Vertex AI and SageMaker for MLOps: A Comparison
Both clouds rebuilt and renamed their ML platforms: SageMaker is now SageMaker AI inside a wider SageMaker, and Vertex AI's documentation now lives under Gemini Enterprise Agent Platform. This comparison maps the MLOps building blocks on each, notes what changed recently, and gives a way to choose.
GloryTecks Team
Training & Placement Faculty
Last updated: 8 October 2026
Choosing a managed ML platform used to mean comparing two familiar names, Amazon SageMaker and Google's Vertex AI. Both have since been reorganised. A comparison needs current names before it can compare anything else.
On AWS, Amazon SageMaker was renamed Amazon SageMaker AI on 3 December 2024, with its APIs unchanged, and the name Amazon SageMaker now covers a wider platform for data, analytics and AI that includes SageMaker AI (AWS: What is SageMaker AI). On Google Cloud, the machine learning documentation that used to sit under Vertex AI now redirects to Gemini Enterprise Agent Platform, which this article calls Agent Platform.
The building blocks, side by side
| MLOps need | Amazon SageMaker AI | Google Agent Platform |
|---|---|---|
| Pipelines | SageMaker Pipelines: AWS's own workflow service | Pipelines running Kubeflow Pipelines or TFX definitions |
| Experiment tracking | Managed MLflow | Agent Platform Experiments |
| Model registry | Model Registry, with versions, lineage and approval stages | Model Registry, a central repository for model versions |
| Online serving | Real-time and serverless endpoints, asynchronous inference | Online endpoints with traffic splitting |
| Batch inference | Batch transform | Batch inference jobs |
| Model monitoring | Model Monitor, closed to new customers | Model Monitoring jobs with alerts |
| Feature store | SageMaker Feature Store, online and offline | Feature Store serving from BigQuery |
Pipelines
SageMaker Pipelines is AWS's purpose-built orchestration service for ML, running on auto-scaling serverless infrastructure and driven by a visual editor, the SDK, APIs or JSON (AWS: Pipelines). Agent Platform Pipelines also runs serverless, but it executes pipelines written with the open Kubeflow Pipelines or TensorFlow Extended frameworks (Google Cloud: Pipelines).
That difference matters for portability. A Kubeflow pipeline written for Google can also run on any Kubeflow installation, while a SageMaker pipeline is tied to AWS. See Kubeflow Pipelines.
Tracking and registry
AWS has moved experiment tracking onto fully managed MLflow, the same open-source tool many teams already use locally (AWS: managed MLflow). Google keeps its own Agent Platform Experiments service for tracking and comparing runs (Google Cloud: Experiments).
Both have a model registry. SageMaker's catalogues models for production, manages versions, attaches metadata such as training metrics, shows lineage and tracks approval stages (AWS: Model Registry). Google's is a central repository for managing the lifecycle of models and their versions (Google Cloud: Model Registry). For the concepts behind both, see the model registry.
Serving
SageMaker AI offers real-time endpoints for low latency, serverless endpoints that scale without managing instances, and asynchronous inference (AWS: deploy models). Agent Platform serves models on endpoints, where a new model can start with a small share of traffic that grows to 100% (Google Cloud: deploy to an endpoint). For offline scoring, SageMaker AI has batch transform (AWS: batch transform) and Google runs batch inference jobs (Google Cloud: batch inference). Each platform expects its own container contract, which deploying ML models on AWS, Azure and GCP runs side by side.
Monitoring: the biggest recent change
SageMaker Model Monitor is no longer open to new customers and will get no new features. AWS points new users to open-source monitoring solutions built on SageMaker AI MLflow Apps and Evidently AI, with QuickSight dashboards and CloudWatch (AWS: Model Monitor availability). Google's Model Monitoring still runs monitoring jobs on demand or on a schedule and raises alerts when metrics pass thresholds, such as drift (Google Cloud: Model Monitoring).
So on AWS, plan monitoring as something you assemble; on Google, check the managed service fits before building your own. For what to monitor either way, see monitoring ML models in production.
Feature stores
SageMaker Feature Store keeps an online store for real-time inference and an offline store for training and batch work (AWS: Feature Store). Google's Feature Store takes a different approach: features live in BigQuery tables or views and are served online directly from that source (Google Cloud: Feature Store). If your data already lives in BigQuery, that removes a copy; if it lives in S3, AWS's design fits better.
How to choose
- Start where your data and identity already are. Moving data between clouds costs money, time and security reviews.
- Decide how open you want to be. Google's pipelines use Kubeflow or TFX definitions, and AWS's tracking uses MLflow; open formats make it easier to leave.
- Check monitoring early, since AWS now expects you to assemble it from other services.
- Compare total cost on a real workload, not on list prices alone; see cost optimisation for ML workloads.
- Re-check names and features before relying on them. Both platforms changed substantially in the last two years, and any managed ML platform can retire a service, as AWS did with Model Monitor.
Common mistakes
- Choosing a platform before knowing where the data will live.
- Following tutorials written for the old names and services.
- Assuming every managed feature is still offered, as Model Monitor shows.
- Locking every pipeline into one cloud's proprietary format without deciding to.
- Ignoring the open-source tools both platforms now lean on.
Is Vertex AI the same as Gemini Enterprise Agent Platform?
What happened to Amazon SageMaker?
Which is better for MLOps, SageMaker AI or Google's platform?
Can I use MLflow on these platforms?
MLOps on AWS, Microsoft Azure and Google Cloud is covered in the Cloud MLOps Platforms module of the MLOps course.
Sources
- What is Amazon SageMaker AI? (AWS documentation) — Amazon Web Services
- Pipelines (Amazon SageMaker AI documentation) — Amazon Web Services
- Managed MLflow on Amazon SageMaker AI (AWS documentation) — Amazon Web Services
- Model Registration Deployment with Model Registry (AWS documentation) — Amazon Web Services
- Deploy models for inference (AWS documentation) — Amazon Web Services
- Batch transform for inference with Amazon SageMaker AI (AWS documentation) — Amazon Web Services
- Create, store, and share features (Amazon SageMaker AI documentation) — Amazon Web Services
- Amazon SageMaker Model Monitor availability change (AWS documentation) — Amazon Web Services
- Introduction to Gemini Enterprise Agent Platform Pipelines (Google Cloud documentation) — Google Cloud
- Introduction to Gemini Enterprise Agent Platform Experiments (Google Cloud documentation) — Google Cloud
- Introduction to Model Registry (Google Cloud documentation) — Google Cloud
- Deploy a model to an endpoint (Google Cloud documentation) — Google Cloud
- Get batch inferences from a custom trained model (Google Cloud documentation) — Google Cloud
- Introduction to Model Monitoring (Google Cloud documentation) — Google Cloud
- About Feature Store on Gemini Enterprise Agent Platform (Google Cloud documentation) — Google Cloud
GloryTecks Team
Training & Placement Faculty
The GloryTecks faculty team mentors learners across Data Science, AI and Data Engineering, with a focus on real-time projects and placement readiness in Hyderabad.
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