AWS surprise bills from autoscaling follow a small set of repeatable failure patterns: feedback loops, scale-out without scale-in, burst amplification from misconfigured metrics, and commitment mismatches after scaling events. Each pattern has a specific fix.
AWS publishes every price on a public page, yet bills still arrive as surprises. The problem is not opacity — it is that real costs emerge from interactions between services, not from any single line item.
A technical deep dive into EC2 performance optimization for API workloads — covering instance family selection, Graviton vs x86 economics, network tuning, EBS configuration, and Linux kernel parameters that directly impact throughput and tail latency.
A technical guide to hybrid compute architectures that combine EC2, Lambda, Fargate, and Step Functions — with worked cost calculations, SQS buffering patterns, and decision frameworks based on invocation pattern rather than unit cost.
MongoDB Atlas and self-hosted EC2 deployments have very different cost profiles at different scales. This guide covers TCO comparison, sharding strategies, index design for memory efficiency, and the edge cases that cause MongoDB costs to spiral.
A practical guide to AWS auto scaling — target tracking, step scaling, scheduled scaling, predictive scaling, and the strategies that balance performance, availability, and cost across EC2, ECS, and Lambda workloads.