Sold by: AppXen
Deployed on AWS
Self-hosted visual AI builder for chatbots, RAG, and agent workflows, powered by Amazon Bedrock and running in your own AWS account.
Overview
Build chatbots, retrieval (RAG) pipelines, and multi-step AI agents with a drag-and-drop visual editor - running entirely in your own AWS account, with models served by Amazon Bedrock. Production-hardened from first boot: automatic HTTPS, PostgreSQL persistence, a firewall, and automatic security updates. Your flows, credentials, and data stay in your account - no per-seat fees.
Inference runs on Amazon Bedrock using an IAM role you attach at launch (the included CloudFormation template sets this up), so there are no LLM API keys to manage and no GPU to pay for. Requires Amazon Bedrock model access to be enabled in your account.
This is independent software packaged by AppXen and is not affiliated with or endorsed by the Flowise project.
Highlights
- Visual builder for chatbots, RAG, and agent workflows behind automatic TLS
- Amazon Bedrock models via an instance role - keyless, and no GPU required
- PostgreSQL persistence on a hardened Ubuntu 24.04 base
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04 LTS
Deployed on AWS
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Features and programs
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t3.small Recommended | $0.049 |
t3.medium | $0.049 |
Dimensions summary
You pay by the hour to run AppXen AI Builder, a drag-and-drop tool for building chatbots, RAG, and agents in your own AWS account. Pricing scales with the compute size you pick. The t3.medium option runs on a mid-size instance for heavier workloads. The t3.small option runs on a lighter instance for smaller workloads. Both bill per hour of runtime, so you pay only while the instance runs. Choose the size that matches your workload; the difference is the underlying compute capacity, not the software features.
Top-of-mind questions for buyers
Each option runs AppXen AI Builder on a specific AWS EC2 instance size. The t3.medium runs on a mid-size instance with more compute and memory for heavier workloads. The t3.small runs on a lighter instance for smaller workloads. You pick one size to run the software.
The hourly software charge applies only while the instance runs. If you stop or power off the instance, the software charge stops accruing. Note that AWS may still bill underlying storage fees for a stopped instance, but the software meter counts running time only.
This is usage-based, pay-as-you-go billing. You are charged per hour of instance runtime with no upfront commitment or minimum. The instance runs in your own AWS account, and you pay only while it is active. Stop the instance to stop the software charges.
appxen.ai
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Vendor refund policy
Refunds are available on a case-by-case basis. To request a refund, email support@appxen.ai within 30 days of the charge; we typically respond within 1 business day.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
64-bit (x86) Amazon Machine Image (AMI)
Amazon Machine Image (AMI)
An AMI is a virtual image that provides the information required to launch an instance. Amazon EC2 (Elastic Compute Cloud) instances are virtual servers on which you can run your applications and workloads, offering varying combinations of CPU, memory, storage, and networking resources. You can launch as many instances from as many different AMIs as you need.
Version release notes
Initial release. Flowise on a hardened Ubuntu 24.04 base with automatic HTTPS, PostgreSQL, and keyless Amazon Bedrock via an instance role.
Additional details
Usage instructions
Attach an instance role with bedrock:InvokeModel at launch and enable Anthropic Claude model access in the Bedrock console (one-time, per region). Wait about 2 minutes for first boot, then browse to https://<public-ip>/ and create your account. In a chatflow, add an AWS Bedrock node with an inference-profile model id (for example us.anthropic.claude-sonnet-4-6) and leave the credential empty. See https://appxen.ai/appliances/flowise .
Support
Vendor support
Email support@appxen.ai for setup help and troubleshooting. Best-effort email support during business hours. Documentation and launch guide:
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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