Can I host models on AWS without using SageMaker?

Yes. You can host models on AWS without SageMaker in several ways, depending on how much control you want and what kind of workload you have.

Common options:

• EC2
  Run your own inference server on a VM. Good if you want full control over the OS, dependencies, and GPU setup.

• ECS / EKS
  Package your model server in a container and run it on Amazon ECS or Kubernetes on EKS. Good for scalable, containerized deployments.

• Lambda
  Works for lightweight models or preprocessing/inference with short execution times. Not ideal for large models or GPU needs.

• AWS Batch
  Better for offline/batch inference rather than real-time serving.

• Elastic Beanstalk
  Easier app deployment, but less common for serious model serving compared with ECS/EKS/EC2.

• Managed GPU instances + your own serving stack
  For example, use EC2 GPU instances with tools like:

  • TensorFlow Serving
  • TorchServe
  • NVIDIA Triton Inference Server
  • FastAPI/Flask + your model code

If you want, I can recommend the best AWS setup based on:

• model type (PyTorch, TensorFlow, LLM, etc.)
• real-time vs batch
• CPU vs GPU
• expected traffic and latency requirements