Cloud Optimization | Hokstad Consulting

Cloud Optimization

Blog posts in the Cloud Optimization category

Preemptible VMs for Rendering Farms: 5 Cost Models

Compare five preemptible VM models for render farms to find true cost per finished frame, accounting for retries and deadline risk.

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Multi-Cluster Kubernetes Config Management Guide

GitOps for multi-cluster Kubernetes: use bases/overlays, labels for targeting, external secret references, and promote immutable releases.

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Cluster Autoscaling for Zero Downtime Deployments

Configure rolling updates, probes and cluster headroom so autoscaling can schedule surge Pods and keep Kubernetes rollouts zero‑downtime.

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9 Free Cloud Cost Auditing Tools

Use native cloud billing first; add open-source tools only to fill gaps for multi‑cloud, Kubernetes, governance or reporting.

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Top 7 Kubernetes Rightsizing Tools 2026

Compare seven Kubernetes rightsizing tools by control model, automation and cost impact to pick a safe, practical fit for your team.

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Custom Dashboards for Cloud Monitoring

Design role-based cloud dashboards that prioritise service health and spend, link alerts to logs and runbooks, and reduce noise.

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Shared vs Dedicated Runners: Cost Comparison

Compare shared vs dedicated CI runners by total delivery cost—minutes, queue delays, developer wait time and upkeep.

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Hybrid Cloud CI/CD: Tool Comparison

Hybrid CI/CD assessment by deployment flow, self‑hosted execution, package governance, compliance and cost.

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About IBM Cloud Global Load Balancers

DNS-based global load balancers prevent regional outages, reduce latency and unify multi-region services under one hostname.

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AWS vs Azure vs GCP: IAM Feature Comparison

Compare IAM models, conditional controls, logging and access reviews across AWS, Azure and GCP to pick the right cloud identity approach.

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Cost Optimization in Event-Driven Orchestration

Send fewer, smaller and better‑routed events to cut cloud event costs—shrink payloads, cap retries, limit fan‑out and tighten retention.

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Commitment Discount Forecasting for AI Workloads

Size cloud commitments to the steady AI usage floor, favour inference, use 60–90 day baselines and stagger renewals.

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