The biggest model isn't always the best choice. Optimization techniques often deliver the same perceived quality at a fraction of the hardware cost.
RAG and fine-tuning are often presented as equivalent alternatives. They aren't: they solve different problems, and choosing the wrong one costs months of work.
Adopting an AI tool without checking where the data ends up is the fastest way to turn a productivity gain into a compliance problem. Here's what to check first.
A closed model and an open-weight model aren't two variants of the same product: they change who sees your data and who controls the system. Here's how to decide, case by case.
AI agents promise to automate entire workflows. On open models, today, some use cases work well in production — others don't, and it's better to know which before you build on top of them.
The word "open" gets used very loosely in the AI world. The licenses behind the models, however, differ significantly — and reading them before adoption is not optional.
"Do we need a supercomputer?" is the question that stalls more AI projects than any other before they even start. The answer is almost always more modest than feared.
"Open model" isn't a single category: behind the term are very different families. Here's how to find your way without letting benchmarks alone guide you.