Machine Learning & Data Science · head to head
Dask vs Comet ML
Comet ML
Machine Learning & Data Science
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
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; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: Dask covers Parallel computing, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which Dask and Comet ML actually diverge.
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 Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- XGBoost
Only in Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Both cover
- scikit-learn
- Linux support
- Mac support
- Windows 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 Comet ML
- Parallelising custom Python task graphsnot Comet ML
- Processing larger than memory arrays and dataframes on a clusternot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Dask
- Monitoring and evaluating LLM applications with tracingnot 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
Comet ML
- The free cloud tier caps data at 25,000 spans a month with 60 day retention
- Retention stays at 60 days even on the paid Pro plan, and extending it is a $29 per 100k spans add on
- Overage on Pro is $5 per additional 100,000 spans
- The free MLOps tier is a single user with 100 GB of storage and training hours governed by a fair usage policy
- Pro MLOps is $19 per user per month and caps the team at 10 users
Pricing, plan by plan
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
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 Comet ML if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Linux, Mac, Windows.
- You also want code versioning.
Questions people ask
- Is Dask or Comet ML better?
- Neither clearly leads. Dask starts at Free and Comet ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Dask or Comet ML?
- Dask starts at Free and Comet ML at Free.
- Does Dask or Comet ML run on more platforms?
- Dask runs on Linux, Mac, Windows. Comet ML runs on Web, Linux, Mac, Windows.
- 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 Comet ML is typically brought in for.
- What can Dask do that Comet ML cannot?
- Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle scikit-learn, Linux support, Mac support, Windows support.