Field
Tools & Ecosystem
What you run models on, and what it costs.
The practical layer. Not what models are — what it takes to actually use one.
The central decision is whether to rent or own. An inference API means someone else buys the hardware and keeps it running, and the arithmetic favours that for far longer than most teams assume. Open-weight models let you download and run them yourself — genuinely valuable when data can't leave or you need to work offline, and usually not "open source" despite the label: weights without training data is closer to a compiled binary.
GPUs are what everything runs on, and the specs people shop for are the wrong ones — memory decides what runs at all, bandwidth usually decides speed, and raw compute is rarely the bottleneck. Quantization is how large models fit on hardware you own, at a cost that isn't spread evenly. Vector databases are the storage under semantic search.
Start with Inference API — it's how nearly everyone actually uses AI.
4 concepts in this field
Vector Database
A database built to store embeddings and find the most similar ones fast — the search engine behind meaning-based retrieval.
Open-Weight Models
Models whose weights you can download and run yourself — often called open source, usually not quite.
GPU
The chip that made deep learning possible — thousands of small cores doing the same maths at once, which is exactly what neural networks need.
Inference API
Renting a model by the request — how nearly everyone actually uses AI, and the dependency that comes with it.