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Software · head to head

Google Vertex AI vs AWS SageMaker

Google Vertex AI logo

Google Vertex AI

Software

Unified ML platform to build, deploy, and scale AI models

From
On request
Rated
-
Full reviewWebsite
AWS SageMaker logo

AWS SageMaker

Software

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only AWS SageMaker has a free tier, so it costs nothing to try first.
  • Each has a real cost: Google Vertex AI vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • They diverge on capability: Google Vertex AI covers AutoML, AWS SageMaker covers Jupyter notebooks.

Where they differ

Only the attributes on which Google Vertex AI and AWS SageMaker actually diverge.

Attributes where Google Vertex AI and AWS SageMaker differ
AttributeGoogle Vertex AIAWS SageMaker
Starting priceOn requestFree
Free tierNoYes
PlatformsCloud, WebWeb
Founded20082006

Identical on both: pricing model (Unknown), user rating (Not yet rated), category (Unknown).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in Google Vertex AI

  • AutoML
  • Custom training
  • Feature Store
  • Prediction serving
  • BigQuery
  • Cloud Storage
  • TensorFlow
  • PyTorch

Only in AWS SageMaker

  • Jupyter notebooks
  • Built-in algorithms
  • Automatic model tuning
  • One-click deployment
  • S3
  • Lambda
  • Step Functions
  • CloudWatch

Both cover

  • Model monitoring
  • Web support

What people use each for

The jobs each tool is most often brought in to do.

Google Vertex AI

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

AWS SageMaker

  • Machine learning
  • Data analysis
  • Model training
  • Predictive analytics

Both are used for machine learning, data analysis, model training, predictive analytics, on those jobs the choice comes down to price and fit rather than capability.

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

Google Vertex AI

  • Vendor lock-in to Google Cloud ecosystem makes migration to other platforms difficult
  • Requires familiarity with Google Cloud Platform infrastructure and concepts
  • Cost can escalate quickly with large training and inference workloads

AWS SageMaker

  • Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
  • Does not include native job scheduling, requiring Lambda or EventBridge integration

Pricing, plan by plan

Google Vertex AI

On request

No published plan breakdown. See the Google Vertex AI review.

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Which should you pick?

Choose Google Vertex AI if

  • You need automl.
  • You work on Cloud, Web.
  • You also want custom training.
Google Vertex AI review

Choose AWS SageMaker if

  • You need jupyter notebooks.
  • You want to start without paying.
  • You also want built-in algorithms.
AWS SageMaker review

Questions people ask

Is Google Vertex AI or AWS SageMaker better?
Neither clearly leads. Google Vertex AI starts at On request and AWS SageMaker at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Google Vertex AI or AWS SageMaker?
AWS SageMaker has a free tier; the other does not. Paid plans start at On request for Google Vertex AI and Free for AWS SageMaker.
Does Google Vertex AI or AWS SageMaker run on more platforms?
Google Vertex AI runs on Cloud, Web. AWS SageMaker runs on Web.
Can I use AWS SageMaker for free?
Yes. AWS SageMaker has a free tier, so you can try it without paying. Google Vertex AI starts at On request.
What is Google Vertex AI best used for?
Google Vertex AI is most often used for machine learning, data analysis, model training, predictive analytics.
What can Google Vertex AI do that AWS SageMaker cannot?
Google Vertex AI covers AutoML, Custom training, Feature Store, Prediction serving. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Both handle Model monitoring, Web support.

Answered from the vendors’ own pages

Google Vertex AI: What is the pricing model for Google Vertex AI?

Vertex AI uses a pay-as-you-go model with no upfront costs or lock-in fees. Costs vary by service: training is billed by compute resources and time (30-second increments), online predictions by machine type per hour, and batch predictions by compute time or per-record for specific AutoML types.

Source
AWS SageMaker: What is AWS SageMaker used for?

AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.

Source
Google Vertex AI: What types of data can Vertex AI handle?

Vertex AI supports image, video, text, and tabular data types with tools for uploading, storing, and managing large datasets.

Source
AWS SageMaker: How is AWS SageMaker priced?

SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.

Source
Google Vertex AI: Does Vertex AI support custom model training?

Yes. Vertex AI supports both AutoML for automated machine learning and custom training code in Python, R, and other languages.

Source
AWS SageMaker: Does AWS SageMaker have a free tier?

Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.

Source
Google Vertex AI: What deployment options are available in Vertex AI?

Vertex AI supports online predictions for real-time use cases and batch predictions for large-scale processing.

Source
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