798 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
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.
Both Savings Plans and Reserved Instances discount EC2 in exchange for a commitment, but they differ in flexibility and how they apply. Savings Plans commit to a dollar-per-hour spend; RIs commit to specific capacity. Here is how to choose.
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.
Showback shows teams what they spend; chargeback bills it back to their budgets. Both make cloud cost accountable, but they differ in enforcement and organizational fit. Here is how to choose between them.
Snowflake cost is driven by virtual warehouse compute credits, and the biggest wastes are oversized warehouses and warehouses left running idle. Here is how to right-size, auto-suspend, and tune Snowflake spend.
Spot instances offer steep discounts off on-demand prices for spare capacity that AWS can reclaim with short notice. For fault-tolerant workloads the savings are large; the catch is interruption. Here is how to use spot safely.
SQS, SNS, and EventBridge all move messages between services but price differently, per request, per publish and delivery, and per event with routing. Which is cheapest depends on your fan-out and routing needs. Here is the comparison.
AWS Step Functions bills the Standard workflow type per state transition, so a workflow with many steps or high execution volume can cost more than expected. The Express type prices differently. Here is how to keep orchestration cheap.
T3a instances use AMD processors to deliver the same burstable profile as T3 Intel instances at a lower price. For most general-purpose workloads the swap is a simple, low-risk saving. Here is how they compare.
Consistent resource tags are what let you attribute cloud cost to teams, projects, and environments. Without them, cost is an unattributable lump. Here is how to design and enforce a tagging strategy that makes cost accountable.
Distributed tracing is billed by span or trace volume, so capturing every request gets expensive at scale. Intelligent sampling keeps the diagnostic value while cutting the volume you pay for. Here is how sampling affects cost.
AI cost divides into training (building the model, often GPU-heavy and periodic) and inference (running it, continuous and scaling with usage). They have different profiles and different levers. Here is how to think about the split.
An Azure invoice mixes compute, storage, networking, and service charges across subscriptions and resource groups, which makes it hard to read. Knowing the structure, and where the big and surprising lines come from, is the first step to control. Here is how to read it.
Most surprise AWS charges come from a short list of usual suspects, data transfer, NAT gateways, idle resources, request fees, and forgotten services. Knowing them turns a mystifying bill into a fixable one. Here is the catalog of surprises.
us-east-1 and us-west-2 are two of AWS's cheapest and most feature-complete regions, with pricing that is close but not identical. Here is how they compare on cost, and what else to weigh beyond the sticker price.
Vector databases for AI and search come as managed services, self-hosted engines, and add-ons to existing databases, each with a different cost model. Which is cheapest depends on scale, query volume, and operational appetite. Here is the comparison.
Vertex AI cost spans training jobs, online prediction endpoints, notebooks, and pipelines, and the biggest drains are always-on endpoints and idle notebooks. Here is how to optimize each part of GCP's managed ML platform.
Migrating VMware workloads to the cloud can mean a like-for-like lift onto managed VMware services or a re-architecture to native cloud. Each has a distinct cost profile, and recent VMware licensing changes have sharpened the math. Here is the breakdown.