Amazon Web Services

AWS has the broadest ML service ecosystem — SageMaker for model training and deployment, Bedrock for foundation model access, and the deepest catalog of managed infrastructure services. For teams already invested in the AWS ecosystem, the integrated IAM, VPC, and data services reduce integration friction. We recommend AWS when your team already uses AWS, when you need fine-grained control over infrastructure, or when your use case benefits from SageMaker Pipelines or Bedrock. We recommend GCP when Vertex AI or BigQuery ML better fit the use case, and Azure for Microsoft-stack enterprises.

A production AWS AI architecture typically includes: SageMaker endpoints or ECS/EKS for model serving, S3 for data and artifact storage, a feature store, CloudWatch for observability, IAM roles for least-privilege access, and VPC isolation for sensitive workloads. We use Terraform or CDK for infrastructure-as-code, and build CI/CD pipelines that automatically retrain, evaluate, and promote models through staging to production.

Setting up a complete MLOps infrastructure on AWS (training pipeline, model registry, serving endpoint, monitoring, and CI/CD) typically takes 6-10 weeks. Migrating an existing AI system to AWS with production-grade infrastructure takes 4-8 weeks depending on system complexity. Simple SageMaker endpoint deployments for an existing model can be done in 2-3 weeks.