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Software · head to head

AWS SageMaker vs Dask

AWS SageMaker logo

AWS SageMaker

Software

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
Dask logo

Dask

Software

Scalable analytics in Python

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; Dask each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, Dask covers Parallel computing.

Where they differ

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

Attributes where AWS SageMaker and Dask differ
AttributeAWS SageMakerDask
Pricing modelUnknownopen-source
PlatformsWebLinux, Mac, Windows
Founded20062015

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 AWS SageMaker

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

Only in Dask

  • Parallel computing
  • Distributed DataFrames
  • Lazy evaluation
  • Dynamic task scheduling
  • Dashboard
  • NumPy
  • Pandas
  • scikit-learn

What people use each for

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

AWS SageMaker

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

Dask

  • Scaling pandas and NumPy workloads beyond a single machine's memorynot AWS SageMaker
  • Parallelising custom Python task graphsnot AWS SageMaker
  • Processing larger than memory arrays and dataframes on a clusternot 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

Dask

  • Each Dask task carries between 200 microseconds and 1 millisecond of scheduler overhead, so graphs of millions of tasks add 10 minutes to hours of pure overhead
  • Partition sizing is left to the user: chunks must fit several times over in worker memory, and both oversized and undersized chunks are documented failure modes
  • Embedding large locally created DataFrames or Arrays into a Dask computation is documented as a practice to avoid because of network overhead
  • Calling compute repeatedly in a loop rather than batching prevents parallelisation of queries
  • The documentation itself advises trying better algorithms, file formats or sampling before adopting Dask

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

Dask

Free
  • Open SourceFree
    • Parallel computing
    • Distributed DataFrames
    • ML integration

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 Dask if

  • You need parallel computing.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want distributed dataframes.
Dask review

Questions people ask

Is AWS SageMaker or Dask better?
Neither clearly leads. AWS SageMaker starts at Free and Dask at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or Dask?
AWS SageMaker starts at Free and Dask at Free.
Does AWS SageMaker or Dask run on more platforms?
AWS SageMaker runs on Web. Dask runs on Linux, Mac, Windows.
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 Dask is typically brought in for.
What can AWS SageMaker do that Dask cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling.

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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