756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
DataSync bills about $0.0125 per GB of data copied, plus any underlying storage and transfer at the endpoints. Cost tracks how much you move, so incremental transfers and filters keep it down. Here is the simple model.
AWS Batch adds no charge of its own; you pay for the EC2, Fargate, or Spot compute your jobs run on. Compute environment choice and Spot are the big levers. Here is how to run batch workloads cheaply.
AppSync bills GraphQL query and data-modification operations per million, real-time subscription messages per million, and connection-minutes. Chatty clients and broad subscriptions drive the bill. Here is how the managed-GraphQL pricing works.
Azure Standard Load Balancer bills per load-balancing rule and per GB of data processed, plus outbound. The Basic tier is free but limited and retiring. Here is how the Standard tier prices and how to keep it lean.
Cloud Composer bills environment compute (per-hour, always on) plus the underlying GKE, database, and storage. The always-on environment fee is significant, so consolidating DAGs and right-sizing the environment matter. Here is the breakdown.
The right index turns a full scan into a fast lookup, cutting both query latency and the compute or I/O you pay for. But every index adds write overhead and storage. Here is how indexing trades cost against performance and how to get it right.
Databases are often the biggest single line on the bill and the most over-provisioned, sized for a peak that rarely comes or copied from another environment. Right-sizing to real CPU, memory, and IO need cuts cost without hurting performance. Here is how.
Running in multiple regions cuts latency for distributed users and adds resilience, but it multiplies compute, replicates data, and adds cross-region transfer. Knowing when the latency and availability are worth the multiplied cost is the decision. Here is the framework.
Redshift Serverless bills per Redshift Processing Unit (RPU) hour while queries run, plus managed storage, with no cluster to pause. It suits variable, intermittent analytics; steady heavy load is cheaper on a provisioned cluster. Here is how to size it.
Amazon MQ bills broker instance hours by size and deployment (single or active-standby), plus storage. Active-standby doubles the instance cost for high availability. Here is how the managed-broker pricing works and when to use it.
WorkSpaces bills per user by bundle, in either an always-on monthly mode or an hourly mode with a small monthly base plus per-hour usage. Choosing the right mode for each user's usage pattern is the main saving. Here is how to decide.
Transfer Family bills about $0.30 per hour for each enabled protocol endpoint (SFTP, FTPS, FTP) plus about $0.04 per GB uploaded and downloaded. The always-on endpoint fee is the surprise for low-volume use. Here is the breakdown.
Azure VPN Gateway bills a fixed hourly fee by SKU (from about $0.19/hour for VpnGw1 to several dollars for higher SKUs) plus outbound data transfer. The SKU, chosen for throughput and tunnels, dominates the bill. Here is how to size it.
Scale Sets add no charge of their own; you pay for the underlying VMs, disks, and load balancer. Autoscaling rules, instance size, and Spot instances decide the bill. Here is how to tune them for cost without hurting performance.
Cloud Run jobs bill per vCPU-second and memory-GiB-second while the job runs, with no idle cost since jobs run to completion and stop. It suits batch and scheduled work. Here is how the model works and how it differs from Cloud Run services.
Filestore bills per GB-month of provisioned capacity by tier (Basic HDD, Basic SSD, Zonal, Enterprise), with tier minimums. You pay for the capacity you allocate, not what you use, so tier and size choice drive the bill. Here is how to pick.
Cloud DNS bills about $0.20 per managed zone per month plus about $0.40 per million queries, similar to Route 53. For most domains it is a few dollars. Here is the breakdown and where cost hides for high-query or many-zone setups.
GPU instances cost many times a CPU instance per hour, so idle GPUs, oversized accelerators, and full-price on-demand are expensive mistakes. Spot, right-sizing, and sharing are the big levers. Here is how to keep GPU cost under control.
Cost anomaly detection flags unexpected spend increases, a misconfigured resource, a runaway job, a leak, before they become large bills. Automated detection plus fast response is the FinOps safety net. Here is how it works.
The biggest FinOps lever is engineers who see and consider the cost of what they build. Giving developers cost visibility at design and PR time, and making cost a normal engineering concern, prevents waste at the source. Here is how.
A large share of cloud spend is waste, idle resources, over-provisioned capacity, orphaned volumes, forgotten environments. Systematically finding and eliminating it is the fastest cost win. Here is a complete waste-hunting playbook.
The right cloud cost KPIs, unit cost, coverage and utilization of commitments, waste percentage, forecast accuracy, turn cost from a monthly surprise into a managed metric. Here are the KPIs that matter and how to use them.
Cost allocation tags attribute cloud spend to teams, projects, environments, and customers, making cost accountable and enabling showback, unit economics, and optimization. A consistent tagging strategy is the foundation of FinOps. Here is how to build one.
FinOps works as a collaboration between engineering, finance, and a central FinOps function, not a single cost-cutting team. Clear roles, shared ownership, and a central enabling team make cost a managed discipline. Here is how to structure it.