What should I use for GPU-backed model hosting with autoscaling?

If you want GPU-backed model hosting with autoscaling, the best choice depends on whether you want a managed service or self-managed infrastructure.

Best managed options

• AWS SageMaker real-time endpoints
  Good if you’re already on AWS and want built-in autoscaling, GPU instance support, and managed deployment.

• Google Vertex AI endpoints
  Strong managed option on GCP with autoscaling and GPU support.

• Azure Machine Learning online endpoints
  Best fit if you’re in Azure and want managed deployment plus scaling.

• Hugging Face Inference Endpoints
  Nice for model-centric workflows; supports GPU instances and autoscaling in a simpler interface than cloud-native ML platforms.

Best self-managed / flexible options

• Kubernetes + KServe or Seldon + GPU nodes
  Best if you need portability, custom scaling logic, or multi-model serving. More ops work.

• Ray Serve on Kubernetes or VMs
  Good for Python-native deployments and dynamic scaling, especially if you’re already using Ray.

• NVIDIA Triton Inference Server
  Excellent for high-throughput GPU inference. Usually paired with Kubernetes/autoscaling tooling.

If you want the simplest answer

• For fastest time to production: Hugging Face Inference Endpoints
• For enterprise cloud-native: SageMaker / Vertex AI / Azure ML
• For maximum control and scale: Kubernetes + KServe/Triton

Things to check before deciding

• Autoscaling behavior: scale on GPU utilization, request rate, queue length, or concurrency?
• Cold start time: GPU nodes can take time to provision.
• Batching support: critical for good GPU efficiency.
• Framework compatibility: PyTorch, TensorFlow, vLLM, TensorRT, Triton, etc.
• Cost controls: scale-to-zero may not be available for GPU endpoints.
• Latency requirements: real-time chat/LLM serving often needs different infra than batch inference.

If you tell me:

1. your cloud provider,
2. model type (LLM, CV, embeddings, etc.), and
3. latency/traffic expectations,

I can recommend a more specific setup.