Deep dives on provider economics, GPU selection, pricing trends, and what it all actually means for your budget. Written by practitioners, not analysts.
H100 availability, pricing trends, neocloud vs hyperscaler dynamics, and what's changed in the past 12 months. Essential reading for anyone buying compute in 2026.
Spot and preemptible instances can cut your GPU bill by 60–90%. But interruptions, checkpointing overhead, and availability uncertainty make the calculus non-obvious. We break it down.
In June 2025, AWS made an aggressive price cut on H100 instances. We look at what drove it, how competitors responded, and what it signals for 2026 pricing trends.
Most teams buy for training and realize inference has completely different requirements. GPU choice, networking, cost structure — here's what changes when you move to production.
The H100 is 2–3× faster for transformers, but costs 60–80% more per hour. We model out real training runs to show when you should upgrade — and when you're overpaying for headroom.
Most teams either dramatically over-buy or run out of compute mid-training. This guide walks through a real methodology for estimating compute needs from model size, dataset size, and timeline.
The right pricing model depends on your workload type, fault tolerance, and budget. We walk through a decision tree that most procurement teams can actually use.
Egress fees, storage, data transfer, support plans, and tooling costs — hyperscaler list prices rarely tell the full story. Here's what actually shows up on the bill.
Neoclouds are significantly cheaper and often faster to provision. But hyperscalers offer broader ecosystems and compliance coverage. Here's how to decide.
Both GPU-native neoclouds with strong H100 availability. But they serve different buyers in subtle ways. Reliability, pricing models, networking, and who belongs where.
Vast.ai's marketplace pricing is unbeatable. But the "community" nature raises questions about reliability and support. We look at the real use cases and risk profile.
AWS is rarely the cheapest option. But there are real scenarios where paying the premium is justified — compliance, data locality, VPC integration. We map them out.
The difference between SXM and PCIe variants isn't just clock speed — it's interconnect bandwidth. For large model training, this can be the difference between linear and sublinear scaling.
Choosing the wrong parallelism strategy wastes GPU hours. This guide breaks down the tradeoffs — and shows the compute and networking cost implications of each approach.
Lower precision means faster training and cheaper compute. But it also means more careful implementation. Here's the practical breakdown of when each precision makes sense.