GCP vs AWS for ML without an MLOps team: 7 setups ranked for small teams in 2026

For small teams without dedicated MLOps staff, the comparison recommends Google Cloud Vertex AI as the simplest greenfield option because it consolidates training, registries, pipelines, and endpoints under a relatively unified service and IAM model, while Amazon SageMaker AI is presented as the better fit for organizations whose data, networking, compliance requirements, and cloud commitments already reside on AWS. The central decision factor is data gravity, since transferring large datasets and rebuilding identity and networking across clouds can outweigh modest GPU price differences. Managed Kubernetes options such as GKE Autopilot and EKS with Karpenter offer portability and lower costs for sustained inference workloads but require teams to manage upgrades, GPU configuration, autoscaling, monitoring, and on-call operations. Qovery is positioned as a middle path for deploying containerized inference workloads in a team’s own cloud account while reducing Kubernetes operational burden, though it does not replace managed training, feature stores, model registries, or AutoML. Serverless GPU providers including Modal, Replicate, and Hugging Face Inference Endpoints are described as fastest for prototypes and irregular traffic because they scale to zero, but can become more expensive than committed cloud capacity as utilization rises. Across all options, the analysis emphasizes that idle GPU time is usually a larger cost concern than AWS-versus-GCP pricing, and advises teams to choose based on data location, need for managed ML capabilities, traffic pattern, compliance requirements, and responsibility for production incidents.

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