59 articles on architecture — what drives the cost, how it is priced, and where the savings actually are.
Images are often the largest bytes a site serves, so optimizing them, right-sizing dimensions, modern formats like WebP and AVIF, and compression, cuts egress and storage substantially. Here is where image optimization saves cloud cost.
Serverless cold starts add latency; mitigating them with provisioned concurrency or minimum instances means paying for warm capacity around the clock. Whether that trade is worth it depends on traffic and latency needs. Here is the cost math.
When a single database cannot handle the write load, sharding splits data across nodes to scale horizontally. It adds infrastructure and significant operational complexity, so it is a last resort after cheaper scaling. Here is the cost tradeoff.
Repatriation, moving workloads from cloud back to on-premises or colocation, can cut cost for large, steady, predictable workloads, but the migration, hardware, and operational costs are substantial. Here is an honest look at when it pays.
AWS instance families (general purpose, compute, memory, storage, and accelerated) target different workloads, and using the wrong family over-pays for resources you do not need. This is a practical guide to choosing the cost-effective family.
Polling burns compute and requests asking for changes that usually have not happened; event-driven architectures do work only when something occurs. For infrequent events, event-driven is dramatically cheaper. Here is the cost tradeoff.
Compression shrinks the bytes you store, transfer, and scan, and cloud bills those bytes. Compressing data can cut storage, egress, and query cost at once, for a little CPU. Here is where compression pays and where it does not.
Most data cools over time: hot when new, cold within weeks, rarely touched within months. A tiered storage strategy moves data through cheaper tiers as it ages, cutting storage cost without losing access. Here is how to design one.
Spot instances are cheap but reclaimable, and concentrating a fleet in one instance type risks a mass interruption. Diversifying across types and zones smooths that risk, letting you run more of a workload on Spot safely. Here is how.
Serverless bills only for what you use, which can be cheap or expensive depending on memory, duration, invocation volume, and idle provisioned capacity. This is a practical guide to keeping Lambda, Fargate, and serverless databases economical.
Every cache hit is a database query, a Lambda invocation, or an origin fetch you did not pay for. Caching is one of the few levers that cuts cost and improves latency at once. Here is where to add it and how the math works.