cost optimization
798 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
How to cut your GCP bill: the ordered reduction playbook
GCP has its own waste profile: idle persistent disks, unattached external IPs, snapshot sprawl, oversized machine types, and BigQuery scans. Here is the sequenced playbook with real rates and expected savings per lever.
The first 30 days of a cloud cost reduction program
A cost program that starts with a tool selection stalls. One that starts with a baseline, a hit list, and a weekly number delivers in the first month. Here is a day by day plan for the first 30 days with expected outcomes.
Reducing dev and test cloud spend without slowing engineers down
Non-production is typically 30 to 40 percent of a cloud bill and used for 30 percent of the hours. The savings are large and safe, but only if the changes do not add friction. Here is the playbook that cuts spend without cutting velocity.
Weekend and overnight shutdown: how much it actually saves
A week has 168 hours and most non-production workloads need about 45. Turning things off for the rest is the highest ratio saving in cloud. Here is the arithmetic, the implementation, and the resources that keep billing anyway.
The zombie resource hunt: finding what bills but does nothing
Zombie resources are alive on the invoice and dead in production: disks with no instance, clusters with no pods, gateways with no traffic. Here is the hunt playbook, the detection query for each type, and how to delete safely.
The cloud storage cleanup playbook: cutting object and block spend
Storage rarely spikes, it creeps, which is why it is the least audited line on most bills. Here is a five step cleanup covering tier policy, incomplete uploads, versioning, replication, and block storage, with real per GB rates.
Snapshot and backup cleanup: cutting the line that only grows
Backups are the one cost line with no natural ceiling: a daily schedule with no expiry runs forever. Here is how to audit snapshot spend, set retention that satisfies compliance, and cut 50 to 80 percent without losing recoverability.
The two week right-sizing sprint: a repeatable playbook
Right-sizing stalls when it becomes a permanent backlog. Running it as a time boxed sprint with a ranked list, a batch cadence, and a rollback rule ships 30 to 60 resources in two weeks. Here is the sprint plan.
Migrating to ARM for cost: a staged playbook with real numbers
ARM based instances price 10 to 20 percent below their x86 equivalents and often deliver better price performance than the headline rate suggests. Here is the staged migration playbook, the workloads that port cleanly, and the ones that do not.
The commitment purchase playbook: buying discounts without regret
A commitment is the only cost lever you cannot undo. Buy it at the wrong time or the wrong size and you have converted fixable waste into a three year obligation. Here is the sizing method, the timing rule, and the coverage targets.
Cutting the data transfer bill: a playbook for the invisible line
Data transfer is billed per GB with no resource to point at, which is why it is the hardest line to attribute and the easiest to ignore. Here is how to find the flows, rank them, and cut them, with the per GB rates that matter.
Reducing logging and observability spend without going blind
Observability spend grows with traffic, with services, and with every new dashboard, and it is often the second largest line after compute. Here is how to cut it 40 to 70 percent while keeping the signal you actually use.
Consolidating underused Kubernetes clusters to cut fixed cost
Every cluster carries a fixed overhead before a single pod runs: control plane, system nodes, load balancers, and monitoring agents. Running eight clusters at 15 percent utilisation is expensive in a specific, measurable way.
Deleting unused load balancers and IP addresses: a small line that adds up
A load balancer with no targets and a public IP with no association each cost a few dollars to tens of dollars a month. Individually trivial, collectively thousands. Here is how to find them, verify they are dead, and remove them safely.
Tuning autoscaling for cost: the settings that waste the most money
Autoscaling is sold as a cost control and often operates as a cost floor. Minimum counts set for comfort, thresholds that never trigger, and cooldowns that hold capacity long after demand fell all quietly set a bill you never chose.
Reducing marketplace and licensing spend on your cloud bill
Third party software bought through a cloud marketplace lands on the same invoice as compute but obeys different rules. It is rarely audited, often over-licensed, and frequently the largest line nobody in engineering owns.
Cloud cost cleanup before a funding round or audit
Due diligence looks at gross margin, unit economics, and whether infrastructure spend scales with revenue. A cost cleanup before the process starts changes the numbers investors see. Here is what to fix and in what order.
The bill spike emergency runbook: stopping the bleed in one hour
When spend triples overnight, the priority is stopping the burn, not understanding it. Here is an hour by hour runbook: contain first, diagnose second, then decide what to reverse and what to keep.
Graviton4 vs Graviton3 cost and performance: is the upgrade worth it
Graviton4 instances (R8g, M8g, C8g) deliver meaningfully more performance per core than Graviton3, at a similar or slightly higher hourly price. Whether the newer generation is cheaper for your workload depends on how well it converts extra performance into fewer or smaller instances. Here is the comparison.
EC2 compute-optimized vs general-purpose cost: C-family or M-family
The C-family (compute-optimized) gives more CPU per dollar with less memory, while the M-family (general-purpose) balances CPU and memory at a higher price. Picking the wrong one wastes money on RAM you do not use or forces oversizing to get enough CPU. Here is how to choose on cost.
EC2 memory-optimized instances cost: when the R-family pays off
The R-family (memory-optimized) packs 8 GB of memory per vCPU, costing more per hour than general-purpose but far less than oversizing an M-instance to reach the memory. For in-memory databases, caches, and analytics, it is the cheapest way to buy RAM. Here is when it pays off.
EC2 Auto Scaling cost optimization: paying for load, not for peak
An Auto Scaling group can save money by matching capacity to demand, or waste it by scaling on the wrong metric, holding too much warm capacity, or never scaling down. Here is how to tune an ASG so you pay for the load you have, not the peak you fear.
Lambda vs EC2 cost breakeven: when serverless stops being cheaper
Lambda is cheap for spiky, low-utilization workloads and expensive for steady, high-utilization ones, where an EC2 instance running full-time wins. The breakeven depends on request volume, duration, and how busy the compute would be. Here is how to find the crossover.
Fargate Spot cost savings: cutting container bills up to 70 percent
Fargate Spot runs your ECS tasks on spare capacity for up to about 70 percent less than regular Fargate, at the cost of possible interruption. For fault-tolerant, stateless, and batch workloads it is one of the biggest container savings available. Here is how it works and when to use it.