Kubernetes AI FinOps — Make Production AI Affordable Without Breaking SLAs

Overwhelmingly to idle GPU time — accelerators reserved but not doing useful work. The other big buckets are oversized requests/limits that prevent bin-packing, agents and endpoints that never scale down, cold-start overhead, redundant computation from poor KV-cache reuse, over-large models for simple requests, and storage/network egress for weights and data. We quantify each before changing anything.

By tuning FinOps and SRE together. We improve GPU packing and utilisation (MIG, topology-aware placement, continuous batching), introduce scale-to-zero and suspend/resume where cold starts are acceptable (and mitigate them where they are not), route requests to right-sized models, and use Spot/preemptible with graceful interruption — always watching TTFT/TPOT and success-rate SLOs so savings never come at the cost of user experience.

Yes. We help you implement scale-to-zero with KEDA/Knative or inference-platform autoscalers, decide which workloads can tolerate cold starts, and reduce cold-start time (model caching, warm pools, faster weight loading) so scaling to zero is actually usable in production rather than a latency landmine.

Yes. We help you get per-namespace, per-workload, and per-request/token cost visibility (labels, GPU accounting, and tools like OpenCost or your cloud cost explorer) so you can show owners where the spend is and set budgets and quotas that hold.

Message us on WhatsApp with your cluster, GPU types, and the biggest cost worry. We will start from utilisation and cost data, find the largest avoidable spend, and work the fixes with you — same-day.