Cast AI

Most FinOps tools do little more than dump colorful cost charts into your lap and generate endless Jira tickets telling you to resize your Kubernetes pods. Cast AI ditches passive monitoring for autonomous execution. Instead of leaving SREs and DevOps engineers to manually tweak YAML files, the platform ingests telemetry, cost data, and SLO metrics to rightsize workloads and orchestrate nodes directly inside your production clusters.

At the core of the service is the Cast AI Engine, a predictive machine learning model trained on operational datasets from thousands of clusters and millions of workloads. Rather than relying on rigid threshold rules, the engine tracks actual resource utilization down to the millicore. It predicts Spot instance interruptions up to 30 minutes in advance, migrating workloads gracefully before cloud providers yank capacity. It also tackles container bin-packing through its Evictor component, packing pods tightly to eliminate underutilized machines and automatically falling back to On-Demand capacity when spot pools run dry.

While specialized AI compute clouds like Together AI or Novita AI focus on offering managed GPU instances and serverless model endpoints, Cast AI takes charge of the Kubernetes clusters you already run across AWS, Microsoft Azure, Google Cloud, and Oracle Cloud. It brings that same autoscaling discipline to AI and machine learning infrastructure: you get GPU sharing through time-slicing, NVIDIA Multi-Instance GPU (MIG), Dynamic Resource Allocation (DRA), and smooth orchestration for LLM inference workloads inside your private VPC.

What the platform delivers:

• Autonomous workload rightsizing: Continuously tunes CPU and RAM requests, limits, and replica counts on the fly, preventing out-of-memory crashes and resource starvation without unnecessary restarts.
• Intelligent cluster autoscaling: Automates node provisioning, aggressive bin-packing, and cluster hibernation during non-working hours, slashing compute bills by up to 70%.
• GPU infrastructure optimization: Squeezes maximum density out of hardware using GPU time-slicing, multi-cloud GPU connectivity, and granular cost attribution per namespace and project.
• OpsPilot and agentic runbooks: Uses an interactive AI assistant and self-healing runbooks to diagnose cost anomalies, resolve configuration drift, and fix operational failures under human approval workflows.
• Enterprise visibility and compliance: Ships with free namespace-level cost monitoring, SOC 2 compliance, and ISO 27001 certification, alongside native integrations for Terraform, Helm, Crossplane, Prometheus, and Grafana.

Keep in mind that enabling full autonomy means granting substantial IAM and RBAC privileges in your cloud environments so the platform can provision nodes and mutate workloads. On top of that, running aggressive pod evictions and consolidation demands properly configured Pod Disruption Budgets (PDBs) and thorough validation on staging clusters to keep critical services completely stable.