A year ago, most of the AI projects we saw were using public APIs from major providers. That made sense: it was the fastest way to get started, and the quality was good.
But lately, we've noticed a clear shift. More and more companies—even smaller ones—are asking about running AI models on their own infrastructure.
Why is this happening? We see three main reasons.
1. Data privacy and compliance
This is the big one. When you send company data to a third-party AI API, that data leaves your infrastructure. Depending on your industry, this can create compliance issues.
Many organizations handle sensitive data: customer information, internal documents, financial data, proprietary research. When you use a public API, you're sending that data to someone else's servers. Even if the provider says they won't use it for training, it's still a risk many organizations aren't comfortable taking.
With a private deployment, your data never leaves your environment. That addresses a lot of compliance concerns right out of the gate.
2. Cost at scale
When you're only making a few thousand API calls a month, public APIs are cheap. But when you start scaling up to millions of calls, the token costs add up quickly.
For many use cases, after you factor in the ongoing API costs, running your own model on infrastructure you control ends up being cheaper—often significantly cheaper.
The upfront work is more, of course. But for companies that are using AI heavily, the math starts to favor self-hosting pretty quickly.
3. Control and reliability
When you rely on a public API, you're dependent on that provider. They can change their pricing, change their model, change their terms of service, or have outages.
When you run the model yourself, you control it. You know what model version you're running, you can performance test it, you can patch security issues when you want to, and you don't have to worry about sudden changes from the provider.
But is it more complicated?
Yes, it is. You have to think about model selection, infrastructure sizing, performance optimization, and ongoing maintenance.
But the good news is that it's gotten much easier over the past year. There are now great tools for deploying and serving open-source models. Model quality has gotten really good for many common use cases. You don't need a team of ML engineers to run this stuff anymore.
For many companies, the tradeoff is worth it. You do a bit more work up front, and you get privacy, cost predictability, and control in return.
Wrapping up
Public APIs aren't going away. They're still the best choice for many projects—especially when you're getting started or when you don't handle sensitive data.
But private AI deployments are becoming a practical and increasingly popular alternative. The technology has improved, the costs have come down, and for many organizations, the benefits clearly outweigh the additional complexity.
We expect this trend to continue as models get better and tooling improves.