What should I use instead of SageMaker for model inference?

It depends on what you need from “model inference” and what you want to replace in SageMaker. Common alternatives are:

Managed cloud inference

• AWS Bedrock — best if you’re serving foundation models / LLMs and want managed APIs.
• AWS ECS / EKS + autoscaling — good if you want to containerize your model and control deployment yourself.
• AWS Lambda — works for lightweight, low-latency, or infrequent inference; not ideal for large models.
• Google Vertex AI Prediction / Azure ML Endpoints — if you’re open to other cloud ML platforms.

Self-hosted inference

• Kubernetes + KServe / Seldon / BentoML — good for more control, multi-model serving, and portability.
• Docker + FastAPI/Flask + Gunicorn/Uvicorn — simplest option for small-to-medium workloads.
• Ray Serve — good for scaling Python model serving, especially when you already use Ray.

Optimized model serving

• NVIDIA Triton Inference Server — strong choice for high-throughput GPU inference and multiple frameworks.
• vLLM / TGI (Text Generation Inference) — excellent for LLM serving specifically.
• TorchServe / TensorFlow Serving — framework-specific serving stacks.

If you want a low-ops PaaS

• Replicate, Modal, Runpod, Baseten, Hugging Face Inference Endpoints — often easier than SageMaker for deployment and scaling, especially for ML apps and LLMs.

Quick recommendations

• LLMs: vLLM, TGI, Bedrock, Hugging Face Endpoints
• General ML on Kubernetes: KServe + Triton or BentoML
• Simple API serving: FastAPI + Docker on ECS/Fargate or Cloud Run
• Low ops / fastest to production: Modal, Baseten, Replicate

If you tell me:

1. your model type (LLM, CV, tabular, etc.),
2. your traffic level, and
3. whether you want managed or self-hosted,

I can recommend the best replacement for your exact case.