Software · head to head
Comet ML vs Databricks
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
Databricks
Software
Unified analytics platform for data engineering and data science
- 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; Databricks cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- They diverge on capability: Comet ML covers Experiment tracking, Databricks covers Delta Lake.
Where they differ
Only the attributes on which Comet ML and Databricks actually diverge.
| Attribute | Comet ML | Databricks |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Web, Linux, Mac, Windows | Web, Aws, Azure, Gcp |
| Founded | 2017 | 2013 |
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 Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Only in Databricks
- Delta Lake
- Apache Spark
- MLflow
- Unity Catalog
- Photon Engine
- Collaborative Notebooks
- Auto-scaling
- AWS
Both cover
- Web 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 Databricks
- Monitoring and evaluating LLM applications with tracingnot Databricks
Databricks
- Running Spark data engineering pipelines on managed clustersnot Comet ML
- Building a lakehouse over data in cloud object storagenot Comet ML
- Training and serving machine learning models alongside the datanot 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
Databricks
- Cloud compute is billed separately by the cloud provider on top of Databricks DBU charges
- The free trial lasts 14 days
- Discounts require a Committed Use Contract, with larger commitments needed for larger discounts
- Azure Databricks pricing is set by Microsoft rather than by Databricks
- Security and compliance capabilities are sold as separate platform add ons rather than included in the base rate
Pricing, plan by plan
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Databricks
Free- Community EditionFree
- Limited cluster
- Notebook environment
- Community support
- Standard$0.07/DBU
- Jobs compute
- SQL compute
- Standard support
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 Databricks if
- You need delta lake.
- You want to start without paying.
- You work on Web, Aws, Azure, Gcp.
- You also want apache spark.
Questions people ask
- Is Comet ML or Databricks better?
- Neither clearly leads. Comet ML starts at Free and Databricks at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Comet ML or Databricks?
- Comet ML starts at Free and Databricks at Free.
- Does Comet ML or Databricks run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Databricks runs on Web, Aws, Azure, Gcp.
- 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 Databricks is typically brought in for.
- What can Comet ML do that Databricks cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Databricks covers Delta Lake, Apache Spark, MLflow, Unity Catalog. Both handle Web support.
Related pages
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