When Do AI SaaS Products Need Kubernetes?
Kubernetes becomes necessary when: you're running local AI models at scale, you have multiple AI services to orchestrate, you need GPU workload scheduling, or your inference traffic has significant variability. For API-based AI (OpenAI/Anthropic), you likely don't need Kubernetes — Vercel handles it.
Kubernetes AI Architecture Overview
A typical AI SaaS on Kubernetes: ingress → API server pods → queue (Kafka/NATS) → inference worker pods (GPU) → cache layer (Redis) → database.
Setting Up GPU Node Pools
On GKE: create a node pool with NVIDIA A100 or L4 GPUs. Label nodes with accelerator: nvidia-gpu. Your inference pods request GPUs with: resources.limits["nvidia.com/gpu"]: 1.
Model Serving: vLLM
vLLM is the best open-source LLM inference server for production: PagedAttention for memory efficiency, continuous batching for throughput, compatible with OpenAI API format (easy migration from OpenAI). Deploy as a Kubernetes Deployment with GPU node affinity.
Horizontal Pod Autoscaling for AI
Standard CPU/memory HPA doesn't work well for AI. Use KEDA (Kubernetes Event-Driven Autoscaling) with custom metrics: queue depth, GPU utilization, inference latency. Scale from 0 pods (cost savings) to N pods in under 60 seconds.
Cost Optimization
- Use spot/preemptible GPU instances for batch inference (60–70% cheaper)
- Schedule batch jobs during off-peak hours
- Use node auto-provisioning to right-size clusters
- Cache model weights in a shared PVC to avoid re-loading per pod