3 verified briefings on Finops. Each story includes a plain-English summary, why it matters, and the concrete action engineering teams should take.
Standard cloud cost-saving practices, like downsizing underused GPUs, don't apply to secure AI training. The usual utilization metrics can be misleading for these specialized workloads, creating a blind spot for FinOps teams and leading to incorrect infrastructure decisions.
Traditional FinOps practices often recommend downsizing resources with low utilization. However, for certain AI workloads like secure machine learning, low GPU compute usage can be misleading. These tasks may be memory-bound, not compute-bound, making "underutilized" GPUs essential for performance and avoiding higher costs.
Apache Kafka is evolving into a cloud-native platform. This shift involves tiered storage for cost efficiency, better financial operations (FinOps) telemetry, and elastic scaling. Architects are also exploring a future where Kafka could operate without local disks, changing its core operational model.