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Practical writing on the questions teams actually ask before committing to AI infrastructure — the economics, the trade-offs, and the decisions that are expensive to get wrong.
Fine-tuning vs RAG
Most teams reach for fine-tuning when they have a knowledge problem, and for retrieval when they have a behaviour problem. Both are expensive mistakes, and both are avoidable with one question.
Read the article →GPU cloud pricing explained
The hourly rate on a pricing page is the smallest part of an AI infrastructure bill. Here is what the rest of it consists of, and which parts you can control.
Read the article →UAE AI data residency
Residency, sovereignty and isolation get used interchangeably in procurement conversations. They are three different things, and only one of them is usually what a regulator asked for.
Read the article →Evaluating a fine-tuned model
A training run that finishes successfully tells you almost nothing. Here is what to measure instead, and why the comparison matters more than the score.
Read the article →Arabic AI and Gulf dialect
Models that score well on Modern Standard Arabic often fall apart on how people in the Gulf actually write. The reasons are structural, and most of them are addressable.
Read the article →On-premises vs dedicated cloud
Three deployment models, three very different operational burdens. Most teams choose the heaviest one available and then live with it for years.
Read the article →Questions these did not answer?
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