Key takeaways
- Public APIs win for pilots and low volume. Private models win when data can't leave your control or volume is steady and high.
- The real question is per workload, not per company. A model gateway lets each request go to the right model.
- Private hosting is an operations commitment: GPUs, updates, monitoring and evaluation need an owner.
Two ways to use a large language model
With a public AI API, you send prompts to a provider's shared service and pay per token. With a private LLM, an open-weight model runs on infrastructure dedicated to your company, and your prompts and documents stay inside that environment.
Both are legitimate. The right choice depends on the data involved, the volume, and how much control you need over the model's behavior.
Side by side
| Criteria | Public AI API | Private LLM |
|---|---|---|
| Data control | Sent to a third-party provider | Stays in an isolated environment |
| Time to start | Minutes | Days to weeks |
| Cost at high volume | Grows with every request | Flatter, capacity-based |
| Model quality | Access to the largest frontier models | Strong open-weight models, tuned to your tasks |
| Customization | Limited | Fine-tuning on your data possible |
| Operations | Handled by the provider | You (or a partner) run GPUs, updates and monitoring |
When a public API is the right call
- You're validating a use case and need results this month.
- The data isn't sensitive, or the provider's enterprise terms meet your requirements.
- Volume is low or unpredictable, so paying per request is cheaper than reserving capacity.
- The task needs the very largest models available.
When a private LLM is the right call
- Contracts, regulations or clients require that data never leave your control, common in healthcare, legal and financial services.
- Volume is steady and high enough that per-token pricing costs more than dedicated capacity.
- You want to fine-tune a model on your own language, formats or decisions.
- You need to pin a model version so behavior doesn't change underneath you.
Why most companies end up with both
In practice the decision is per workload. A contract-review assistant handling privileged documents belongs on a private model; a marketing copy helper can use a public API.
A model gateway makes this practical: one secure endpoint for your apps, with rules that route each request by sensitivity, cost and speed. It's also where you enforce quotas per team and log usage for audits.
What private hosting really involves
Running your own model isn't just renting a GPU. Someone has to choose and serve the model efficiently, scale capacity with demand, monitor latency and accuracy, test upgrades before rolling them out, and keep costs in check.
That's the work our managed AI infrastructure service covers, priced as a clear monthly plan. If you're weighing the options, our AI audit includes a cost comparison for your actual workloads.