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The latest News and Information on Cost Management and related technologies.

Enterprises are making their biggest AI bets blind

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value. On July 14, IBM had its worst trading day since 1987.

What is AI cost observability? A guide to tracking LLM and AI spend

AI cost observability is the practice of measuring, attributing, and analyzing AI workload costs at the request, model, and workflow level in real time. It connects cloud infrastructure spend, inference and token costs, and business attribution (cost per feature, team, customer, or product) so engineering, finance, and product teams can see where AI spend goes and whether it creates value.

ManageEngine CloudSpend tutorial: Cost allocation report for AWS, Azure, and GCP

Learn how to use the Cost Allocation report in ManageEngine CloudSpend to accurately split, track, and attribute your multi-cloud spend across AWS, Azure, and GCP. This step-by-step tutorial shows you how to create a cost allocation, choose accounts, apply labels, configure allocation levels, and read the hierarchical allocation report by cloud, account, and region. Cost allocation is the foundation of FinOps. It tells you exactly which teams, projects, and cost centers are driving your cloud bill so you can charge back, budget, and optimize with confidence.

Application monitoring tools in 2026: APM, observability, and AI monitoring compared

Application monitoring tools track your application's health, speed, errors, and resource usage in real time. Also called APM tools or application performance monitoring software, these tools are essential for any team running production workloads. The leading options in 2026 are Datadog, New Relic, Dynatrace, Grafana, and Elastic APM for traditional workloads, plus Arize AI, LangSmith, and Weights & Biases for AI observability.

Why UK Businesses are Shifting Away From the Public Cloud Cost Model

Over the past five years, one of the most consistently tracked figures in the UK business technology sector has been the flight from public cloud. Barclays' 2021 CIO survey revealed that 43% of enterprises plan to shift workloads away from public cloud. By 2024, that had grown to 83%.

Deployment strategies explained: types, trade-offs, and what each one actually costs

A deployment strategy is the method an engineering team uses to release new software to production. The six core deployment strategies are recreate (big bang), rolling update, blue-green, canary, A/B testing, and shadow deployment. Each trades off between downtime risk, rollback speed, infrastructure cost, and complexity. This guide covers all six along with what each strategy actually costs in cloud and AI infrastructure spend.