Sold by: QuasiScience
Deployed on AWS
Your Own Private AI: Take back control of your AI strategy with our secure, flexible, and powerful self-hosted Large Language Model (LLM) solution.
Overview
Instead of relying on external APIs, you can deploy and manage state-of-the-art LLMs directly within your own infrastructure, giving you a competitive edge and peace of mind.
This solution is ideal for businesses that handle sensitive data, require deep customization, or need predictable performance and cost management.
Key Benefits:
- Unparalleled Data Security & Privacy: With our self-hosted solution, your data never leaves your environment. All prompts, responses, and proprietary information remain securely within your private cloud or on-premise infrastructure. You have complete control over who can access your data and how it's used, eliminating the risk of third-party data breaches or having your confidential information used to train external models.
Ultimate Flexibility & Customization: Break free from vendor lock-in and API limitations. Our solution allows you to:
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Fine-tune models on your specific, proprietary datasets to create a truly unique and powerful AI assistant tailored to your business needs.
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Choose from a wide range of open-source models to find the perfect balance between performance and resource requirements.
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Integrate seamlessly with your existing enterprise systems, databases, and workflows.
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Optimize performance for your specific workloads, controlling latency, throughput, and scalability without being subject to external rate limits or policy changes.
Product Features:
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Easy Deployment: Our pre-configured templates, documentation, and support simplify the deployment process, allowing you to get up and running in minutes on popular cloud platforms or on-premise.
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Model-Agnostic: Support for a wide variety of leading open-source LLMs, giving you the freedom to choose the best model for your application.
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Scalable Architecture: Designed to scale with your business, whether you need to run a single model for a small team or a cluster of models for a large enterprise.
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Enterprise-Grade Security: Implement your own security protocols and access controls, including end-to-end encryption, to ensure the highest level of protection for your AI applications.
Stop compromising on security and flexibility. Empower your team to innovate with the full power of AI on your own terms.
Highlights
- Fully customizable deployment
- Access many different Foundational Models
- Keep your data safe
Details
Sold by
Delivery method
Delivery option
64-bit (x86) Amazon Machine Image (AMI)
Latest version
Operating system
Ubuntu 24.04
Deployed on AWS
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Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Financing for AWS Marketplace purchases
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 |
|---|---|
p3.2xlarge Recommended | $15.00 |
g4ad.4xlarge | $15.00 |
c4.2xlarge | $10.00 |
g5.4xlarge | $15.00 |
t3.xlarge | $5.00 |
g4ad.xlarge | $15.00 |
c4.xlarge | $10.00 |
g5.xlarge | $15.00 |
t3.nano | $5.00 |
Dimensions summary
You pay by the hour for the AWS EC2 instance type you run this LLM server on. Pricing scales with the compute resources each instance provides. The general-purpose t3.nano and t3.xlarge options suit lighter workloads. The c4.xlarge and c4.2xlarge options add more CPU capacity. The g4ad.xlarge, g4ad.4xlarge, g5.xlarge, g5.4xlarge, and p3.2xlarge options include GPU acceleration for heavier model workloads. Larger instances within each family cost more per hour. You choose the instance type at launch based on your performance and budget needs, then pay only for the hours you run it.
Top-of-mind questions for buyers
The rate covers software running on the chosen AWS EC2 instance, billed per hour it runs. Stopped instances stop accruing software charges. You still pay any underlying AWS fees, such as storage for attached volumes, while the instance is stopped.
The g4ad, g5, and p3 options include GPU acceleration, which speeds up running larger language models. The t3 and c4 options rely on CPU only, suiting lighter workloads. You choose based on model size and response speed needs, then pay the hourly rate for that instance type.
Billing does not scale automatically. You select one instance type at launch and pay its hourly rate. To get more capacity, you launch a different, larger instance type. There is no automatic tier upgrade; the change is a manual choice you make when configuring the deployment.
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Vendor refund policy
Please refer to our refund policy for digital goods at: https://quasiscience.com/resources/returns-policy-digital/
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
Build for AWS Marketplace
Additional details
Usage instructions
Log in can be executed with the user ubuntu with the selected key file and using port 22. The backend and frontend are executed as services so they will restart automatically between reboots without interventions. For more insights please refer to the full guide at: https://quasiscience.com/resources/private-llm-server-docs
Resources
Vendor resources
Support
Vendor support
Access our documentation or contact our developers directly with the following links:
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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