Cloud Optimization | Hokstad Consulting

Cloud Optimization

Blog posts in the Cloud Optimization category

5 Feature Selection Methods for Cloud Spend Models

Compare five feature-selection methods to improve cloud cost forecasts, reduce complexity and boost accuracy.

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Top 5 Metrics for Cloud Cost and DevOps Efficiency

Measure five DevOps-focused cloud metrics—cost per deployment, unit cost, utilisation, deployment speed and MTTR—to cut cloud spend and boost delivery.

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Checklist for API Gateway Performance Tuning

Practical checklist to reduce API gateway latency and costs: connection reuse, compression, caching, tuning, monitoring and scaling.

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EKS Cost Optimisation with Spot Instances

Cut EKS compute costs using Spot Instances, Karpenter/Autoscaler, interruption handling and node strategies for resilient savings.

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Best Practices for Data Access Optimisation in DevOps

Embed observability, optimise queries, caching and storage, and add CI/CD checks and AI monitoring to cut latency and cloud costs in DevOps.

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Data Replication: Cost vs Performance

Compare replication strategies, storage tiers and network costs to balance performance with cloud expenses.

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Cloud-Native Services for FinOps Compliance

Automate tagging, budgets, policy enforcement and audit-ready reporting with cloud-native tools for FinOps compliance and cost control.

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Custom Strategies for Multi-Cloud Pricing Negotiation

Cut multi-cloud overspend with consolidated spend data, workload rightsizing, egress negotiations and flexible contract terms.

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Spot Instances vs Reserved Instances for CI/CD Scaling

Balance cost and reliability in CI/CD by using Reserved Instances for baseline capacity and Spot Instances for burst savings.

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AWS Cost Allocation Tags: Best Practices

Plan, enforce and analyse AWS cost allocation tags with practical naming, governance, automation and reporting best practices.

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Multi-Cloud Compliance: Role of Policy Versioning

Track, test and enforce versioned policy-as-code across AWS, Azure and GCP to simplify audits, prevent drift and reduce compliance risk.

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AI in Multi-Cluster Workload Scheduling

AI forecasts demand to optimise multi-cluster Kubernetes scheduling—cutting cloud costs, improving GPU job throughput and enforcing UK data rules.

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