AI Models Explanation: Open Source vs Closed AI APIs

Open-source model weights

Open-source models means that the company has published the trained model parameters ("weights"), allowing anyone to download and run the model on their own hardware. Examples like Ollama or vLLM offers with these weights an opportunity for a company to:

• Run the model locally or on your own GPU servers
• Fine-tune it with your company's data
• Deploy it inside your infrastructure
• Avoid paying per-token API costs

Fully Open AI is very rare because training datasets often contain copyrighted or licensed material. Materials in the question means releasing:

• Model architecture
• Training code
• Model weights
• Datasets (or enough information to recreate them)
• Training methodology

What Is Closed-source Models?

Closed-source models include accessing them only through an API or hosted service. Most common ones are:

• OpenAI GPT-4.1 / GPT-5
• Anthropic Claude
• Google Gemini

With closed-source models you cannot:

• Download the model
• Inspect the weights
• Run it on your own servers
• Modify it

Metaphorically speaking, comparing open-source models to closed AI APIs we can use a car example:

Closed AI (GPT, Claude): You can rent and drive the car, but you never own it or look under the hood.

Open-source AI: You receive the entire car. You can keep it in your own garage, modify the engine, repaint it, replace parts, and drive it without paying the manufacturer for every trip.

Why businesses like open-source AI

For enterprise deployments, open-source models offer several advantages:

Open-source AI:

• Runs on your own GPUs
• Company data stays inside your network
• One-time infrastructure investment
• Can be fine-tuned for your business
• Full control over updates and deployment
• No vendor lock-in

Closed AI APIs:

• Runs in the provider's cloud
• Data leaves your infrastructure (subject to the provider's policies and configuration)
• Ongoing API/token costs
• Limited customization
• Vendor controls model updates
• Tied to a specific provider

This is why companies building Private AI platforms often use open-source models such as Llama, vLLM, or TGI: they can deploy them entirely inside a customer's infrastructure, keeping data under the customer's control while eliminating recurring API charges.

What does "Runs on your own GPUs" mean?

When you ask an AI model a question like: "Summarize this document" the model performs millions or even billions of calculations to generate the response. A GPU can process these calculations usually in 1–3 seconds.

So what does "Runs on your own GPUs" actually means?

It means the AI model is hosted on hardware that your organization owns or controls, instead of using a cloud provider's API.

The setup looks like this:

Employees
     │
     ▼
Company Chat Interface
     │
     ▼
Your AI Server
     │
     ▼
Your GPU(s)
     │
     ▼
Open-source AI Model

Everything stays within your company's infrastructure.

Why is this valuable?

Running AI on your own GPUs provides several benefits:

• Data privacy – Company documents remain inside your network.
• No per-token API charges – After purchasing the hardware, you don't pay for every AI request.
• Greater control – You choose when to update or fine-tune the model.
• Lower latency – Responses can be faster because they don't depend on an external API.

In simple terms, a GPU is the hardware that provides the computing power for AI. When someone says "served from your own GPUs" they mean the AI is running on your company's own AI hardware rather than on someone else's cloud servers.

Final Thoughts

Choosing between open-source and closed AI is about more than selecting a model, it is about deciding how much control, flexibility, and ownership your business needs. Closed AI solutions offer convenience and fast adoption, making them suitable for many general-purpose use cases, like fast Google search for information or an AI chat friend. Open-source AI, on the other hand, gives organizations the freedom to deploy models within their own infrastructure, protect sensitive data, customize AI for specific business needs, and eliminate recurring API costs.