Software · head to head
Dask vs Seldon
The short version
- Each has a real cost: 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; Seldon production deployment requires a Kubernetes cluster, whether managed such as GKE, EKS or AKS, or on-premises such as OpenShift
- They diverge on capability: Dask covers Parallel computing, Seldon covers Model serving.
Where they differ
Only the attributes on which Dask and Seldon actually diverge.
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 Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- scikit-learn
Only in Seldon
- Model serving
- A/B testing
- Canary deployments
- Outlier detection
- Model explainability
- Istio
- Prometheus
- Grafana
Both cover
- Kubernetes
- Linux support
What people use each for
The jobs each tool is most often brought in to do.
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
Seldon
- Serving and routing machine learning models on Kubernetesnot Dask
- Building multi-step inference pipelines with A/B tests and explainersnot Dask
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Pricing, plan by plan
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Seldon
Free- Seldon CoreFree
- Open source
- Kubernetes deployment
- Model serving
- Seldon DeployFree
- Enterprise features
- GUI
- Monitoring
Which should you pick?
Choose Dask if
- You need parallel computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want distributed dataframes.
Choose Seldon if
- You need model serving.
- You want to start without paying.
- You work on Linux.
- You also want a/b testing.
Questions people ask
- Is Dask or Seldon better?
- Neither clearly leads. Dask starts at Free and Seldon at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dask or Seldon?
- Dask starts at Free and Seldon at Free.
- Does Dask or Seldon run on more platforms?
- Dask runs on Linux, Mac, Windows. Seldon runs on Linux.
- Can I use Dask for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Dask best used for?
- Dask is most often used for scaling pandas and numpy workloads beyond a single machine's memory, parallelising custom python task graphs, processing larger than memory arrays and dataframes on a cluster. Of those, scaling pandas and numpy workloads beyond a single machine's memory and parallelising custom python task graphs are not what Seldon is typically brought in for.
- What can Dask do that Seldon cannot?
- Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Seldon covers Model serving, A/B testing, Canary deployments, Outlier detection. Both handle Kubernetes, Linux support.
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