Why On-Premise AI Is Almost Always Safer
Free models, exposed credentials, and anonymization that does not hold up: why local AI is the only truly consistent answer to the security question.
API Standards for Large Language Models (LLM) AI
Practical guide to LLM API standards: OpenAI-compatible Chat Completion API for on-premise models (GPT-OSS, Llama, Qwen, DeepSeek), LiteLLM as unified gateway, control parameters, multimodality, structured outputs, tool calling and MCP. Everything developers need to know for enterprise LLM integration.
The Mathematics of AI Reliability: SLA-Based Capacity Planning Using Engset's Formula
How proven telecommunications mathematics ensures your on-premise AI infrastructure meets performance guarantees. A technical white paper on SLA-based capacity planning using Engset's formula.
Transparently Calculate On-Premises AI Costs
Our interactive cost calculator helps you accurately determine hardware requirements and costs for running your own Large Language Models, including a comparison of AMD and NVIDIA solutions.
AI Costs by Office Role: Token Consumption Patterns Across Your Team
Calculate the real AI costs for your team. From Executive Assistants to Software Engineers—understand what drives token consumption by office role and why developers are your biggest budget item.
Dataset: LLM Token Usage in Everyday Office Tasks
Comprehensive dataset of token consumption across 64 real-world AI tasks, including standard and reasoning models, with multimodal inputs.
On-Premise AI Solutions for every SME
On-premise AI for SMEs: Practical guide to models, hardware, and operations. Concrete recommendations on servers, data protection, and stability.