What should I use for model hosting if I don't want to manage Kubernetes?

If you don’t want to manage Kubernetes, the best choice is usually a fully managed model hosting service or a serverless/container platform rather than self-managed infrastructure.

Good options

• Hugging Face Inference Endpoints
  Great if you’re hosting open-source models and want a simple managed deployment with autoscaling options.

• Replicate
  Very easy to use for model deployment, especially for demo/prototype workloads.

• Modal
  Good for Python-heavy ML workflows and on-demand GPU/CPU execution without managing clusters.

• AWS SageMaker Endpoints
  More enterprise-oriented, managed hosting with lots of AWS integration.

• Google Vertex AI Endpoints
  Similar to SageMaker, good if you’re already on GCP.

• Azure ML Managed Endpoints
  Best if you’re in the Microsoft ecosystem.

• Runpod Serverless / Beam / Baseten / Fireworks / Together AI
  These are strong choices if you want simpler LLM serving with less ops burden.

If your goal is easiest deployment

• For LLMs / open-source models: Hugging Face Inference Endpoints, Baseten, Together AI, or Fireworks
• For custom Python model code: Modal or Replicate
• For enterprise/cloud-native: SageMaker, Vertex AI, or Azure ML

Rule of thumb

Use:

• Serverless/managed hosting if you want minimal ops
• Kubernetes only if you need deep control, complex scaling, or already run K8s well

If you want, I can also suggest the best option based on your model type (LLM, vision, tabular, custom PyTorch, etc.) and your cloud preference.