Learn how a SaaS AI platform reduced inference costs by 40% by testing open-weight models and migrating part of its OpenAI workload to dedicated LLM infrastructure.

Most AI apps send every request to a frontier model, including the simple ones. This post runs the numbers on what that costs at production scale and shows how routing commodity tasks to open-weight models cuts the bill without changing your stack.

Learn how engineering teams migrate from OpenAI to open-weight models, reduce inference costs, and run LLMs on dedicated infrastructure.

Hourly GPU pricing measures reserved time, not useful output. This post looks at where billed hours and productive training time drift apart, and why cost per trained model is the metric that matters.

Data sovereignty for AI workloads is increasingly required by contracts and sector-specific policies, not just a preference. This post covers what the requirements actually are, why running inference on US or European hyperscalers does not fully solve the problem, and how regional GPU infrastructure changes the calculus.

A practical guide to running Qwen models through an OpenAI compatible API. Keep your existing integrations while gaining more control over AI infrastructure.