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Software · head to head

Seldon vs Dask

Seldon logo

Seldon

Software

Deploy, scale, and monitor machine learning models

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: Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift; 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: Seldon covers Model serving, Dask covers Parallel computing.

Where they differ

Only the attributes on which Seldon and Dask actually diverge.

Attributes where Seldon and Dask differ
AttributeSeldonDask
Pricing modelfreemiumopen-source
PlatformsLinuxLinux, Mac, Windows
Founded20142015

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 Seldon

  • Model serving
  • A/B testing
  • Canary deployments
  • Outlier detection
  • Model explainability
  • Istio
  • Prometheus
  • Grafana

Only in Dask

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

Both cover

  • Kubernetes
  • Linux support

What people use each for

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

Seldon

  • Serving and routing machine learning models on Kubernetesnot Dask
  • Building multi-step inference pipelines with A/B tests and explainersnot Dask

Dask

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

Where each one falls short

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

Seldon

  • Production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
  • The documented components carry both minimum and maximum supported versions, so newer Kubernetes and dependency versions are not automatically supported
  • Dataflow Pipelines need an additional component that the docs recommend avoiding installing when pipelines are not used
  • The Docker Compose install is offered as a lightweight alternative for environments without Kubernetes rather than as a production path

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

Seldon

Free
  • Seldon CoreFree
    • Open source
    • Kubernetes deployment
    • Model serving
  • Seldon DeployFree
    • Enterprise features
    • GUI
    • Monitoring

Dask

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

Which should you pick?

Choose Seldon if

  • You need model serving.
  • You want to start without paying.
  • You work on Linux.
  • You also want a/b testing.
Seldon 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 Seldon or Dask better?
Neither clearly leads. Seldon 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, Seldon or Dask?
Seldon starts at Free and Dask at Free.
Does Seldon or Dask run on more platforms?
Seldon runs on Linux. Dask runs on Linux, Mac, Windows.
Can I use Seldon for free?
Both have a free tier, so you can try either at no cost before committing.
What is Seldon best used for?
Seldon is most often used for serving and routing machine learning models on kubernetes, building multi-step inference pipelines with a/b tests and explainers. Of those, serving and routing machine learning models on kubernetes and building multi-step inference pipelines with a/b tests and explainers are not what Dask is typically brought in for.
What can Seldon do that Dask cannot?
Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Both handle Kubernetes, Linux support.
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