Avoiding Vendor Lock-In on AI Cloud Platforms: A Practical Checklist
Where lock-in actually hides in AI cloud setups, and a checklist of design habits that keep migration options open without going fully multi-cloud.
AI Cloud, GPUs & MLOps
Where AI workloads actually run.
AI Cloud Platforms, GPU Cloud Computing, AI Model Hosting & Deployment, MLOps & AI Infrastructure and AI Cloud Pricing & Optimization explained.
Latest guides
Browse categories How to Choose an AI Cloud Platform for Your Team's Workloads
A practical selection guide for ML engineers and tech leads comparing AI cloud platforms, from workload fit and data gravity to team skills and exit options.
Data Residency and Egress: Questions to Settle Before Moving AI Workloads
Where your training data lives and what it costs to move it can decide an AI cloud migration. Questions to answer before you pick a region or provider.
How to Run a Proof of Concept on an AI Cloud Platform Before You Commit
A step-by-step way to test an AI cloud platform with a real workload, clear success criteria and a firm end date before signing anything long term.
Committed Use vs On-Demand GPUs: A Decision Framework for AI Teams
How to decide between on-demand, reserved and committed GPU capacity using your baseline usage, forecast confidence and flexibility needs, without guessing.
Forecasting GPU Spend Before a Training Project Starts
A simple method for estimating the cloud cost of a training project from a small pilot run, with the hidden line items and a buffer for reruns.