architecture
60 articles on architecture — what drives the cost, how it is priced, and where the savings actually are.
Image optimization: cutting storage and egress with smaller files
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.
Cold start mitigation cost: paying to keep functions warm
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.
Database sharding: the cost of scaling writes beyond one node
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.
Cloud repatriation cost: when moving back on-prem actually saves
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 explained: matching the family to the workload
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.
Event-driven vs polling: the cost of asking versus being told
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 to reduce cloud cost: fewer bytes, lower bills
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.
Tiered storage strategy: matching data temperature to storage cost
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 fleet diversification: capturing the discount without the drops
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.
The cheapest AWS region: how much location affects your bill
AWS prices the same service differently by region, sometimes 10 to 50 percent apart. us-east-1 is often the cheapest, but latency, data residency, and transfer between regions complicate a naive move. Here is how region choice affects cost.
Serverless cost optimization: pay-per-use is not automatically cheap
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.
Caching to reduce cloud cost: deflect the work you are paying for
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.