Choosing Cloud Storage and Compute for AI Data Workloads
How to match cloud storage tiers and compute options to AI data workloads: training reads, feature serving, and retrieval, with trade-offs to weigh.
AI Data Infrastructure
Cloud Infrastructure for AI
Cloud Infrastructure for AI guides, comparisons and explainers from AIDataCloudBase.
Controlling Cloud Costs for AI Data Workloads
Practical ways to reduce and govern cloud spend on AI data workloads: tagging, idle resources, storage lifecycle, data movement, and review habits.
Planning for Data Egress and Network Costs in Multi-Region AI Workloads
How data egress, cross-region transfer, and network design affect AI workload costs, and practical ways to keep data and compute close together.
GPU or CPU? Choosing Compute for Embedding and Inference Workloads
How to decide between GPU and CPU compute for embedding generation and model inference, based on latency, throughput, utilization, and operational cost.