For years, running AI models inside your own network was something that only made sense for large enterprises with dedicated infrastructure teams. That has changed. Open-source models have reached quality levels that until recently only commercial models had, and the hardware needed to run them has become more accessible. Today a mid-sized company can seriously evaluate whether a given use case should run in the cloud or on its own servers, and that decision deserves more judgment than enthusiasm.
The most common mistake is framing the question backwards: choosing the architecture first and then looking for something to use it for. "We want a local AI solution" without being clear about which process, with what data, and with what team operating it is a reliable way to end up with expensive, underutilized hardware. The right question is not cloud or local. It is what problem is being solved and what that problem requires.
There are four questions that help frame the decision.
How sensitive is the data? If the use case involves contracts, patient records, legal documents, plant information, or financial data that cannot leave the organization due to regulation or internal policy, a local solution eliminates the risk of exposure. If the data is operational but not critical, the cloud has sufficient controls for most cases.
Is the volume stable and predictable? The cloud charges by usage. Some cases have high, consistent volume where the accumulated cost can justify running the model on your own infrastructure. The clearest example is video or image processing at scale: analyzing security camera footage, reviewing production material, or processing visual documents in volume can be prohibitively expensive via API. For those kinds of flows, the cost per call adds up fast and a local solution becomes economically reasonable.
Does it need low latency or offline operation? There are contexts where depending on internet connectivity is a real operational problem: manufacturing floors with limited connectivity, field operations, systems that cannot tolerate the latency of an external API call. In those cases a local solution is not a preference — it is a requirement.
Does the team have the capacity to operate this? This filter is frequently skipped. Running a model locally involves updates, monitoring, resource management, and support when something breaks. It is not an enormous burden, but it is not invisible either. If there is no one who can take ownership of that operation, the local solution becomes a maintenance problem before it becomes a working use case.
As for which models to use for a local solution, the ecosystem has grown considerably. Meta's Llama and Mistral are the best known and have versions for different hardware sizes. NVIDIA has its own model family, Nemotron, optimized to run on their GPUs with solid performance on enterprise tasks. For more specialized cases, such as document processing or image analysis, there are models fine-tuned for those specific tasks. The choice depends on the use case, not on a preference for one or the other.
That said, there is a category of cases where a local solution simply does not make sense: anything related to general productivity and office work. If the goal is for teams to write better, summarize meetings, generate drafts, or work with everyday documents, tools like Microsoft 365 Copilot, Google Workspace, or the enterprise plans from ChatGPT and Claude are built exactly for that, with integration already handled. No one should build their own infrastructure to replace that kind of functionality. Beyond that, frontier models advance at a pace that a local solution cannot keep up with: what is today the best model available to run on your own hardware will in a few months lag behind what cloud providers offer. For use cases that depend on the model's general capability, that gap matters.
A local solution makes sense when there is a specific process, data that cannot leave the organization, a volume that justifies the investment, or a concrete operational constraint. Outside of that, the cloud remains simpler, more up to date, and easier to maintain. The right decision is not ideological — it is the one that best serves the use case being solved.
