756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
Spreading pods across availability zones buys resilience and costs bandwidth. Kubernetes load balances without regard to zone by default, so most of your service traffic is billed. Here is how to fix it.
A read replica in another zone or region is not just a second instance bill. Every write is shipped over a metered network, and for write-heavy systems the transfer can rival the compute. Here is the breakdown.
Backup storage is cheap and well understood. Backup movement is neither. Cross-region copies, restores, and third-party backup targets all cross metered networks. Here is what each one costs.
Video is the heaviest thing most companies put on a network. One hour of 1080p delivered to one viewer moves several gigabytes, and the per-GB meter does the rest. Here is the cost per viewer, worked out.
DNS is priced per million queries, which feels free until you count how many queries a busy system actually makes. TTLs, health checks, and service discovery all move the number. Here is the math.
Transfer Acceleration routes S3 traffic over the CloudFront edge network for a per-GB premium on top of normal transfer. Sometimes it is worth several times its price, and sometimes it does nothing at all.
Origin shield adds a second caching layer between edge locations and your origin. It costs per request and saves origin load, but it only pays for itself under particular traffic shapes. Here is when.
Active-active across two regions is not twice the cost of one. Duplicated compute, duplicated storage, and constant bidirectional replication traffic push the multiple closer to 2.3 or 2.5. Here is the breakdown.
A bandwidth bill is meaningless as an absolute number. Divided by active users it becomes a metric you can trend, forecast, and compare against revenue per user. Here is how to build it.
The more data you accumulate in one place, the more it costs to move elsewhere, and the more services cluster around it. Egress pricing is what turns that pull into a financial constraint.
Every pod start pulls an image. Multiply a 1.2 GB image by a few thousand pulls a day and registry traffic becomes a real line item, especially when the registry sits in another region or another cloud.
Observability data leaves your cloud on its way to a vendor, and it leaves continuously. At typical volumes the egress alone can rival a mid-size database bill, before the vendor charges anything.
Moving tens or hundreds of terabytes into or out of a cloud has three viable paths with very different economics. The right one depends on volume, deadline, and available bandwidth. Here is the comparison.
Private Service Connect gives you a private IP inside your VPC for a Google or third-party service. It charges per endpoint hour and per gigabyte, and it usually beats routing the same traffic through Cloud NAT.
Archive tiers advertise storage at a fraction of a cent per gigabyte. The catch is on the way out: retrieval fees, minimum durations, and per-request charges that can dwarf a year of storage savings.
Cold starts are usually discussed as a latency problem, but they also change what you pay. Initialization time, runtime choice, package size, and burst shape all land on the invoice. Here is the arithmetic.
Provisioned concurrency trades a usage bill for a capacity bill. There is a clean break-even point based on utilization, and most teams sit on the wrong side of it. Here is how to compute yours.
More Lambda memory means more CPU, so a bigger function can be cheaper than a smaller one. The relationship is not linear and the sweet spot is workload-specific. Here is how to find it with numbers.
Orchestrating a multi-step process can be a managed state machine, a chain of queues, or a single long-running function. Each has a different cost curve. Here is where each one wins.
Dead letter queues look free until a poison message or a bad deploy fills them. The cost is in redrive traffic, retry amplification, and storage of messages nobody reads. Here is the real arithmetic.
Fan-out multiplies one event into many deliveries, and the bill multiplies with it. Different fan-out mechanisms have very different per-delivery economics. Here is what each one costs at scale.
Three ways to put a function on the internet, three completely different pricing models. One is free, one bills per request, one bills per hour. Here is where each becomes the cheapest option.
The function is cheap, the database it talks to often is not. Connection limits, proxies, and idle capacity turn a per-request compute model into a per-hour database bill. Here is how to pair them well.
On-demand looks expensive per request and provisioned looks cheap, until the traffic spikes. Burst shape, not average volume, decides which capacity mode costs less. Here is the arithmetic for bursty workloads.