Softwr

Machine Learning & Data Science · head to head

AWS SageMaker vs DataRobot

AWS SageMaker logo

AWS SageMaker

Machine Learning & Data Science

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
DataRobot logo

DataRobot

Machine Learning & Data Science

Enterprise AI platform for automated machine learning

From
On request
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: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; DataRobot model transparency is limited, often resembling a black box with limited explainability
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, DataRobot covers Automated ML.

Where they differ

Only the attributes on which AWS SageMaker and DataRobot actually diverge.

Attributes where AWS SageMaker and DataRobot differ
AttributeAWS SageMakerDataRobot
Starting priceFreeOn request
Pricing modelUnknownsubscription
Free tierYesNo
Founded20062012

Identical on both: platforms (Web), user rating (Not yet rated), category (Machine Learning & Data Science).

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 AWS SageMaker

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

Only in DataRobot

  • Automated ML
  • Model deployment
  • Time series
  • MLOps
  • Snowflake
  • Databricks
  • AWS
  • Azure

Both cover

  • Model monitoring
  • Web support

What people use each for

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

AWS SageMaker

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

DataRobot

  • 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.

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

DataRobot

  • Model transparency is limited, often resembling a black box with limited explainability
  • Requires integration with separate data manipulation tools for complex data transformation
  • Lacks native Python and R code customization for proprietary algorithms
  • Dependence on cloud connectivity means offline capabilities are not available
  • Uploading sensitive data to third-party servers raises data privacy and security concerns

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

DataRobot

On request
  • TrialFree
    • Limited access
    • Basic features
  • EnterpriseFree
    • Full platform
    • AutoML
    • MLOps

Which should you pick?

Choose AWS SageMaker if

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

Choose DataRobot if

  • You need automated ml.
  • You also want model deployment.
DataRobot review

Questions people ask

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

Answered from the vendors’ own pages

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
DataRobot: Does DataRobot require data science expertise?

DataRobot automates much of the ML pipeline including data preparation, feature engineering, and model selection, making it more accessible to non-experts, though it is still an enterprise platform.

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
DataRobot: What does DataRobot cost?

DataRobot uses custom enterprise pricing with typical starting costs around $2,500 per month for smaller organizations. For 10 users, monthly costs range from $15,000 to $20,000. Implementation and professional services are 20-40% of first-year contract value.

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
DataRobot: Does DataRobot support generative AI?

Yes, DataRobot offers generative AI capabilities with API-first integrations for LLMs, vector databases, and embedding models.

Source
DataRobot: Can DataRobot handle unstructured data?

Yes, DataRobot supports machine learning on both structured and unstructured data, including deep learning, NLP, and image analysis.

Source
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