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.
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.
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.
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.
Comprehensive dataset of token consumption across 64 real-world AI tasks, including standard and reasoning models, with multimodal inputs.
On-premise AI for SMEs: Practical guide to models, hardware, and operations. Concrete recommendations on servers, data protection, and stability.