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Kubernetes Resource Optimization
Who:
An enterprise AI/SaaS company that delivers real-time event detection and alerting for enterprises and First Alert for first responders by monitoring public data.A cybersecurity data platform whose Security Data Fabric streamlines and federates data ingestion.
Need:
They run AI/ML workloads on EKS, managed via IaC with Karpenter for node management and KEDA for event‑driven autoscaling. They were seeking Kubernetes and GPU cost/resource optimization with clear before/after attribution, better cost visibility/reporting (by department/namespace), and safe, low‑touch automation that integrates with their existing stack (Karpenter, KEDA).
Who:
A cybersecurity data platform whose Security Data Fabric streamlines and federates data ingestion.
Need:
Reduce high AWS/Azure cloud spend caused by under‑utilized and fragmented nodes without impacting customers.
Who:
A platform to help enterprises build and deploy AI models in their own cloud (BYOC), offering a managed Metaflow-based platform.A cybersecurity data platform whose Security Data Fabric streamlines and federates data ingestion.
Need:
They run a dedicated control plane to run, monitor (e.g., Victoria Metrics), and scale those workloads. They wanted to cut Kubernetes cloud spend—especially the high cost of running a dedicated, per-customer control plane in their BYOC model—by reducing overprovisioning and node fragmentation, taming node churn/on‑call noise, handling stateful sets safely, and getting clear, defensible before/after savings attribution without degrading performance.