APrime's LLM Kit, Part 1: How to host your own private AI models in AWS

This is Part 1 of our 3 -part series on APrime's LLM Kit. Read Part 2 , Read Part 3 .

Welcome to APrime’s three-part series on hosting your own Large Language Models (LLMs) in Amazon Web Services (AWS) by using our free, open-source tools. Whether you are eager to leverage AI in your product but wary of sharing your data with third parties like OpenAI or are simply unsure where to begin with deploying AI, our tools and guides aim to help you get your AI model running in AWS as quickly as possible.

Our tools automatically handle the infrastructure setup and deployment of an AI model while also providing you with an API and UI to directly interface with your model, all without ever sending any data outside of your AWS environment.

Visit Part 2, our quickstart guide, to get up and running with default settings, and Part 3 for an in-depth walkthrough and detailed discussion of the scripts and Terraform modules.

Why Should You Host Your Own AI Models?

Many organizations are eager to leverage AI capabilities in their products but hesitate to share sensitive data with third-party providers like OpenAI. Self-hosting AI models in your own cloud provider, such as AWS, offers complete data privacy, cost control, and the ability to experiment with AI without needing specialized ML engineers on staff.

Why Did We Build This?

APrime’s customers in fintech and healthtech wanted to integrate AI into their products, but had serious concerns about data privacy. As we looked at the tooling available, we discovered a gap in the market: existing solutions like Ollama supported local hosting but lacked cloud deployment and multi-user support. At the same time, vendors were updating their privacy policies to begin training models on customer data, which prompted us to build our own solution and share it with the community.

APrime’s Free AI Hosting Kit

APrime provides open-source scripts and infrastructure files enabling teams to set up, deploy, and interact with an AI model within your own AWS account while keeping data isolated within your environment. The toolkit includes a default Hugging Face model and supports deployment of other open-source alternatives.

Access the tools on GitHub.

When should a Team Consider a Vendor-Provided AI Solution instead?

Self-hosting isn’t appropriate for every use case. You should consider a vendor-provided AI solution if:

Proprietary data privacy isn’t a concern.
You need computationally intensive generative tasks such as video or image analysis.
High usage-based costs aren’t a concern.
You expect sparse, pay-per-token usage patterns.
You need vendor-specific features that aren’t available elsewhere.
Your model requires internet access for query processing.

Why Should You Use Our Free AI Self-Hosting Tools?

Quick Experimentation and Testing

Single-click deployment enables rapid iteration and validation of ideas without the usual infrastructure complexity.

Data Privacy and Security

Your data remains within your AWS account with no third-party sharing. Our documentation includes teardown procedures for complete environment deletion when you’re done.

Setup Made Simple

No dedicated ML engineer required. Anyone with scripting knowledge and an AWS account can deploy models using our detailed guides, which cover every step.

Cost Control

Self-hosted solutions are much more economical compared to third-party services or managed platforms like AWS Bedrock. You control runtime and can select models matching your specific needs rather than paying for unnecessary computational capacity.

Get Started

The remaining posts in this series guide you through deployment using ECS, GPU instances, and Hugging Face’s Text Generation Inference service. Start with Part 2: Quickstart Guide.