model serving platform with autoscaling

A model serving platform with autoscaling is infrastructure that:

1. Hosts ML models behind an API
2. Scales instances up/down automatically based on traffic, latency, CPU/GPU usage, queue depth, or custom metrics

Common building blocks

• Model registry/artifact store: S3, GCS, Azure Blob, MLflow, Hugging Face, etc.
• Serving layer: REST/gRPC inference service
• Autoscaler: adds/removes pods or nodes
• Scheduler/orchestrator: Kubernetes is the most common
• Monitoring: Prometheus, Grafana, CloudWatch, Datadog
• Load balancer / API gateway: routes requests to replicas

Popular platforms

Kubernetes-based

• KServe: Kubernetes-native model serving with autoscaling, can scale to zero
• Seldon Core: model deployment and traffic management
• Ray Serve: good for Python-native model serving and autoscaling
• BentoML + Kubernetes: packaging plus autoscaling on K8s

Managed cloud services

• AWS SageMaker Endpoints: autoscaling supported
• Google Vertex AI Endpoints: autoscaling supported
• Azure ML Online Endpoints: autoscaling supported

GPU-heavy / high-throughput

• NVIDIA Triton Inference Server + Kubernetes autoscaling
• TensorFlow Serving / TorchServe behind K8s HPA/KEDA

Autoscaling strategies

• CPU/GPU utilization
• Request rate
• Concurrent requests
• P95/P99 latency
• Queue length
• Custom business metrics
• Scale-to-zero for cost savings on idle models

Typical architecture

Client → API Gateway → Load Balancer → Model Serving Pods → Model Store
                                             ↘ Monitoring/Autoscaler

If you want to build one

A practical stack is:

• Kubernetes
• KServe or Ray Serve
• KEDA or HPA
• Prometheus + Grafana
• S3/GCS for model artifacts

If you want, I can also give you:

1. a reference architecture diagram
2. a Kubernetes deployment example
3. a platform comparison table
4. a recommended stack for LLMs vs classic ML models