Machine Learning · head to head
Comet ML vs Amazon Redshift ML
Comet ML
Machine Learning
Platform for tracking, comparing, and optimizing ML experiments
- From
- Free
- Rated
- -
Amazon Redshift ML
Machine Learning
SQL statements in Redshift that train models on SageMaker and return them as functions
- 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; Amazon Redshift ML training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
- They diverge on capability: Comet ML covers Experiment tracking, Amazon Redshift ML covers CREATE MODEL in SQL.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Comet ML and Amazon Redshift ML actually diverge.
| Attribute | Comet ML | Amazon Redshift ML |
|---|---|---|
| Pricing model | freemium | usage-based |
| Platforms | Web, Linux, Mac, Windows | Web |
| Founded | 2017 | 2006 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated), category (Machine Learning).
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 Amazon Redshift ML
- CREATE MODEL in SQL
- Automatic model selection
- Local inference
- Bring your own model
- Algorithm selection
- Cost ceiling controls
- Existing warehouse security
- Batch and interactive scoring
What people use each for
The jobs each tool is most often brought in to do.
Comet ML
- LLM observability and monitoringnot Amazon Redshift ML
- AI agent testing and debuggingnot Amazon Redshift ML
- Experiment tracking for machine learningnot Amazon Redshift ML
- Model registry and version managementnot Amazon Redshift ML
- ML model training monitoringnot Amazon Redshift ML
Amazon Redshift ML
- Adding a churn or propensity score to an existing dashboard where the data is already in Redshift and nobody needs a bespoke modelnot Comet ML
- Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Comet ML
- Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Comet ML
- Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot 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
Amazon Redshift ML
- Training is billed by SageMaker separately from Redshift, so a feature that looks like a free SQL statement produces a second line item on a different part of the bill that the analyst who ran it usually cannot see.
- Autopilot searches many candidate models by default and the duration and cost of CREATE MODEL scale with the data size and the MAX_CELLS setting, so an unconstrained statement against a large table is an expensive accident rather than an experiment.
- Local inference runs on the Redshift cluster itself, so scoring millions of rows competes for the resources the warehouse exists to provide, and the remote inference alternative adds a per-batch network call plus an hourly SageMaker endpoint charge that persists whether or not anyone queries it.
- The supported problem types are limited to what the exposed algorithms cover, so anything involving text, images, sequences, a custom loss function or a bespoke evaluation metric is out of scope and has to be built conventionally.
- There is no retraining schedule, drift detection or model registry, so a model created by a statement stays exactly as trained until somebody remembers to recreate it, and nothing in the warehouse will report that its accuracy has decayed.
Pricing, plan by plan
Comet ML
Free- Free CloudFree
- Up to 10 team members
- 25,000 spans per month
- 60-day data retention
- Pro Cloud$19/month
- Up to 50 team members
- 100,000 spans per month
- 60-day data retention
- MLOps FreeFree
- 1 user with fair usage policy
- Experiment tracking
- Dataset management
- MLOps Pro$19/user/month
- Up to 10 users
- 1,500 training hours included
- 500GB storage included
Amazon Redshift ML
Free- Free TrialFree
- 2-month trial
- 750 DC2.Large hours
- On-Demand$0.25/hour
- Per-node pricing
- SageMaker training
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 Amazon Redshift ML if
- You need create model in sql.
- You want to start without paying.
- You also want automatic model selection.
Questions people ask
- Is Comet ML or Amazon Redshift ML better?
- Neither clearly leads. Comet ML starts at Free and Amazon Redshift ML at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Comet ML or Amazon Redshift ML?
- Comet ML starts at Free and Amazon Redshift ML at Free.
- Does Comet ML or Amazon Redshift ML run on more platforms?
- Comet ML runs on Web, Linux, Mac, Windows. Amazon Redshift ML runs on Web.
- 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 llm observability and monitoring, ai agent testing and debugging, experiment tracking for machine learning, model registry and version management. Of those, llm observability and monitoring and ai agent testing and debugging are not what Amazon Redshift ML is typically brought in for.
- What can Comet ML do that Amazon Redshift ML cannot?
- Comet ML covers Experiment tracking, Code versioning, Model registry, Hyperparameter optimization. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.
Answered from the vendors’ own pages
Comet ML: Does Comet.ml offer a free plan?
Yes, Comet.ml offers free tiers for both Opik (cloud observability) and MLOps platforms. Free Cloud Opik includes up to 10 team members and 25,000 spans/month. Free MLOps tier is limited to 1 user.
SourceAmazon Redshift ML: Does it require SageMaker?
Yes. Redshift ML is an interface; the training happens in SageMaker and needs an IAM role and an S3 bucket for the intermediate data.
Comet ML: How many team members can use the free Comet.ml tier?
Free Cloud supports up to 10 team members. The Pro Cloud plan supports up to 50 team members at $19/month.
SourceAmazon Redshift ML: Is there an extra charge?
The SQL interface is part of Redshift, but the training runs as a SageMaker job charged at SageMaker rates, and a remote inference endpoint is billed for as long as it exists.
Comet ML: What is a span in Comet.ml pricing?
A span represents a single tracked operation such as model requests or function calls. Free Cloud tier includes 25,000 spans per month.
SourceAmazon Redshift ML: What kinds of model can it build?
Regression, binary and multiclass classification through the automatic path, plus direct use of XGBoost, linear learner, multilayer perceptron and K-means. Anything beyond structured tabular prediction is out of scope.
Comet ML: Does Comet.ml offer academic pricing?
Yes, a free Pro plan is available for academic users; verification is required via signup.
SourceAmazon Redshift ML: Can I use a model I trained myself?
Yes, through the bring-your-own-model path, either compiled into the cluster for local inference or called as a remote SageMaker endpoint.
Amazon Redshift ML: Does it retrain automatically?
No. Retraining means running CREATE MODEL again, on a schedule you build yourself, and nothing in the product monitors whether it is needed.
Related pages
More on Amazon Redshift ML
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