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Machine Learning & Data Science · head to head

DVC vs Amazon Redshift ML

DVC logo

DVC

Machine Learning & Data Science

Data version control for machine learning projects

From
Free
Rated
-
Full reviewWebsite
Amazon Redshift ML logo

Amazon Redshift ML

Machine Learning & Data Science

Create machine learning models using SQL

From
Free
Rated
-
Full reviewWebsite

The short version

  • Each has a real cost: DVC dVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.; Amazon Redshift ML free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million
  • They diverge on capability: DVC covers Data versioning, Amazon Redshift ML covers SQL-based ML.

Where they differ

Only the attributes on which DVC and Amazon Redshift ML actually diverge.

Attributes where DVC and Amazon Redshift ML differ
AttributeDVCAmazon Redshift ML
Pricing modelopen-sourceusage-based
PlatformsLinux, Mac, WindowsWeb
Founded20182006

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

  • Data versioning
  • Pipeline management
  • Experiment tracking
  • Remote storage
  • Git integration
  • Git
  • Azure Blob
  • Google Cloud Storage

Only in Amazon Redshift ML

  • SQL-based ML
  • AutoML
  • SageMaker integration
  • BYOM support
  • In-database predictions
  • Amazon Redshift
  • SageMaker
  • Glue

Both cover

  • S3

What people use each for

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

DVC

  • Machine learningnot Amazon Redshift ML
  • Data analysisnot Amazon Redshift ML
  • Model trainingnot Amazon Redshift ML
  • Predictive analyticsnot Amazon Redshift ML

Amazon Redshift ML

  • Training and running machine learning models directly from SQL inside Amazon Redshiftnot DVC

Where each one falls short

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

DVC

  • DVC is Apache 2.0 licensed open source with no enterprise tier or paid support offering documented in the project itself; teams needing SLA-backed support get nothing from the DVC project directly.

Amazon Redshift ML

  • Free tier covers only two CREATE MODEL requests per month for two months, capped at 100,000 cells per request; beyond that training is metered at $20 per million cells for the first 10 million, dropping in tiers to $7 per million cells over 100 million

Pricing, plan by plan

DVC

Free
  • Open SourceFree
    • Data versioning
    • Pipeline management
    • Experiment tracking
  • DVC StudioFree
    • Web UI
    • Team collaboration
    • Visualizations

Amazon Redshift ML

Free
  • Free TrialFree
    • 2-month trial
    • 750 DC2.Large hours
  • On-Demand$0.25/hour
    • Per-node pricing
    • SageMaker training

Which should you pick?

Choose DVC if

  • You need data versioning.
  • You want to start without paying.
  • You work on Linux, Mac, Windows.
  • You also want pipeline management.
DVC review

Choose Amazon Redshift ML if

  • You need sql-based ml.
  • You want to start without paying.
  • You also want automl.
Amazon Redshift ML review

Questions people ask

Is DVC or Amazon Redshift ML better?
Neither clearly leads. DVC starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, DVC or Amazon Redshift ML?
DVC starts at Free and Amazon Redshift ML at Free.
Does DVC or Amazon Redshift ML run on more platforms?
DVC runs on Linux, Mac, Windows. Amazon Redshift ML runs on Web.
Can I use DVC for free?
Both have a free tier, so you can try either at no cost before committing.
What is DVC best used for?
DVC is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Amazon Redshift ML is typically brought in for.
What can DVC do that Amazon Redshift ML cannot?
DVC covers Data versioning, Pipeline management, Experiment tracking, Remote storage. Amazon Redshift ML covers SQL-based ML, AutoML, SageMaker integration, BYOM support. Both handle S3.
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