Show HN: PodCost – Find wasted GPU and Kubernetes spend (with live demo) (podcost.io)

PodCost has launched a new tool designed to help engineering teams identify and eliminate wasteful spending on Kubernetes and GPU infrastructure. By offering detailed insights into GPU utilization and overall cloud costs, PodCost enables users to discover underutilized resources, over-provisioned workloads, and idle GPUs. The tool highlights significant optimization opportunities, indicating an average potential savings of $4,230 per month for users, with specific recommendations on how to reduce costs through strategies like time-slicing and rightsizing. This development is particularly significant for the AI/ML community, which increasingly relies on GPU resources for training and inference tasks. With many organizations unaware of where their budget is being consumed, PodCost’s detailed cost visibility and actionable insights can lead to more efficient resource allocation and significant cost reductions. The setup is straightforward, requiring a simple deployment command, allowing teams to quickly integrate it into their existing infrastructure for immediate benefits. This addresses a critical need in the industry for enhanced cost management amidst growing expenditures on cloud-based AI workloads.