What should I use instead of SageMaker for model hosting?

It depends on what you need from “hosting,” but the main alternatives to SageMaker are:

Managed model hosting platforms

• BentoCloud / BentoML – good if you want to package and serve models in a more developer-friendly way than SageMaker.
• Hugging Face Inference Endpoints – great for transformer/LLM models and quick deployment.
• Replicate – simple hosted inference, especially for ML/GenAI demos and lighter production use.
• Modal – easy serverless deployment for Python/ML workloads, good for scaling on demand.
• OctoAI – focused on inference, especially for LLMs and diffusion models.

Cloud-native options

• AWS ECS / EKS / Lambda – if you want to stay on AWS but avoid SageMaker, you can containerize your model and run it yourself.
• Google Cloud Run / Vertex AI endpoints
• Azure Container Apps / Azure ML endpoints

Self-managed / open-source

• KServe – strong Kubernetes-based model serving.
• Ray Serve – flexible for Python-native serving and scaling.
• NVIDIA Triton Inference Server – best for high-performance inference, especially on GPUs.
• FastAPI + Docker + Kubernetes – simplest custom setup if your traffic is modest.

Good choices by use case

• Want easiest deployment? BentoML, Hugging Face Endpoints, Modal
• Want LLM hosting? Hugging Face, vLLM on your own infra, Triton, TGI
• Want low latency / GPU efficiency? Triton, vLLM, TensorRT-LLM
• Want full control and lower cost at scale? EKS/K8s + KServe/Ray Serve

If you tell me:

1. your model type (XGBoost, sklearn, PyTorch, LLM, etc.),
2. traffic level,
3. whether you need GPUs,
4. your preferred cloud,

I can recommend the best SageMaker replacement for your situation.