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.

Naming note (read this first): On April 22, 2026, Google rebranded Vertex AI as the Gemini Enterprise Agent Platform. It is not a new product running alongside Vertex AI - Google has said all Vertex AI services and roadmap work now ship exclusively through the Agent Platform, and the standalone Vertex AI documentation set is no longer being updated. Existing Vertex AI workloads, APIs, and SDKs keep working unchanged under the new name; there is no forced migration. See Google Vertex AI for the full rebrand story. This page keeps using “Vertex AI” for the ML-platform features below, since that is still what the SDKs, billing line items, and most console surfaces call them.

The AWS side has its own naming history worth knowing. 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. Separately, as of July 30, 2026, several older SageMaker AI features - Ground Truth, Model Monitor, Clarify, Debugger, Mechanical Turk, Amazon Augmented AI (A2I), Studio Lab, Role Manager, and Geospatial - closed to new customers and moved to maintenance-only status; existing users were unaffected at that point, and AWS pointed new customers to alternatives (SageMaker AI MLflow, Amazon Bedrock Evaluations/Guardrails, and open-source monitoring reference solutions) instead. Mechanical Turk has since gone further than “maintenance-only”: on August 25, 2026, AWS announced that the Mechanical Turk marketplace itself permanently closes on September 30, 2026 (requesters have until October 30, 2026 to approve or reject outstanding work). After that date the Mechanical Turk worker type is removed from both Ground Truth and A2I entirely - existing customers who relied on it, not just new ones, must move to a private or vendor workforce. This matters for the table below, since Ground Truth is SageMaker’s answer to Vertex AI Data Labeling.

On the Google side, as the note above describes, Vertex AI is now branded the Gemini Enterprise Agent Platform, adding agent-building tools (a code-first Agent Development Kit, a low-code Agent Studio, a managed Agent Runtime - called Agent Engine at launch - with Memory Bank for persistent agent memory) on top of the ML platform and Model Garden this page covers. (Google Agentspace is a separate rebrand: it became the Gemini Enterprise end-user app back in October 2025, distinct from this developer platform, so it isn’t part of what folded into the Agent Platform.) The training, deployment, Model Garden, and pipeline capabilities described below still exist under that umbrella, and most 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)

SageMaker Ground Truth stopped accepting new customers on July 30, 2026. Its Mechanical Turk workforce option goes further: Mechanical Turk permanently closes on September 30, 2026, so even existing Ground Truth customers who use that workforce type must switch to a private or vendor workforce by then. AWS has not published a suggested first-party replacement for new Ground Truth customers; Vertex AI Data Labeling is the closest managed equivalent on Google Cloud.

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) and Guardrails are included. For agents, note that the original Bedrock Agents feature was renamed Bedrock Agents Classic and closed to new customers on July 30, 2026; AWS now points new agent builds to Amazon Bedrock AgentCore , whose next-generation Runtime became generally available on September 18, 2026.

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 now one part of the wider Gemini Enterprise Agent Platform, sitting alongside the Agent Development Kit, Agent Studio, and Agent Runtime that Google added for building and running agents on top of those models - a broader, more agent-oriented surface than Bedrock’s. Model selection itself is broadly comparable; Bedrock pairs naturally with Amazon’s own Nova models and has deep Anthropic integration, while Vertex AI/Gemini Enterprise is the native home for Google’s Gemini models and also offers Claude.

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 is now branded the Gemini Enterprise Agent Platform (since April 22, 2026 - see the naming note above), so newer agent-building and orchestration tooling ships alongside the classic ML platform under that name.

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