Continuous Delivery for Agile Teams: Guide
Keep every change release-ready: build once, test early, promote the same artefact, gate production and measure bottlenecks.
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Blog posts in the Cost Management category
AI Monitoring Tools: Cost vs Performance
AI monitoring demands hybrid strategies to cut telemetry costs while improving detection speed and regulatory compliance.
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How to Optimise Multi-Cluster Kubernetes Costs
Cut multi-cluster Kubernetes spend with visibility, consolidation, rightsizing, spot instances and automated governance.
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Chargeback Models for Shared Cloud Services
Practical guide to allocating shared cloud costs: define cost objects, pick allocation drivers, choose usage/fixed/hybrid models and start with showback.
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5 Steps to Align Cloud Costs with Financial Rules
Five steps to align cloud spending with UK accounting and regulations: tagging, allocation, automation, reporting and governance.
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Economic Models for Long-Term Cloud Cost Planning
Use driver-based models, unit economics and TCO to reduce cloud forecast variance and align costs with business metrics.
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5 Namespace Strategies for Cost Control
Use namespace design, quotas, labels and automation to improve cost visibility, right-size resources and cut Kubernetes spend.
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Usage-Based Cloud Cost Forecasting: Methods
Practical methods to forecast usage-based cloud costs using historical data, seasonality, predictive models and multi-cloud normalisation.
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Checklist for Using Spot Instances on AWS, Azure, GCP
Checklist for running spot instances on AWS, Azure and GCP: workload suitability, interruption handling, resilience and cost optimisation.
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Optimizing Reserved Instances for Mixed Workloads
Commit to proven baseline compute, layer RIs and Savings Plans, and keep 20–40% on‑demand to balance savings and flexibility.
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Metrics vs. Logs: Cost Implications in Observability
How metrics and logs drive observability costs and practical steps to cut spend: limit cardinality, filter logs, and use tiered storage.
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Machine Learning on AWS, Azure, and GCP
Compare AWS SageMaker, Azure ML and GCP Vertex AI for framework support, tooling, cost and integration to pick the right cloud for ML.
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