Current entered spend plus estimated hosted usage overage where heavy use exceeds it.
Local language models
AI workflows without surrendering the data layer.
Local and private model deployments can make sense when control, repeatability, and operating cost matter more than novelty.
Cost model
Estimate hosted AI spend against a local setup.
Adjust the assumptions to match your team. The estimate compares current seats/API usage with a local open-source model deployment, infrastructure, and management range.
Infrastructure, open-source model serving, maintenance, and management.
Negative savings means the local path is likely strategic, not cost-led.
* Assumptions: heavy use defaults to 300 prompts per user per month with high input/output token volume. Local model class and currency are planning assumptions, not vendor quotes. Local Llama/open-source deployments usually become strongest when usage is frequent, data is sensitive, workflows are repeatable, or per-user tooling spreads across a larger team.
Private AI infrastructure
Useful language models can live closer to the data.
Local LLM work is about practical control: where information goes, what the workflow costs, and how model output is grounded in approved internal sources.
Data sovereignty
Use local language models where sensitive, regulated, or commercially important data should stay inside controlled infrastructure.
InspectPrice effectiveness
Use local models for repeatable high-volume tasks where external per-token pricing becomes inefficient or difficult to forecast.
InspectPrivate knowledge systems
Build internal assistants over policies, manuals, reports, product data, and operational records with controlled retrieval.
Inspect