Software · head to head
AWS SageMaker vs Comet ML
AWS SageMaker
Software
Build, train, and deploy machine learning models at scale
- From
- Free
- Rated
- -
Comet ML
Software
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
The short version
- Each has a real cost: AWS SageMaker vendor lock-in to AWS ecosystem makes migration to other platforms difficult; Comet ML the free cloud tier caps data at 25,000 spans a month with 60 day retention
- They diverge on capability: AWS SageMaker covers Jupyter notebooks, Comet ML covers Experiment tracking.
Where they differ
Only the attributes on which AWS SageMaker and Comet ML actually diverge.
| Attribute | AWS SageMaker | Comet ML |
|---|---|---|
| Pricing model | Unknown | freemium |
| Platforms | Web | Web, Linux, Mac, Windows |
| Founded | 2006 | 2017 |
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 AWS SageMaker
- Jupyter notebooks
- Built-in algorithms
- Automatic model tuning
- One-click deployment
- Model monitoring
- S3
- Lambda
- Step Functions
Only in Comet ML
- Experiment tracking
- Code versioning
- Model registry
- Hyperparameter optimization
- Production monitoring
- PyTorch
- TensorFlow
- Keras
Both cover
- Web support
What people use each for
The jobs each tool is most often brought in to do.
AWS SageMaker
- Machine learningnot Comet ML
- Data analysisnot Comet ML
- Model trainingnot Comet ML
- Predictive analyticsnot Comet ML
Comet ML
- Tracking machine learning experiments, metrics and model versionsnot AWS SageMaker
- Monitoring and evaluating LLM applications with tracingnot AWS SageMaker
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
AWS SageMaker
- Vendor lock-in to AWS ecosystem makes migration to other platforms difficult
- Opaque pricing can lead to unexpected expenses like forgotten EBS volume charges
- Does not include native job scheduling, requiring Lambda or EventBridge integration
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
AWS SageMaker
FreeNo published plan breakdown. See the AWS SageMaker review.
Comet ML
Free- FreeFree
- 100 experiments
- Basic features
- Community support
- Team$179/month
- Unlimited experiments
- Team collaboration
- Priority support
Which should you pick?
Choose AWS SageMaker if
- You need jupyter notebooks.
- You want to start without paying.
- You also want built-in algorithms.
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 AWS SageMaker or Comet ML better?
- Neither clearly leads. AWS SageMaker 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, AWS SageMaker or Comet ML?
- AWS SageMaker starts at Free and Comet ML at Free.
- Does AWS SageMaker or Comet ML run on more platforms?
- AWS SageMaker runs on Web. Comet ML runs on Web, Linux, Mac, Windows.
- Can I use AWS SageMaker for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is AWS SageMaker best used for?
- AWS SageMaker is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Comet ML is typically brought in for.
- What can AWS SageMaker do that Comet ML cannot?
- AWS SageMaker covers Jupyter notebooks, Built-in algorithms, Automatic model tuning, One-click deployment. Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Both handle Web support.
Answered from the vendors’ own pages
AWS SageMaker: What is AWS SageMaker used for?
AWS SageMaker is a machine learning service for building, training, and deploying ML models at scale. It provides tools for data preparation, model training, inference endpoints, and performance optimization.
SourceAWS SageMaker: How is AWS SageMaker priced?
SageMaker uses pay-as-you-go pricing with no upfront costs or long-term commitments. Pricing starts at $0.04 per hour for basic notebook instances and scales based on instance type. ML Savings Plans offer up to 64% off with hourly spend commitments.
SourceAWS SageMaker: Does AWS SageMaker have a free tier?
Yes, the free tier includes 250 hours of notebook usage, 50 hours of training, and 125 hours of hosting on ml.t3.medium instances during the first two months.
SourceRelated pages
More on AWS SageMaker
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