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Machine Learning · head to head

AWS SageMaker vs Preset

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
Preset logo

Preset

Business Intelligence

Managed Apache Superset

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Preset limited SQL IDE advanced features compared to specialized query tools
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, Preset covers Managed Superset.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

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

Attributes where AWS SageMaker and Preset differ
AttributeAWS SageMakerPreset
PlatformsWebWeb, Cloud
CategoryMachine LearningBusiness Intelligence
Founded20062019

Identical on both: starting price (Free), pricing model (Unknown), free tier (Yes), user rating (Not yet rated).

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
  • Model monitoring
  • S3
  • Lambda
  • Step Functions

Only in Preset

  • Managed Superset
  • Auto-scaling
  • Enterprise Security
  • Custom Branding
  • API Access
  • Snowflake
  • BigQuery
  • Redshift

Both cover

  • Web support

What people use each for

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

AWS SageMaker

  • Machine learningnot Preset
  • Data analysisnot Preset
  • Model trainingnot Preset
  • Predictive analyticsnot Preset

Preset

  • Self-service analyticsnot AWS SageMaker
  • Data explorationnot AWS SageMaker
  • Ad-hoc reportingnot AWS SageMaker
  • Collaborative analysisnot AWS SageMaker
  • Embedded analyticsnot AWS SageMaker

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

Preset

  • Limited SQL IDE advanced features compared to specialized query tools
  • Viewer licenses add substantial cost for embedded analytics deployments
  • Dataset-centric approach requires preprocessing by data teams for some use cases

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Preset

Free

No published plan breakdown. See the Preset review.

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 Preset if

  • You need managed superset.
  • You want to start without paying.
  • You work on Web, Cloud.
  • You also want auto-scaling.
Preset review

Questions people ask

Is AWS SageMaker or Preset better?
Neither clearly leads. AWS SageMaker starts at Free and Preset at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or Preset?
AWS SageMaker starts at Free and Preset at Free.
Does AWS SageMaker or Preset run on more platforms?
AWS SageMaker runs on Web. Preset runs on Web, Cloud.
Can I use AWS SageMaker for free?
Both have a free tier, so you can try either at no cost before committing.
What is AWS SageMaker best used for?
AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Preset is typically brought in for.
What can AWS SageMaker do that Preset cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Preset covers Managed Superset, Auto-scaling, Enterprise Security, Custom Branding. Both handle 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
Preset: Is Preset free?

Preset offers a free tier for small teams called Starter with 5 users and no credit card required. Paid plans start at $25 per user per month.

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
Preset: Can I export my data from Preset?

Yes. Preset uses Apache Superset and the founders contribute over 75% of commits to the open-source project, enabling migration to Superset without vendor lock-in.

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
Preset: Does Preset include embedded analytics?

Yes. Embedded dashboards are available on Professional and Enterprise plans, with viewer licenses starting at $500 per month for 50 licenses.

Source
Preset: What is the enterprise pricing for Preset?

Enterprise plans are custom quoted. The median buyer pays $35,495 per year.

Source
Preset: Does Preset support AI-powered analytics?

Yes. As of 2026, Preset includes an AI Chatbot and MCP (Model Context Protocol) integration for building charts and dashboards via natural language.

Source
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