, which makes prototyping a chatbot or RAG pipeline faster.

Which LLM providers can I use?

OpenAI, Anthropic, Hugging Face, Cohere, Google Vertex AI, Azure OpenAI, Ollama for local models, and others. You bring your own API keys.

Do I need to code?

Not for most use cases. Chatbots, RAG pipelines and basic agents are drag-and-drop; custom code nodes are there if you need them.

Can end users chat with my flows?

Yes. Every flow has an API endpoint, and Flowise includes an embeddable chat widget for websites.

Which instance size should I choose?

t3a.medium is the recommended starting point on AWS.

How is SSL set up?

Automatically if your domain is hosted on Route 53. Otherwise SSH in and run sudo /root/certificate\_generate\_standalone.sh.

How do I upgrade?

Back up your flows and configuration, remove the old stack and launch the updated AMI from AWS Marketplace.

## Flowise guides and articles

### [Flowise vs Langflow vs n8n: Visual AI Workflow Builders](https://meetrix.io/blogs/flowise-vs-langflow-vs-n8n/)

Stacks, focus and the licensing detail most roundups skip.

### [Flowise on AWS Marketplace: AI Chatbots Without Code](https://meetrix.io/blogs/flowise-aws-marketplace/)

Build chatbots, RAG pipelines and agents on your own infrastructure.

### [Flowise: Developer Guide](https://meetrix.io/blogs/flowise-developer-guide/)

Launch the stack, set up DNS and SSL, and sign in.

## Need a hand with Flowise?

We build and run self-hosted AI infrastructure for teams every day, from RAG pipelines to model hosting. Tell us what you need.

[Contact us](https://meetrix.io/contact-us/)