Kubernetes Resource Limits: Predictability vs Performance
Explains Kubernetes requests vs limits, CPU vs memory sizing, QoS choices and rightsizing to balance cost and reliability.
Read moreBlog posts in the Performance category
Explains Kubernetes requests vs limits, CPU vs memory sizing, QoS choices and rightsizing to balance cost and reliability.
Read moreTrack Deployment Frequency, Lead Time, Change Failure Rate and Time to Restore Service using SCM, CI/CD and incident data with automated baselines.
Read moreAI monitoring demands hybrid strategies to cut telemetry costs while improving detection speed and regulatory compliance.
Read morePractical strategies to reduce latency across AWS, Azure and GCP: region choice, private links, monitoring, routing and caching.
Read moreKeep core microservice functions running during failures using circuit breakers, fallbacks, timeouts, bulkheads and async workflows.
Read morePractical checklist to reduce API gateway latency and costs: connection reuse, compression, caching, tuning, monitoring and scaling.
Read moreEmbed observability, optimise queries, caching and storage, and add CI/CD checks and AI monitoring to cut latency and cloud costs in DevOps.
Read moreKubernetes traffic splitting lowers deployment risk and cost by routing a small share to a canary, monitoring and enabling quick rollbacks.
Read moreAI predicts CI/CD failures by spotting signals in logs, tests and metrics — shifting teams from firefighting to prevention.
Read moreHow CDN charges work and how to cut bills: bandwidth, request fees, cache misses, regional pricing, optimisation and monitoring.
Read moreRun fast, isolated unit tests early in CI/CD, use JUnit XML reports, parallelise suites and monitor flaky tests for reliable pipelines.
Read moreManage service mesh configs using version control, preflight validation, staged rollouts, observability and fast rollback to avoid outages.
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