Key takeaways
- Commercial AI APIs offer the strongest models with the least operational effort, billed by usage.
- Open-weight models from several vendors can be run on your own infrastructure for maximum data control.
- Self-hosting shifts costs to hardware or cloud GPUs, engineering and operations.
- Many businesses combine both: APIs for general tasks, self-hosted models for sensitive or high-volume workloads.
As AI moves into everyday business processes, a common question is whether to send data to commercial AI providers or run models yourself. There is no single right answer; it depends on your data, volumes, skills and budget.
The two approaches
| AI APIs | Self-hosted open-weight models | |
|---|---|---|
| Where models run | The provider’s infrastructure | Your servers or private cloud |
| Model quality | Typically the most capable frontier models | Strong and improving, varies by model and size |
| Cost model | Pay per use, usually per token | Hardware or GPU rental, plus engineering and operations |
| Data control | Governed by provider terms and agreements | Data stays in your environment |
| Effort | Low: call an API | High: deployment, scaling, monitoring, updates |
Open-weight models
Several organizations publish model weights that can be downloaded and run locally, including families from Meta, Mistral, Alibaba, DeepSeek and, since August 2025, OpenAI. Licenses differ, so review each model’s terms for commercial use. Smaller models run on modest hardware; larger ones need powerful GPUs.
When APIs make sense
- You need the best available reasoning and writing quality.
- Volumes are moderate or unpredictable.
- Your team does not want to operate GPU infrastructure.
- The provider offers suitable data terms, such as no training on your data and, where needed, a BAA or enterprise agreement.
When self-hosting makes sense
- Data must not leave your environment for regulatory or contractual reasons.
- Workloads are high-volume and steady, where owned capacity can be cheaper than per-token pricing.
- Tasks are narrow, such as classification or extraction, where a smaller model performs well.
- You need full control over model versions and behavior.
Hidden costs of self-hosting
- GPU hardware or cloud instances, often running even when idle.
- Engineering to deploy, scale and secure inference servers.
- Monitoring, updates and evaluation as new models appear.
- Building features APIs provide out of the box, such as safety filters and tooling.
Privacy with APIs
Using an API does not have to mean giving up control. Review providers’ business terms: many state that API data is not used to train models by default and offer retention controls, regional processing and enterprise agreements. For regulated data, confirm compliance commitments such as a BAA for healthcare information before sending anything.
A pragmatic hybrid
Many teams route requests by sensitivity and complexity: general drafting and reasoning to a commercial API under appropriate terms, and sensitive or high-volume narrow tasks to a self-hosted model. A simple routing layer makes it possible to switch models as quality and prices change.
Our AI solutions team helps choose and deploy the right mix for each use case.
A worked example
A healthcare software company processes referral documents containing PHI. Its main assistant features use a commercial model API under a BAA, while a self-hosted open-weight model performs a high-volume classification step on internal servers, keeping that data in-house and reducing per-token costs. A routing layer decides which model handles each request, making it easy to swap models as better options appear.
Frequently asked questions
Are self-hosted models as good as commercial APIs?
For many narrow tasks they can be close. For complex reasoning and writing, leading commercial models are often stronger. Test on your own tasks.
Is self-hosting cheaper?
At high, steady volumes it can be. At low or variable volumes, APIs are usually cheaper once hardware and engineering are counted.
Can we keep data private with an AI API?
Often yes, through business terms that exclude training on your data, retention controls and appropriate agreements. Review them carefully.
