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Machine Learning · head to head

AWS SageMaker vs DuckDB

AWS SageMaker logo

AWS SageMaker

Machine Learning

Build, train, and deploy machine learning models at scale

From
Free
Rated
-
Full reviewWebsite
DuckDB logo

DuckDB

Databases

Fast in-process analytical database

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; DuckDB client-server setup remains in beta and not recommended for production distributed scenarios
  • They diverge on capability: AWS SageMaker covers Jupyter notebooks, DuckDB covers In-process Execution.

Where they differ

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

Attributes where AWS SageMaker and DuckDB differ
AttributeAWS SageMakerDuckDB
Pricing modelUnknownopen-source
PlatformsWebLinux, macOS, Windows, WebAssembly
CategoryMachine LearningDatabases
Founded20062019

Identical on both: starting price (Free), 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 DuckDB

  • In-process Execution
  • Columnar Storage
  • Vectorized Execution
  • Rich SQL Support
  • Parquet Support
  • CSV/JSON Import
  • Zero Dependencies
  • Python

What people use each for

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

AWS SageMaker

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

DuckDB

  • Analytics and data warehousingnot AWS SageMaker
  • OLAP queries and data explorationnot AWS SageMaker
  • Data science and machine learning workflowsnot AWS SageMaker
  • Multi-format data ingestion and processingnot 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

DuckDB

  • Client-server setup remains in beta and not recommended for production distributed scenarios

Pricing, plan by plan

AWS SageMaker

Free

No published plan breakdown. See the AWS SageMaker review.

DuckDB

Free

No published plan breakdown. See the DuckDB 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 DuckDB if

  • You need in-process execution.
  • You want to start without paying.
  • You work on Linux, macOS, Windows, WebAssembly.
  • You also want columnar storage.
DuckDB review

Questions people ask

Is AWS SageMaker or DuckDB better?
Neither clearly leads. AWS SageMaker starts at Free and DuckDB at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, AWS SageMaker or DuckDB?
AWS SageMaker starts at Free and DuckDB at Free.
Does AWS SageMaker or DuckDB run on more platforms?
AWS SageMaker runs on Web. DuckDB runs on Linux, macOS, Windows, WebAssembly.
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 DuckDB is typically brought in for.
What can AWS SageMaker do that DuckDB cannot?
AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. DuckDB covers In-process Execution, Columnar Storage, Vectorized Execution, Rich SQL 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
DuckDB: Is DuckDB free to use?

Yes, DuckDB is completely free. There are no subscription tiers, user limits, or paid plans. The software has zero licensing costs.

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
DuckDB: What license is DuckDB distributed under?

DuckDB is open source under the MIT License, governed by the independent DuckDB Foundation. The MIT License permits commercial use, modification, and distribution with minimal restrictions.

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
DuckDB: Can I use DuckDB in commercial applications?

Yes, the MIT License allows commercial use without restrictions or requirements to publish proprietary code. You can deploy DuckDB anywhere from edge devices to high-core servers.

Source
DuckDB: Are there any limitations on how many instances I can run?

No, there are no user limits, usage limits, or instance restrictions. You have unlimited access to all DuckDB features.

Source
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