756 articles on cost optimization — what drives the cost, how it is priced, and where the savings actually are.
Not every optimization is worth the effort. Weighing the savings against the engineering time and risk, ROI, focuses effort on the high-value wins and avoids over-optimizing trivial costs. Here is how to prioritize.
A CDN's cost hinges on cache hit ratio: cache hits serve from the edge cheaply, while misses hit your origin, incurring origin egress and compute. Raising the hit ratio cuts both CDN and origin cost. Here is how.
Edge compute (Cloudflare Workers, Lambda@Edge, and others) runs code at CDN edge locations, priced by requests and compute time, often cheaply. It cuts origin load and latency but has constraints. Here is the cost model.
Managed DNS (Route 53, Azure DNS, Cloud DNS) prices by hosted zones plus queries, all inexpensive, with health checks and traffic policies adding cost. The cheapest is usually the one native to your cloud. This compares them.
NAT gateways bill an hourly fee plus per-GB data processing, so high-traffic private subnets accessing the internet or AWS services rack up processing charges. VPC endpoints, consolidation, and architecture cut it. Here is how.
PrivateLink (and Private Endpoints) provide private connectivity to services without traversing the internet, billing per endpoint-hour plus data processed. It improves security and can reduce egress, but endpoints add up. Here is the model.
A service mesh (Istio, Linkerd, and others) adds a control plane plus per-pod sidecar proxies that consume CPU and memory, so the cost is that overhead across your fleet. Lightweight meshes and sidecar-less modes cut it. Here is the model.
Connecting workloads across clouds means egress on both sides plus interconnect or VPN cost, so data moving between clouds is billed twice and adds latency. Minimizing cross-cloud data movement is the main lever. Here is how.
Bandwidth (data transfer) cost spans egress, cross-region, cross-AZ, and CDN transfer, and it hides across many services. Compression, caching, CDNs, in-region traffic, and committed transfer pricing cut it. Here is a complete playbook.
A VPC itself is free, but its components, NAT gateways, VPC endpoints, data transfer, and traffic mirroring, carry cost. Knowing which pieces bill, and optimizing them, keeps VPC networking cheap. Here is the breakdown.
Network topology, hub-and-spoke, mesh, flat, or segmented, determines data transfer paths and connectivity cost. A well-designed topology keeps traffic cheap and manageable; a poor one multiplies transfer and complexity. Here is how to design for cost.
GitLab CI bills compute minutes on shared runners (with a free allowance and cost factors by runner type), or you run self-managed runners on your own infrastructure. Caching, right-sized runners, and self-hosting for scale cut it. Here is the model.
Building container images consumes CI compute, and slow builds that rebuild everything waste minutes. Layer caching, small base images, and multi-stage builds cut build time and cost. Here is how.
Build artifacts, packages, and container images accumulate in storage, billed per GB, and unpruned artifacts grow the bill quietly. Retention policies, pruning, and tiering keep it flat. Here is how.
Feature flag platforms (LaunchDarkly, Split, and others) price by seats, monthly active users, or flag evaluations; self-hosted or open-source options trade that for infrastructure. Evaluation volume and MAU drive managed cost. Here is the model.
Application performance monitoring (Datadog, New Relic, and others) prices by hosts, ingested data, or usage, and it is a notoriously large observability bill. Host right-sizing, data filtering, and sampling cut it. Here is how.
Log platforms (Datadog Logs, Splunk, Elastic, Loki, cloud-native) price by ingestion, indexing, and retention, with very different models. Ingest less, index selectively, and tier retention to cut the bill. This compares the approaches.
Distributed tracing captures request paths across services, priced by spans or traces ingested, so high-traffic systems generate enormous volume. Intelligent sampling is the primary cost lever. Here is how.
Status pages communicate incidents and uptime to users, via hosted SaaS (per-subscriber or feature tiers) or self-hosted (open-source plus hosting). For most, hosted is cheap; self-hosted suits scale or customization. Here is the model.
A data pipeline's cost is the compute that ingests and transforms data, the storage it lands in, and the data transfer between stages. Processing only what changed, right-sizing compute, and efficient formats cut it. Here is how.
Data warehouse cost is compute (queries and transformations) plus storage, whether Snowflake credits, BigQuery bytes scanned, or Redshift nodes. Scanning less, right-sizing compute, and auto-suspend cut it. Here is how.
A data lake's cost is object storage (often huge volumes) plus the query engines that read it. Storage tiering, lifecycle policies, compaction, and efficient formats cut it. Here is how to keep a growing lake affordable.
Kafka cost is the broker compute, the storage of retained messages, cross-AZ replication transfer, and (if managed) the service premium. Retention, right-sized brokers, and tiered storage cut it. Here is how.
CDC streams inserts, updates, and deletes from source databases to downstream systems, priced by the volume of change data plus the connector compute. High-change sources and full-table snapshots drive the bill. Here is how to keep it lean.