Are local AI and frontier models opposites?
No. Local refers to deployment on equipment controlled by the business, while frontier refers to models near the leading edge of capability. A model can have both properties, depending on the technology and equipment available.
Open weights create options for deployment outside the model provider’s service. OpenAI’s gpt-oss-20b card, for example, documents local running and other deployment routes. This describes an available model, not a claim that it matches today’s most capable hosted alternatives. The model card
Model size, access to weights and hosting location remain separate properties. A cloud provider can host an open-weight model, and a local model need not be the smallest option.
Does local AI keep business information private?
Local processing can give a business more control over where model inference happens. Privacy still depends on the complete system, including its connections, logging and access arrangements.
A locally running model may use an external search service or send information through another application. The local label therefore describes only part of the data flow. The organisation operating it also takes responsibility for the surrounding equipment and software.
Hosted services can offer specific data commitments. OpenAI states that business and API data are not used for training by default. The scope depends on the product; it is not a promise covering every AI account. OpenAI’s business-data guidance
Is running a model locally cheaper?
It can be, but the result depends on workload, equipment utilisation and the cost of operating it. Buying hardware does not establish the cost of producing usable work.
Local costs can include equipment, energy and technical support. Low use leaves purchased capacity idle; peak demand may require additional capacity. Hosted usage charges can instead vary with the volume and complexity of work, alongside any subscription commitments.
Both arrangements can require human checking and correction. A processing saving may disappear if one model produces more unusable results. The relevant comparison includes those costs over a realistic period and volume, rather than the price of a single call.
Can a business combine local and hosted AI?
Yes. Different tasks can use different deployments when their data and performance requirements permit it. That arrangement introduces decisions about which work goes where and how the handovers operate.
A system that routes work between providers may require more monitoring and evaluation than one arrangement alone. The additional complexity can have value where it accommodates genuinely different needs, but it also has a cost.
The result depends on the whole workflow. Evidence that a local model handles a particular extraction task does not establish its suitability for strategic analysis. Open-weight and closed models covers the separate question of access and licensing.
Legal note: This answer provides general information, not legal advice. Seek advice from qualified legal counsel for your circumstances.