Cloud Cost Optimization — The Practical Guide for AWS, Azure, and GCP

Cloud bills rarely spike in a way anyone notices. They creep: a forgotten environment, an oversized instance family, egress nobody modeled, a logging pipeline ingesting ten times what anyone reads. By the time finance asks why the invoice doubled, the waste is spread across hundreds of line items. This is the map for finding that waste and cutting it without trading away reliability.

Cloud cost optimization is not one trick. It's a small number of high-leverage levers applied in the right order, plus the discipline to keep them applied. Here's where the money goes and what to do about it, with a link to the deep dive for each.

First, see where the money actually goes #

You can't cut what you can't attribute. Before touching anything, get spend broken down by service, team, and environment, and set up alerting so the next spike shows up in hours, not on the invoice. That's covered in catching AWS cost spikes before the invoice. The usual shape once you can see it: compute is the biggest line, then data transfer and storage, then managed services and observability.

Compute: right-size, then commit, then go spot #

Compute is where the largest savings live, in three layers:

Right-size first. Most instances run at a fraction of their provisioned CPU and memory. Downsizing to what's actually used is free money before any commitment.

Commit to the steady-state. For the baseline you always run, commitments cut 30–70%. The tradeoffs between the options are in reserved instances vs savings plans vs spot. GCP's discounts apply automatically, explained in committed use vs sustained use discounts.

Use spot for the interruptible parts. Batch jobs, CI, and stateless workers can run 60–90% cheaper on spot if you handle interruptions, as in running production on spot safely.

Which provider is cheapest for your mix is its own question, answered with real numbers in AWS vs Azure vs GCP pricing.

Storage and data transfer: the quiet line items #

Storage looks cheap per GB and adds up fast, and the pricing differs enough between providers to matter, compared in S3 vs Azure Blob vs GCS. The bigger surprise is usually egress: cross-AZ, cross-region, and internet data transfer that never appears in anyone's mental model until it's a five-figure line. The patterns to avoid it are in cloud egress costs explained.

Kubernetes: cost hides inside the cluster #

A Kubernetes cluster is a cost black box unless you instrument it. Requests set too high waste the whole node; bin-packing and autoscaling recover it. The tools that surface and act on this (Kubecost, OpenCost, Cast AI) are compared in Kubernetes cost optimization tools.

The fastest wins, in order #

When someone hands you a bloated bill and a week, this is the order that pays back fastest, detailed in how to cut your AWS bill by 40%:

Delete idle and orphaned resources (unattached volumes, old snapshots, idle load balancers).

Right-size the obvious over-provisioned instances.

Kill or downsample runaway logging and observability ingestion.

Commit to the steady-state compute.

Fix the egress patterns.

Buy the right tooling #

Past a certain scale, spreadsheets stop working and a cost platform pays for itself by finding waste faster than a human can. The options (CloudZero, Vantage, Cast AI, and the native cost tools) are in best FinOps and cloud cost tools.

The call we'd make #

Instrument spend first so every change is measurable, then work the compute levers in order (right-size, commit, spot), then storage and egress, then the cluster. Make cost a standing metric with an owner, not a quarterly fire drill. The waste is almost never one big thing; it's fifty small things, and the teams that win are the ones who can see all fifty. Each linked guide is a concrete lever. Start with visibility, because everything else depends on it.