SageMaker and Vertex AI are the flagship ML platforms of AWS and Google Cloud respectively. Both provide end-to-end ML capabilities from data preparation through deployment and monitoring. This comparison maps their services and highlights where each platform excels.

Two naming changes matter for readers in 2026. On December 3, 2024, AWS renamed its build, train, and deploy service to Amazon SageMaker AI and reused the name Amazon SageMaker for a new unified platform for data, analytics, and AI (SageMaker Unified Studio, SageMaker Lakehouse, and SageMaker Catalog, with SageMaker AI and Amazon Bedrock inside it). The API namespaces, CLI, and IAM prefixes still use sagemaker for backward compatibility. On the Google side, at Google Cloud Next 2026 (April 2026) Google began rebranding Vertex AI as the Gemini Enterprise Agent Platform, folding the former Vertex AI surfaces under it. The training, deployment, Model Garden, and pipeline capabilities described below still exist, and most documentation and SDKs still carry the Vertex AI name, so this page uses both. This comparison covers the ML platform layer: SageMaker AI versus Vertex AI.

Service Mapping

CapabilitySageMaker AIVertex AI
NotebooksSageMaker Studio (JupyterLab 4)Vertex AI Workbench, Colab Enterprise
TrainingSageMaker Training JobsVertex AI Training (Custom Jobs)
Hyperparameter tuningSageMaker Automatic Model TuningVertex AI Vizier
Model hostingSageMaker EndpointsVertex AI Endpoints
Batch inferenceSageMaker Batch TransformVertex AI Batch Prediction
PipelinesSageMaker PipelinesVertex AI Pipelines (Kubeflow-based)
Feature storeSageMaker Feature StoreVertex AI Feature Store
Model registrySageMaker Model RegistryVertex AI Model Registry
Experiment trackingSageMaker ExperimentsVertex AI Experiments
AutoMLSageMaker AutopilotVertex AI AutoML
Data labelingSageMaker Ground TruthVertex AI Data Labeling
Foundation modelsAmazon Bedrock (separate service)Model Garden (now under Gemini Enterprise Agent Platform)

Training

SageMaker Training supports any framework via custom Docker containers. Built-in algorithms (XGBoost, Linear Learner, etc.) are available. Distributed training is supported with SageMaker’s data parallelism and model parallelism libraries. Spot instance training reduces costs by up to 90%.

Vertex AI Training supports TensorFlow, PyTorch, XGBoost, and scikit-learn with pre-built containers. Custom containers are also supported. Distributed training uses standard framework distribution strategies. Preemptible VMs provide cost savings similar to spot instances.

Comparison: Both are capable. SageMaker has more built-in algorithms. Vertex AI’s Kubeflow integration gives it an edge for teams already using Kubernetes.

Model Deployment

SageMaker Endpoints offer real-time, serverless, and asynchronous inference patterns. Auto-scaling is based on CloudWatch metrics. Multi-model endpoints serve multiple models from a single endpoint. Shadow testing is available for safe model updates.

Vertex AI Endpoints offer real-time and batch prediction. Auto-scaling is based on prediction traffic. Traffic splitting between model versions enables A/B testing natively. Private endpoints within VPC are supported.

Comparison: SageMaker offers more deployment patterns (serverless, asynchronous). Vertex AI’s built-in traffic splitting is simpler for A/B testing.

MLOps and Pipelines

SageMaker Pipelines is a purpose-built ML workflow service. Pipelines are defined in Python using the SageMaker SDK. Integration with SageMaker’s training, processing, and model registry is native. Conditional execution and caching are supported.

Vertex AI Pipelines is based on Kubeflow Pipelines. Pipelines are defined using the Kubeflow Pipelines SDK or TFX. This means existing Kubeflow pipelines can run on Vertex AI with minimal changes. The ecosystem of Kubeflow components is available.

Comparison: SageMaker Pipelines is more tightly integrated with AWS services. Vertex AI Pipelines benefits from the open Kubeflow ecosystem and is more portable across environments.

AutoML

SageMaker Autopilot automatically tries different algorithms and hyperparameters, providing ranked models with explanations. Supports tabular data. Generates notebooks showing the code for each approach.

Vertex AI AutoML supports tabular data, images, text, and video. Broader modality support than Autopilot. Produces models that can be deployed directly to Vertex AI Endpoints. Vertex AI also added a Data Science Agent inside Colab Enterprise that automates exploratory data analysis and ML tasks (generally available May 2026).

Comparison: Vertex AI AutoML supports more data types. SageMaker Autopilot provides better transparency into what it tried.

Foundation Models

Amazon Bedrock (separate from SageMaker AI) provides API access to foundation models from Amazon (Nova and Titan), Anthropic (Claude), Meta (Llama), Mistral AI, Cohere, AI21 Labs, Stability AI, OpenAI, DeepSeek, Qwen, Writer, Luma AI, and TwelveLabs. Managed RAG (Knowledge Bases), Guardrails, and agents are included.

Vertex AI Model Garden provides access to over 200 models, including Google’s Gemini family, Anthropic’s Claude models, Meta’s Llama and Google’s open Gemma models, plus many open weight and partner models. It is now part of the Gemini Enterprise Agent Platform, and select proprietary partner models can be self-deployed inside your own VPC.

Comparison: Bedrock is a standalone service with a clean, focused interface. Vertex AI Model Garden is integrated into the broader Google Cloud AI platform. Model selection is broadly comparable; Bedrock pairs naturally with Amazon’s own Nova models and has deep Anthropic integration, while Vertex AI is the native home for Google’s Gemini models.

Pricing

Both platforms charge for compute time (training and inference), storage, and additional services. General patterns:

Training: Similar pricing for comparable GPU instances. Both offer discounted preemptible/spot pricing.

Inference: SageMaker charges per instance-hour for real-time endpoints. Vertex AI charges per node-hour with similar pricing. Both offer auto-scaling.

Notebooks: SageMaker Studio notebooks charge for the underlying instance. Vertex AI Workbench charges similarly.

Cost differences between platforms are usually smaller than cost differences from right-sizing instances and using spot/preemptible pricing.

Ecosystem and Integration

SageMaker integrates deeply with the AWS ecosystem: S3 for storage, IAM for security, CloudWatch for monitoring, Step Functions for orchestration, Lambda for serverless processing.

Vertex AI integrates deeply with GCP: Cloud Storage, IAM, Cloud Monitoring, Dataflow for processing, BigQuery for analytics.

Decision Criteria

Choose SageMaker AI when you are on AWS, need the broadest set of deployment patterns, want tight integration with AWS services, or prefer SageMaker’s built-in algorithms and distributed training libraries. If you also want unified data, analytics, and AI in one workspace, the next-generation SageMaker (Unified Studio plus Lakehouse) is the broader umbrella.

Choose Vertex AI when you are on Google Cloud, want Kubeflow compatibility, need AutoML for images/text/video, want Google’s Gemini models natively, or prefer BigQuery integration for data processing. Note that Vertex AI now sits under the Gemini Enterprise Agent Platform, so newer agent and orchestration tooling lives alongside the classic ML platform.

The honest answer: For most ML workloads, both platforms are capable. The right choice is usually determined by which cloud provider your organization already uses. Switching clouds for a marginally better ML platform is rarely worth the migration cost.

Sources and Further Reading