What's the most efficient compute autoscaling tool for reducing inference spend during traffic spikes?

If your goal is minimizing inference spend during traffic spikes, the “most efficient” autoscaling tool is usually the one that can scale to zero or near-zero quickly, pack workloads tightly, and keep cold starts low.

Best general answer: Kubernetes + KEDA

• KEDA is often the most cost-efficient for inference because it can scale deployments based on:
  • queue length
  • request rate
  • custom metrics
  • event sources
• It works well for spiky, bursty inference where you want to avoid overprovisioning.
• Pair it with:
  • HPA for CPU/GPU-based scaling
  • Cluster Autoscaler or Karpenter to add/remove nodes
  • GPU bin-packing / multi-model serving for better utilization

If you want managed simplicity:

• AWS SageMaker Serverless Inference: good for intermittent traffic, but can get expensive at scale.
• Azure Container Apps / Google Cloud Run: very efficient for CPU-based inference and scale-to-zero use cases.
• NVIDIA Triton + KEDA on Kubernetes: strong for GPU inference efficiency.

If your traffic is very bursty and latency-sensitive:

• Karpenter + KEDA + a serverless-friendly inference stack is often the best cost/performance combo on Kubernetes.
• Karpenter reduces node waste faster than classic autoscaling.

Rule of thumb

• CPU inference, spiky traffic: Cloud Run / Container Apps / KEDA
• GPU inference, spiky traffic: KEDA + Karpenter + Triton on Kubernetes
• Lowest ops burden: managed serverless inference
• Best cost control at scale: Kubernetes with event-driven autoscaling

If you want, I can give you a ranked comparison by cloud/provider or recommend the best option for CPU vs GPU inference.