AWS vs Azure: Enterprise Discount Models
Compare AWS EDP and Azure MACC: how to size commitments, manage exclusions, and time renewals to avoid costly contract shortfalls.
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Blog posts in the Resource Allocation category
Hybrid Cloud Cost Monitoring: Challenges and Solutions
Hybrid cloud cost control fails when billing, usage and ownership data sit in silos—unify models, enforce tags and allocate shared spend.
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How to Analyse Cloud Resource Utilisation
Measure CPU, memory, storage and network over 60–90 days, align monitoring with billing and tags, then act to remove idle cloud spend.
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Best Practices for Multi-Tenant Kubernetes Clusters
Treat isolation, quotas, autoscaling and cost reporting as one system to safely run shared Kubernetes clusters.
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How Automation Improves FinOps Cost Allocation
Automate tags, billing exports and allocation rules so every pound is assigned, unallocated spend drops and AAI reaches finance-grade 95%+.
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Service Mesh Resource Costs: Optimisation Guide
Measure and cut service mesh compute and observability costs by scoping the mesh, right-sizing sidecars and trimming telemetry.
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Kubernetes Resource Limits: Predictability vs Performance
Explains Kubernetes requests vs limits, CPU vs memory sizing, QoS choices and rightsizing to balance cost and reliability.
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Top KPIs for Cloud Spend Forecasting
Good cloud forecasts start with accuracy metrics, clean allocation and fast remediation to cut budget variance under 10%.
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Batch vs. On-Demand Workloads: Resource Allocation
Match Kubernetes allocation to workload: pack batch for high utilisation and reserve headroom for on-demand services.
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Serverless Cost Optimisation for Stateless Workloads
Cost-effective serverless is about doing less work per request, cutting invocations, and monitoring platform and logging costs continuously.
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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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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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