338 articles on aws — what drives the cost, how it is priced, and where the savings actually are.
A build farm running 36,000 jobs a month for 120 engineers costs about 1,165 dollars on self-hosted Spot instances, against 2,304 dollars on hosted runners. Here is the breakdown and where the crossover actually sits.
An internal developer platform serving 150 engineers across three Kubernetes clusters costs about 3,069 dollars a month, or 20 dollars per engineer. Here is the breakdown and which lines grow with headcount.
A streaming and batch pipeline ingesting 1 TB a day costs about 5,721 dollars a month on AWS, which is 19 cents per gigabyte. Here is the breakdown, and why the transform step is the cheapest part of it.
A search backend over 20 million documents serving 5 million queries a month costs about 946 dollars on a managed cluster, or 269 dollars self-managed. Here is the breakdown and where the extra 677 dollars actually goes.
A notification service sending 5 million emails, 2 million pushes, and 200,000 SMS a month costs about 2,543 dollars, and SMS is 69 percent of it for 2.6 percent of the volume. Here is the breakdown.
A file sharing service holding 80 TB for 50,000 users with 20 TB of monthly downloads costs about 4,006 dollars on AWS. Here is the breakdown and why tiering storage matters more than anything else.
A service receiving 30 million webhooks a month, processing them with retries, and archiving payloads costs about 506 dollars on AWS. Here is the breakdown, including the 150 dollar mistake almost everyone makes.
A self-hosted headless CMS with 8,000 entries, 25 editors, and 30 million API requests costs about 1,025 dollars a month on AWS, or 41 dollars per editor seat. Here is the breakdown against the hosted alternative.
A REST API handling 30 million requests a month costs 267 dollars a month serverless or 579 dollars on containers. Here is the full breakdown of both, and where the crossover actually sits.
Not all outbound traffic is billable. Every major cloud has a list of destinations where egress is free, and routing through them is one of the cheapest optimizations available. Here is the list and how to use it.
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.
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.
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.
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.