756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
S3 has a genuine free tier for the first year, but requests, data transfer out, and storage class choices shape the bill the moment you outgrow it. Here is what the free tier covers and what actually drives S3 cost.
Self-managed Kafka, MSK, and Kinesis price streaming very differently: broker instances plus replication transfer versus shards or throughput units. The cheapest choice depends on throughput, retention, and how much operational work you want. Here is the breakdown.
Both scale Kubernetes nodes, but Karpenter picks instance types and sizes dynamically while Cluster Autoscaler scales fixed node groups. The difference shows up as bin-packing efficiency, spot usage, and idle waste. Here is how each affects the bill.
A shared cluster hides which team spends what. Splitting cost by namespace, using resource requests, actual usage, and node cost, turns one opaque bill into per-team accountability. Here is how namespace cost attribution works and where it gets tricky.
A pod has no price tag; it inherits a slice of its node's cost based on the CPU and memory it requests or uses. Understanding that translation reveals why over-requesting is expensive and where pod-level waste hides. Here is the model.
The gap between the node capacity you buy and the pod usage you actually run is idle cost, and in many clusters it is the single largest source of waste. Here is where idle capacity comes from and how to shrink it.
Resource requests, not actual usage, decide how many nodes Kubernetes buys, so they are the single biggest lever on cluster cost. Set them too high and you pay for idle capacity; too low and you risk instability. Here is how to get them right.
Spot nodes can cut Kubernetes compute cost by well over half, but they can be reclaimed at short notice, so they suit fault-tolerant workloads with graceful handling. Here is how to use spot safely and how much it saves.
Running Lambda functions on ARM-based Graviton instead of x86 costs about 20 percent less per unit of compute, and many functions run just as fast or faster. Here is how the savings work, what to check before switching, and how to migrate.
Lambda bills on requests and on compute measured as memory times duration, with memory also determining CPU. Understanding that model reveals why right-sizing memory, cutting duration, and choosing ARM are the levers that control serverless cost. Here is the full picture.
RDS Multi-AZ roughly doubles instance and storage cost by running a standby in a second Availability Zone for automatic failover. Here is what you actually pay for, and when the availability is worth the premium.
Each RDS read replica is a full additional instance with its own compute and storage cost, plus data transfer for cross-region replicas. Here is how read replica pricing works and how to scale reads without overpaying.
Redshift bills for provisioned or serverless compute plus storage; Snowflake bills for compute credits by the second with separate storage, pausing when idle. Which is cheaper depends on your query pattern and utilization. Here is the comparison.
A reserved instance only saves money if it is used. Low utilization means paying for commitment you are not applying, quietly erasing the discount. Here is how to measure, maintain, and maximize RI utilization.
Kubernetes pod requests reserve node capacity whether used or not, so over-set requests waste cluster resources and money. Right-sizing requests and limits to real usage packs more work onto fewer nodes. Here is how.
Lambda memory controls both cost and CPU, so the cheapest setting is not always the lowest. Right-sizing memory to minimize cost per invocation, factoring in faster execution, is a real optimization. Here is how to tune it.
RDS instances are often provisioned larger than the workload needs, and unlike stateless compute, resizing a database takes care. Here is how to right-size RDS instance class, storage, and IOPS to real usage without risking the database.
Route 53 charges per hosted zone, per million queries, and for health checks, so DNS cost is usually small but can surprise you at high query volume or with many zones and health checks. Here is the full pricing picture.
S3 Intelligent-Tiering moves objects between access tiers automatically based on usage, cutting storage cost for data with unknown or changing access patterns, for a small monitoring fee. Here is how it works and when to use it.
S3 lifecycle policies automatically transition objects to cheaper storage classes and expire them on a schedule, cutting storage cost for data with a known access pattern. Here is how to design policies that save without losing what you need.
S3 charges per request, not just per GB stored, so a workload making millions of small GET, PUT, or LIST calls can pay more for requests than for storage. Here is why it happens and how to cut request cost.
S3 offers storage classes from Standard to Glacier Deep Archive, trading storage price against retrieval cost and latency. Matching each object to the right class is a major cost lever. Here is how the classes compare.
SageMaker cost spans notebooks, training jobs, and always-on inference endpoints, and each has its own trap. Idle notebooks and over-provisioned endpoints are the usual culprits. Here is how to optimize each stage.
Hosted CI runners are billed per minute and get expensive at high build volume; self-hosted runners on your own compute can cost far less per minute but add operational and idle-capacity overhead. Here is the tradeoff.