756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
Amazon FSx offers four managed file systems (Windows, Lustre, NetApp ONTAP, OpenZFS), each priced differently for capacity, throughput, and IOPS. Costs vary widely by type and deployment. Here is how FSx pricing works and how to choose the cheapest fit.
Google Cloud billing reports break spend down by project, service, SKU, and label, with filters and grouping to find what drives cost. Here is how to read GCP billing reports, use SKUs and labels, and export to BigQuery for deeper analysis.
Google Cloud committed use discounts (CUDs) cut compute and other costs by roughly 20 to 57 percent in exchange for a one or three year commitment. Resource-based and spend-based CUDs work differently. Here is how CUDs work and when they save the most.
Google Cloud has two free offerings: a 90-day, roughly 300 dollar credit for new accounts, and an always-free tier with monthly allowances for specific services. Here is what is truly free, where the limits are, and how charges begin.
Google Cloud prices the same resources differently by Region, sometimes by 20 percent or more, and egress between Regions and to the internet varies too. Here is how GCP Region pricing works and how to pick Regions that balance cost, latency, and compliance.
Google Cloud Storage has four classes trading storage price against retrieval cost and minimum durations. Standard is cheapest to access, Archive cheapest to store. Here is how the classes are priced and how to pick the right one for each data set.
GitHub Actions bills by the minute for hosted runners, with multipliers for larger and non-Linux runners and per-GB charges for storage and data transfer. Here is how Actions minutes are priced, what the multipliers mean, and how to cut CI costs.
S3 Glacier Flexible Retrieval, Glacier Instant Retrieval, and Glacier Deep Archive trade storage price against retrieval speed and cost. Deep Archive is the cheapest storage at around a dollar per TB per month. Here is how the cold tiers compare and when to use each.
Provisioned concurrency keeps Lambda instances warm to eliminate cold starts, but you pay for that capacity around the clock whether it runs or not. Here is how the pricing works and when it is worth it.
Lambda charges per request and per GB-second and scales to zero; Fargate charges per vCPU and GB by the second for continuously running tasks. The cheaper option depends entirely on your traffic pattern. Here is how to choose.
Lift and shift moves workloads to the cloud as-is for speed, but often carries over-provisioned cost; refactoring for cloud-native services costs more up front but runs cheaper. Here is how to weigh the two.
Large language model APIs bill per token, split between cheaper input tokens and pricier output tokens, and the counts add up fast with long prompts and context. Here is how token pricing works and how to control it.
Logs are cheap to write and expensive to keep forever. A retention strategy that tiers, expires, and archives logs by value cuts storage and ingestion cost without losing what you actually need. Here is how to build one.
You can call a hosted model API and pay per token, or run your own model on GPU instances and pay for uptime. Each wins at a different scale. Here is how the two cost models compare and where they cross over.
NAT gateways charge both an hourly fee and a per-GB data processing fee, so a chatty private subnet can rack up a large bill nobody expected. Here is why it happens and how to cut the cost.
Observability data (metrics, logs, and traces) is billed by volume, and unmanaged it can rival your compute bill. A cost-aware approach controls what you collect, how long you keep it, and how you sample. Here is the playbook.
Migrating from a data center to AWS has a one-time project cost and a new ongoing run cost that replaces your capital spend. Understanding both, and how they compare to on-prem, is essential before you commit. Here is the breakdown.
Managed OpenSearch bills for data nodes, storage, and often dedicated master and warm nodes, so a large or over-provisioned cluster gets expensive fast. Here is how to right-size and tier it without losing search performance.
AWS PrivateLink gives private, secure access to services without traversing the public internet, but interface endpoints bill an hourly fee per endpoint per AZ plus a per-GB data processing charge. Here is how the cost works and when it pays off.
Self-hosted Prometheus trades a per-metric SaaS bill for the cost of running and operating your own monitoring stack. Which is cheaper depends on scale, cardinality, and how much operational time you can spend. Here is the comparison.
A retrieval-augmented generation pipeline spends on embeddings, a vector database, retrieval compute, and the LLM tokens for augmented prompts. Each stage has its own cost driver, and context size ties them together. Here is the full breakdown.
X-Ray bills about $5 per million traces recorded and about $0.50 per million traces retrieved or scanned, with a free tier. Sampling is the main lever, since you rarely need every request traced. Here is how the tracing model prices.
Traffic Manager bills per million DNS queries plus a monthly fee per monitored endpoint, with external endpoints costing more to check. It is a cheap DNS-based global router. Here is how it prices and where cost hides with many endpoints.
Secret Manager bills about $0.06 per active secret version per location per month and about $0.03 per 10,000 access operations. It is cheap unless you store many versions or access secrets on every request. Here is the model and the caching lever.