756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
AWS AppSync (managed GraphQL) and API Gateway (REST and HTTP) are priced differently and suit different workloads. AppSync bills per query and real-time message; API Gateway bills per request. Here is how they compare and when each is cheaper.
Some cloud data transfers are free (inbound, same-AZ private traffic) and some are expensive (egress to the internet, cross-region, cross-AZ). Knowing which is which is essential to avoiding surprise bills. Here is the map.
Aurora Serverless v2 scales database capacity in fine-grained Aurora Capacity Units and bills per ACU-hour. It can save money on variable workloads but cost more on steady ones. Here is how the pricing works and when to use it.
When your AWS bill jumps unexpectedly, a systematic investigation, Cost Explorer, grouping by service and usage type, checking recent changes, finds the cause fast. Here is a step-by-step troubleshooting playbook.
The identical EC2 instance, S3 bucket, or RDS database costs different amounts depending on the AWS region. Understanding why, and which regions are cheapest, can meaningfully cut your bill. Here is the full explanation.
Azure Blob storage offers hot, cool, cold, and archive access tiers at very different prices, trading storage cost against access cost and retrieval latency. Matching data to the right tier can cut storage spend dramatically. Here is how.
The Azure free tier mixes a 12-month trial, a set of always-free services, and a starting credit. Knowing which is which keeps you from an unexpected bill when a trial window closes. Here is the full breakdown.
Azure Machine Learning cost is dominated by compute, GPUs for training, endpoints for inference, and idle clusters. Right-sizing, autoscaling to zero, spot compute, and batch endpoints are the biggest levers. Here is the playbook.
Azure prices the same service differently by region, sometimes by a wide margin. The cause is local infrastructure cost, demand, and taxes. Knowing the pattern helps you place workloads where they are cheapest without hurting latency.
Azure reservations give a large discount on VMs, databases, and other services in exchange for a one or three year commitment. Understanding scope, flexibility, and exchange rules is key to committing without overcommitting. Here is how they work.
For most AWS accounts, a short list dominates the bill: compute, data transfer, storage, databases, and managed services. Knowing which drivers matter lets you focus optimization where it pays. Here is the breakdown.
BigQuery cost splits into query processing (bytes scanned or slots) and storage. Partitioning, clustering, avoiding SELECT star, and choosing on-demand versus capacity pricing are the biggest levers. Here is the detailed playbook.
FinOps succeeds as a culture, not a tool. Shared ownership, cost visibility for engineers, blameless accountability, and celebrating savings turn cost into a normal engineering concern. Here is how to build that culture.
EC2 prices vary by region, and the cheapest regions for a given instance are usually the large US regions. Choosing a low-cost region for latency-tolerant compute is easy savings. Here is how to find and use the cheapest region.
Azure prices services differently by region, and the cheapest regions for compute are often large US and Western European regions. Choosing a low-cost region for latency-tolerant workloads is easy savings. Here is how to pick one.
GCP prices Compute Engine and other services differently by region, with US regions often cheapest. Choosing a low-cost region for latency-tolerant workloads is simple savings. Here is how to find and use the cheapest GCP region.
Region choice affects compute price, data transfer, latency, and compliance all at once. The cheapest region is not always the right one. Here is a framework for choosing a region that minimizes total cost without hurting users.
CI/CD cost comes from compute minutes, caching misses, oversized runners, and redundant builds. Right-sizing runners, caching aggressively, and skipping needless work are the biggest levers. Plus, pipelines are the place to catch cloud cost before deploy.
Budget alerts notify you when spend crosses a threshold or is forecast to, on AWS, Azure, and GCP. Setting them up per team and service, with actionable thresholds, is the simplest cost safety net. Here is how to configure them well.
A content delivery network bills mostly for data transfer out and requests, and the rates vary by provider and region. Here is how CloudFront, Google Cloud CDN, and Azure CDN compare, and how to keep CDN cost predictable.
A good cost dashboard turns an opaque bill into an actionable picture: spend by team and service, trend, unit cost, commitment health, and anomalies. Here is what belongs on a cloud cost dashboard and how to make it drive decisions.
Unit economics ties cloud spend to a business metric, cost per customer, per transaction, per request, so you measure efficiency instead of just total dollars. This guide covers which units to pick, how to compute them, and how to improve them.
CloudFront bills for data transfer out, requests, and optional features, and the biggest savings come from raising cache hit ratio, choosing the right price class, and using commitments. Here is a complete guide to optimizing CloudFront cost.
CloudWatch can quietly become a large line item through custom metrics, high-cardinality dimensions, verbose logs, and frequent API calls. Here is what drives a CloudWatch surprise bill and how to keep observability cost under control.