Jaunākie raksti
Ceļveži par on-premise AI, privātumu, modeļiem, aparatūru un atbilstību.
On-premise AI vs the cloud: privacy, cost and control
Why a growing number of European companies run their AI in-house instead of sending data to third-party APIs.
The token bill shock: what happens when AI usage explodes
Per-token cloud pricing looks cheap in a demo — then usage scales and the invoice explodes. What Uber-scale adoption teaches every company.
How startups are quietly deploying private AI
Privacy, cost control and no vendor lock-in are pushing fast-moving startups to run their own models.
How to choose the right open-source model and hardware
Matching parameter size to your use case and budget — and the GPU that runs it well.
Build a private knowledge assistant with RAG
Turn your documents into a private assistant that answers with citations — without sending anything to the cloud.
The EU AI Act: a practical checklist for companies
What the AI Act means in practice and how on-premise AI helps you stay in control.
Fine-tuning open models on your own data
When prompting is not enough: how to specialise an open model on your domain — privately.
Data sovereignty: why European companies keep AI in-house
Where your data lives — and who can access it — is becoming a board-level question in Europe.
A practical GPU buying guide for on-premise AI
VRAM, throughput, power and budget: how to buy the right GPUs the first time.
From AI pilot to production without stalling
Most AI pilots never ship. Here is how to cross the gap to real production value.
How to calculate the ROI of private AI
A simple framework to compare on-premise vs metered cloud — and find your break-even.
Running AI fully air-gapped
For the most sensitive environments: AI with no internet connection at all.
Internal copilots teams actually use
The difference between a demo and a tool people open every day.
Autonomous agents for back-office automation
Beyond chat: agents that take actions across your internal systems — safely.
Private AI in healthcare
Clinical documentation, triage and research assistants — without patient data leaving the building.
Private AI for legal teams
Contract review, case-law search and drafting on confidential documents — kept confidential.
AI on the factory floor
Maintenance copilots, visual inspection and production analytics — running locally, even offline.
Quantization and fast inference on your own hardware
How to fit bigger models on smaller GPUs and serve them fast.
Hardening your private AI stack
Threat models and controls for self-hosted models, from access control to prompt-injection defence.
European funding for your AI project
Grants and programmes that can co-finance private AI and digitalisation.