Software · head to head
BentoML vs Dask
The short version
- Each has a real cost: BentoML core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.; 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: BentoML covers Model packaging, Dask covers Parallel computing.
Where they differ
Only the attributes on which BentoML and Dask actually diverge.
Identical on both: starting price (Free), free tier (Yes), platforms (Linux, Mac, Windows), 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 BentoML
- Model packaging
- REST API generation
- Adaptive batching
- Multi-framework support
- Container deployment
- PyTorch
- TensorFlow
- Docker
Only in Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- Kubernetes
Both cover
- scikit-learn
- XGBoost
- Linux support
- Mac support
- Windows support
What people use each for
The jobs each tool is most often brought in to do.
BentoML
- Machine learningnot Dask
- Data analysisnot Dask
- Model trainingnot Dask
- Predictive analyticsnot Dask
Dask
- Scaling pandas and NumPy workloads beyond a single machine's memorynot BentoML
- Parallelising custom Python task graphsnot BentoML
- Processing larger than memory arrays and dataframes on a clusternot BentoML
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
BentoML
- Core BentoML framework is Apache 2.0 and free, but the managed BentoCloud enterprise tier has no published pricing: the README instructs buyers to sign up for personal access or contact sales for enterprise use, with no rate card shown.
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
BentoML
Free- Open SourceFree
- Model packaging
- API creation
- Local serving
- BentoCloudFree
- Managed deployment
- Auto-scaling
- Monitoring
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Which should you pick?
Choose BentoML if
- You need model packaging.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want rest api generation.
Choose Dask if
- You need parallel computing.
- You want to start without paying.
- You work on Linux, Mac, Windows.
- You also want distributed dataframes.
Questions people ask
- Is BentoML or Dask better?
- Neither clearly leads. BentoML 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, BentoML or Dask?
- BentoML starts at Free and Dask at Free.
- Does BentoML or Dask run on more platforms?
- Both run on Linux, Mac, Windows, so platform support will not decide this one for you.
- Can I use BentoML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is BentoML best used for?
- BentoML 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 BentoML do that Dask cannot?
- BentoML covers Model packaging, REST API generation, Adaptive batching, Multi-framework support. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Both handle scikit-learn, XGBoost, Linux support, Mac support.