Blog
Practical guides on ML infrastructure cost optimization, SageMaker cost attribution, and FinOps for machine learning teams on AWS.
How to Reduce Amazon Bedrock Costs: 9 Levers That Actually Move the Bill
Model selection, prompt caching, batch inference, provisioned throughput, and RAG tuning — a practical field guide to cutting Amazon Bedrock spend, plus the attribution you need to make the savings stick.
Read article →Prompt Caching Economics: How the 90% Discount Really Works
Cache writes cost more than regular tokens, hit rates decide everything, and "caching enabled" tells you almost nothing about what you're paying. The pricing mechanics, the break-even math, and the patterns that quietly kill hit rates in production.
Read article →Token Optimization: The Highest-Leverage Lever for Reducing Generative AI Costs
Most teams cut generative AI costs by switching models. That's the lazy lever. Token optimization is the compound lever. It saves on every API call, forever. A four-layer field guide for production LLM workloads.
Read article →Why ML Cloud Costs Spike Without Warning: 8 Hidden Drivers of Runaway AWS Bills
Cloud billing is a lagging indicator — you find out after the damage. Here are the 8 patterns behind unexpected ML infrastructure cost spikes, and how to catch them before they hit your invoice.
Read article →The Complete Guide to SageMaker Cost Attribution: From Billing Chaos to Model-Level Visibility
SageMaker costs are buried across training jobs, endpoints, notebooks, and storage. This guide shows you how to attribute every dollar to the model, team, and experiment that generated it.
Read article →Why Your AWS Bill Doesn't Tell You What Your ML Models Actually Cost
Your AWS bill shows total spend — but it can't tell you which model, team, or experiment drove last month's spike. Here's why that gap is costing you more than you think.
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