Machine Learning & Data Science · head to head
Comet ML vs Dask
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: Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention; 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: Comet ML covers Experiment tracking, Dask covers Parallel computing.
Where they differ
Only the attributes on which Comet ML and Dask 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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Only in Dask
- Parallel computing
- Distributed DataFrames
- Lazy evaluation
- Dynamic task scheduling
- Dashboard
- NumPy
- Pandas
- XGBoost
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.
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot Dask
- Monitoring and evaluating LLM applications with tracingnot Dask
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
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
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
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
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Dask
Free- Open SourceFree
- Parallel computing
- Distributed DataFrames
- ML integration
Which should you pick?
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.
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 Comet ML or Dask better?
- Neither clearly leads. Comet ML 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, Comet ML or Dask?
- Comet ML starts at Free and Dask at Free.
- Does Comet ML or Dask run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Dask runs on Linux, Mac, Windows.
- Can I use Comet ML for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Comet ML best used for?
- Comet ML is most often used for tracking machine learning experiments, metrics and model versions, monitoring and evaluating llm applications with tracing. Of those, tracking machine learning experiments, metrics and model versions and monitoring and evaluating llm applications with tracing are not what Dask is typically brought in for.
- What can Comet ML do that Dask cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Dask covers Parallel computing, Distributed DataFrames, Lazy evaluation, Dynamic task scheduling. Both handle scikit-learn, Linux support, Mac support, Windows support.