Local LLM vs ChatGPT and Claude: when a private model is good enough
Most AI tools are hosted: your prompts travel to a provider's servers and back. On-premises AI runs on hardware in your own office. Neither is right for everyone.
Last updated October 2026
Hosted AI: the strengths
- Access to the largest, newest models without buying hardware.
- Nothing to maintain on site.
- Business plans from major providers describe controls such as no training on business data by default.
Hosted AI: the trade-offs
- Prompts and documents are processed on the provider's systems, under the provider's terms.
- Retention, access and processing locations are set by the provider's policies, which can change.
On-premises AI: the strengths
- Prompts and documents are processed on hardware you own.
- Your own policies decide retention and access.
- It keeps working the same way regardless of a provider's product changes.
On-premises AI: the trade-offs
- Local models can be less capable than the largest hosted models at some tasks.
- Hardware has to be bought and kept up to date.
- Someone has to set it up, secure it and maintain it. That's the part NexoGrid does for you.
When a local model is good enough
For everyday work like summarizing documents, drafting letters and emails, and answering questions from your own files, local models are often a good fit. The largest hosted models can still be stronger at hard reasoning, very long documents and some specialist tasks. The honest way to know is to test with your real examples, which is what we do before you buy anything.
How to decide
Start with the documents you most want AI to help with. If your policies or clients make you want them on hardware you control, on-premises is worth a look. Our checklist covers the questions to ask either way.
We sell managed private AI, so read this with that in mind. Facts about other products come from their own documentation, linked below, as we read it in October 2026. Products change; check the current documentation before you decide.