Sold by: IntuzÂ
Deployed on AWS
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AWS Free Tier
This production-ready AMI delivers a pre-configured stack featuring Qdrant vector database, LangChain, LlamaIndex, and MinIO. Designed by Intuz for AI developers building RAG, semantic search, and ML-powered applications on AWS.
Overview
This is a repackaged open-source software product. Intuz brings you the Qdrant Vector Database AI Stack AMI, a fully configured, production-ready environment tailored for AI developers and data scientists working with vector search, Retrieval-Augmented Generation (RAG), and semantic search.
Built on Ubuntu 22.04 and optimized for AWS, the stack includes Qdrant (vector database), LangChain, LlamaIndex, and MinIO for object storage-along with JupyterLab for experimentation, Docker for container management, and Python libraries such as PyTorch, TensorFlow, Transformers, and Scikit-learn.
Whether you're building recommendation engines, intelligent document retrieval systems, or LLM-powered agents, this AMI saves you hours of setup and configuration. It comes with pre-installed examples, scripts, and notebooks to help you get started quickly.
Ideal for startups, research teams, and enterprises looking for scalable, self-hosted, and cost-effective vector database solutions with full control and flexibility.
Highlights
- One-Click AWS Deployment: Instantly launch a production-ready AI stack with Qdrant, LangChain, and MinIO on secure, high-performance infrastructure.
- Ready for RAG and LLM Apps: Pre-installed tools and frameworks for building Retrieval-Augmented Generation pipelines, semantic search, and AI agents.
- Pre-Configured Dev Environment: Includes JupyterLab, Docker, Python virtual environments, sample notebooks, and API integration examples for fast prototyping.
Details
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Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 22.04
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
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Pricing
Free trial
Try this product free for 5 days according to the free trial terms set by the vendor. Usage-based pricing is in effect for usage beyond the free trial terms. Your free trial gets automatically converted to a paid subscription when the trial ends, but may be canceled any time before that.
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. Alternatively, you can pay upfront for a contract, which typically covers your anticipated usage for the contract duration. Any usage beyond contract will incur additional usage-based costs.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Cost/hour |
|---|---|
t3a.medium Recommended | $0.09 |
t2.micro AWS Free Tier | $0.09 |
t3.micro AWS Free Tier | $0.09 |
i7ie.metal-24xl | $0.09 |
m5dn.12xlarge | $0.09 |
g6e.16xlarge | $0.09 |
c5.9xlarge | $0.09 |
inf2.xlarge | $0.09 |
r7iz.16xlarge | $0.09 |
c5.12xlarge | $0.09 |
Vendor refund policy
Intuz will not refund money in any case. However, you can cancel your subscription any time.
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)Â .
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
Latest Stable Release
Additional details
Usage instructions
Once the instance is running, enter http://<instance-public-ip> in your browser after a few minutes to allow all services to start and health checks to pass.
You can access the following tools included in the stack:
Qdrant Dashboard: http://<instance-public-ip>/dashboard
JupyterLab: http://<instance-public-ip>:8888
MinIO Console: http://<instance-public-ip>:9001 Username: admin Password: minioadmin123
You can also access your instance via SSH using the default username ubuntu and your Amazon EC2 private key.
Support
Vendor support
We provide best-effort technical support for this product. We will do our best to respond to your questions within the next 24 hours in business days. For any technical support or query, fill up this form:
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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