Taking control of AI by running models on your own infrastructure
Open-source AI models are machine learning models whose code, weights, and architecture are publicly available. Unlike proprietary models locked behind APIs, open-source models give you complete control over how they're used, modified, and deployed.
These models are typically hosted on platforms like Hugging Face, where thousands of models are freely available for download and use. From language models like Llama and Mistral to embedding models and specialized task models, the open-source ecosystem is vast and growing.
The key advantage? You own the model. No API rate limits, no usage fees, no data leaving your infrastructure — just pure, unrestricted access to powerful AI capabilities.
Local inference means running AI models on your own infrastructure — whether that's a server in your office, a dedicated cloud instance in your own AWS, Azure, or GCP account, or even a powerful workstation. Instead of sending data to external APIs, the model processes everything within your controlled environment, giving you complete privacy and control.
Modern hardware and cloud infrastructure have made local inference more accessible than ever. With tools like Ollama, LM Studio, and optimized inference engines, you can run sophisticated models on consumer-grade hardware or dedicated cloud instances.
Local inference eliminates the need for constant internet connectivity to third-party APIs, removes per-request fees, and ensures your sensitive data never leaves your infrastructure. It's the foundation of truly private, cost-effective AI.
When we talk about "private AI," we don't just mean on-premise hardware. Private AI means your AI runs in your own isolated environment — whether that's completely on-premise or in your own dedicated cloud instance.
Think of it like a suite in a shared building: the building is the cloud provider (AWS, Azure, GCP), but your suite is completely private — only you have the keys. Your data, your models, your infrastructure, your rules.
Complete control with hardware in your own facility. Perfect for maximum security and compliance requirements.
Your AI deployed in your own AWS, Azure, or GCP account — an isolated environment within your cloud perimeter. Still completely private, still completely yours.
Both options give you true ownership and privacy. The difference? Corporate AI owns your data. Private AI runs inside your walls — whether those walls are physical or virtual.
Your data never leaves your infrastructure — whether that's on-premise or in your own dedicated cloud instance. No third-party APIs, no shared processing, no data sharing agreements.
Corporate AI owns your data. Private AI runs inside your walls. When deployed in your own cloud instance, your security policies (IAM, encryption, auditing, compliance) all still apply. Your data stays within your cloud perimeter, isolated and protected.
No per-request fees, no usage-based pricing, no surprise bills. Once you've set up your infrastructure, your costs are fixed and predictable.
Process as much data as you need, whenever you need it. No API throttling, no request limits, no waiting in queues.
Modify models to fit your specific needs. Fine-tune on your own data, adjust parameters, and create specialized versions for your use cases.
No vendor lock-in, no dependency on external services, no risk of API changes or shutdowns. Your AI infrastructure is truly yours.
At VERTEKS.AI, open-source models and private inference are at the core of our approach. We build systems that small businesses can own and control, not rent from Big Tech.
We can deploy your AI system either completely on-premise for maximum privacy, or in your own dedicated cloud instance (AWS, Azure, GCP) — like a private suite where only you have the keys. Either way, your data never leaves your infrastructure, your security policies apply, and you maintain complete control.
Our solutions leverage models from Hugging Face, run inference on infrastructure our clients control, and ensure complete data privacy. Whether it's embedding models for semantic search, language models for customer service, or specialized models for industry-specific tasks, we prioritize open-source solutions that give our clients true ownership.
This approach gives you the best of both worlds: you don't need to maintain on-premise hardware, but you still get enterprise-grade security and compliance. Your AI integrates directly with your databases, CMS, or ERP systems inside the same VPC (Virtual Private Cloud), and you can scale or pause compute whenever you want — all while keeping your data completely private.
This isn't just about technology — it's about democratizing AI. We build the system. You keep the keys. That's not just private — it's empowering.
Interested in deploying open-source models with local inference? Explore our Labs to see real-world implementations, or get in touch to discuss how we can help you build a private, cost-effective AI infrastructure.