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Software · head to head

Apache Superset vs AWS SageMaker

Apache Superset logo

Apache Superset

Software

Modern data exploration and visualization platform

From
Free
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

  • Each has a real cost: Apache Superset distributed under Apache License 2.0 with no official vendor-hosted SaaS; deploying it requires self-managed infrastructure since the Apache Software Foundation does not sell a managed offering.; AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult
  • They diverge on capability: Apache Superset covers 40+ Visualizations, AWS SageMaker covers Jupyter notebooks.

Where they differ

Only the attributes on which Apache Superset and AWS SageMaker actually diverge.

Attributes where Apache Superset and AWS SageMaker differ
AttributeApache SupersetAWS SageMaker
Pricing modelopen-sourceUnknown
PlatformsWeb, Self-hosted, DockerWeb
Founded19992006

Identical on both: starting price (Free), free tier (Yes), 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 Apache Superset

  • 40+ Visualizations
  • SQL IDE
  • Semantic Layer
  • Caching
  • Security
  • PostgreSQL
  • MySQL
  • Presto

Only in AWS SageMaker

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

Both cover

  • Web support

What people use each for

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

Apache Superset

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

AWS SageMaker

  • Machine learningnot Apache Superset
  • Data analysisnot Apache Superset
  • Model trainingnot Apache Superset
  • Predictive analyticsnot Apache Superset

Where each one falls short

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

Apache Superset

  • Distributed under Apache License 2.0 with no official vendor-hosted SaaS; deploying it requires self-managed infrastructure since the Apache Software Foundation does not sell a managed offering.

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

Apache Superset

Free
  • Open SourceFree
    • Full Features
    • Self-hosted
    • Community Support

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Which should you pick?

Choose Apache Superset if

  • You need 40+ visualizations.
  • You want to start without paying.
  • You work on Web, Self-hosted, Docker.
  • You also want sql ide.
Apache Superset 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 Apache Superset or AWS SageMaker better?
Neither clearly leads. Apache Superset starts at Free and AWS SageMaker at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Apache Superset or AWS SageMaker?
Apache Superset starts at Free and AWS SageMaker at Free.
Does Apache Superset or AWS SageMaker run on more platforms?
Apache Superset runs on Web, Self-hosted, Docker. AWS SageMaker runs on Web.
Can I use Apache Superset for free?
Both have a free tier, so you can try either at no cost before committing.
What is Apache Superset best used for?
Apache Superset is most often used for self-service analytics, data exploration, ad-hoc reporting, collaborative analysis. Of those, self-service analytics and data exploration are not what AWS SageMaker is typically brought in for.
What can Apache Superset do that AWS SageMaker cannot?
Apache Superset covers 40+ Visualizations, SQL IDE, Semantic Layer, Caching. AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. 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
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
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
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