The closer an AI model runs to your data, the harder and more expensive it gets to run it. That relationship is not accidental. It is the shape of every privacy tradeoff in AI infrastructure.

Local means private. Cloud means convenient. Knowing which your situation requires is the only decision that matters.

The Cost of Local

Running a capable language model locally is not like running a web server. A web server has predictable resource requirements and decades of operational tooling behind it. A local LLM needs RAM — a lot of it — and the tooling to manage, update, and serve it is still maturing.

RAM prices have compounded the problem. AI infrastructure demand has driven up memory costs significantly. What would have been a modest server investment two years ago now represents a serious capital commitment. This is not a temporary market fluctuation. It reflects a structural shift in global hardware demand.

Beyond cost, local AI operation requires a different kind of expertise. Quantization, context window management, model selection, inference optimization — none of this maps onto existing web operations knowledge. Teams that can run production infrastructure confidently often find local AI model hosting genuinely unfamiliar.

The Gap in the Middle

The top-tier American models — Claude, GPT-class systems — cannot be run locally. Their parameter counts and architecture requirements place them firmly in data center territory. If you want their capability, you use their API. That means your data leaves your infrastructure.

This is not a flaw in the models. It is a consequence of what they are.

Intermediate solutions exist and are worth understanding. Cloud providers such as AWS operate instances of selected models in dedicated EU-only regions, meaning inference and data never leave EU territory. That regional data residency guarantee is meaningfully different from sending data to a global consumer API. It is not the same as local, but it provides data protection guarantees that unmanaged SaaS cannot.

Proxy architectures, enterprise agreements, and regional cloud deployments sit on the spectrum between fully local and fully public. They are not compromises to be embarrassed about. They are the realistic options available now.

Hardware Will Improve

The current constraint is not permanent. Models are becoming more efficient. Quantization techniques let larger models run in smaller memory footprints. Consumer and workstation hardware is improving. What requires a dedicated server today may run adequately on a developer machine in two or three years.

This matters for planning. Organizations investing in AI infrastructure now should design for flexibility. The boundary between “requires cloud” and “can run locally” will move.

The Decision You Should Actually Make

Before asking where to run a model, ask what data the model will touch.

This is the classification step that most organizations skip, and it is the one that determines everything else.

Highly sensitive data — proprietary source code, personal health or financial records, internal strategy documents — should only touch models running within your own infrastructure. If that is not currently feasible, the answer is not to use a public API. The answer is to not use an LLM for that workload yet.

Low-sensitivity or public data — documentation, publicly available information, marketing copy, open-source code — can move through SaaS LLM APIs without meaningful risk. OpenAI, Anthropic, and similar providers are appropriate for this tier.

Everything in between requires explicit evaluation. Classify the data. Understand the model’s data handling. Make a documented decision.

This is not a one-time exercise. New models, new use cases, and new data types will appear. The classification needs to be a process, not a spreadsheet someone filled out once.

That means:

  • Evaluate your data before you evaluate your models.
  • Match the hosting to the sensitivity, not to the convenience.
  • The models will get better and cheaper. The data does not become less sensitive on its own.