Common Workflow Monitoring Mistakes and Costly Impacts
Avoid common workflow monitoring mistakes that inflate costs and slow deployments. Prioritise key metrics, refine alerts and automate cost controls.
Read moreBlog posts in the Cloud Optimization category
Avoid common workflow monitoring mistakes that inflate costs and slow deployments. Prioritise key metrics, refine alerts and automate cost controls.
Read moreSpot instances can cut multi‑cloud compute costs by up to 90% using spare capacity, automation and a mixed capacity strategy while managing interruption risk.
Read moreGuide to testing a service mesh: validate configs, verify traffic routing and resilience, use observability, benchmark performance and add checks to CI/CD.
Read moreCut cloud analytics costs up to 90% by running fault‑tolerant workloads on spot instances while protecting data with external storage and mixed on‑demand strategies.
Read moreCompare total cost of ownership for open-source and proprietary CI/CD — licensing, infrastructure, maintenance, and when each option saves UK teams money.
Read moreFive CI/CD metrics—deployment frequency, lead time, change-failure rate, MTTR and pipeline duration—to spot bottlenecks, cut costs and boost developer productivity.
Read moreSecure hybrid cloud networks with VPNs: IPsec best practices, Site-to-Site and Remote Access setups, AWS/Azure integration, BGP failover, encryption and cost control.
Read morePractical guide to GitOps-driven multi-cluster CI/CD on hybrid clouds: topology, networking, IaC, secrets, observability and cost optimisation.
Read moreHow to measure vulnerability programmes: key KPIs (MTTD, MTTR, patch latency), case studies and best practices for risk-based prioritisation and faster remediation.
Read moreConnect FluxCD to CI pipelines: bootstrap Flux, secure Git access, enable image automation, and validate GitOps deployments for Kubernetes.
Read moreVPA best practices for Kubernetes: install, modes, resource policies, monitoring, and integrating with HPA/Cluster Autoscaler to reduce waste and stabilise workloads.
Read moreAI reduces recurring DevOps incidents with predictive alerts, automated root-cause analysis, alert deduplication and self-healing.
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