Cloud Accounts for Researchers and Academic Workloads
Research computing has an unusual shape: bursty demand, fixed grants, reproducibility requirements and data that often cannot move. What that implies for a cloud account.
Guides
Step-by-step guides for choosing, opening and running cloud accounts.
Multi-Provider Cloud Strategy: The Practical Version
Using more than one provider is usually right for cost and leverage and usually wrong as a redundancy plan. How to split workloads along the seams that already exist.
Cloud Accounts for Agencies Managing Client Infrastructure
Running infrastructure for other people is a different problem from running your own. Separation, billing, access and exit are the four that decide whether it stays manageable.
Cloud Accounts for Startups: The First Twelve Months
The decisions that are cheap now and expensive in a year: account structure, provider choice, commitments, and what not to build yet.
Cloud Accounts for AI Training: Planning the Run Before You Start It
Training jobs fail for operational reasons far more often than mathematical ones. Checkpointing, data staging, quotas and interruption handling decide whether a run finishes.
Choosing a Cloud Provider for AI Workloads
For AI work the usual provider comparison changes shape: accelerator availability, data gravity and managed model services matter more than instance pricing.
Running LLM Inference on Your Own Cloud Account
Self-hosting a language model is a capacity planning problem more than a machine learning one. What decides whether it is cheaper than an API, and what it commits you to.
GPU Cloud Instances: What They Are For and What They Cost You
A GPU instance is a different kind of machine with different constraints — availability, memory, data movement and billing granularity all behave unlike ordinary compute.
Serverless vs Virtual Machines: A Practical Comparison
Serverless changes the billing unit, the failure modes and the constraints. Where it is clearly better, where it is clearly worse, and why most systems end up using both.
Autoscaling: What It Fixes and What It Hides
Autoscaling handles predictable variation well and sudden spikes badly. What it requires from the application, what it costs, and the failures it can disguise.