Machine Learning · head to head
Kubeflow vs Amazon Redshift ML
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: Kubeflow complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations; 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: Kubeflow covers ML pipelines, 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 Kubeflow and Amazon Redshift ML actually diverge.
| Attribute | Kubeflow | Amazon Redshift ML |
|---|---|---|
| Pricing model | Unknown | usage-based |
| Platforms | Kubernetes | 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 Kubeflow
- ML pipelines
- Training operators
- Model serving
- Jupyter notebooks
- Hyperparameter tuning
- Kubernetes
- TensorFlow
- PyTorch
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.
Kubeflow
- Machine learningnot Amazon Redshift ML
- Data analysisnot Amazon Redshift ML
- Model trainingnot Amazon Redshift ML
- Predictive analyticsnot 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 Kubeflow
- Letting an analytics team test whether a predictive column has any business value before asking for data science headcountnot Kubeflow
- Scoring rows inside a SQL pipeline where moving data out to a separate service would add fragility for little benefitnot Kubeflow
- Organisations committed to AWS whose main constraint is a data science backlog rather than modelling sophisticationnot Kubeflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Kubeflow
- Complex installation and configuration requiring Kubernetes expertise, upgrade paths between versions need manual CRD migrations
- Resource-intensive infrastructure with minimal installs consuming significant CPU and memory
- Limited multi-tenancy support and multi-cloud setup leaves users largely on their own
- No native CI/CD integration, requiring custom glue code for versioning and automated deployments
- Debugging jobs and monitoring workloads often requires dropping down into raw Kubernetes commands
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
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
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 Kubeflow if
- You need ml pipelines.
- You want to start without paying.
- You work on Kubernetes.
- You also want training operators.
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 Kubeflow or Amazon Redshift ML better?
- Neither clearly leads. Kubeflow 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, Kubeflow or Amazon Redshift ML?
- Kubeflow starts at Free and Amazon Redshift ML at Free.
- Does Kubeflow or Amazon Redshift ML run on more platforms?
- Kubeflow runs on Kubernetes. Amazon Redshift ML runs on Web.
- Can I use Kubeflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Kubeflow best used for?
- Kubeflow is most often used for machine learning, data analysis, model training, predictive analytics. Of those, machine learning and data analysis are not what Amazon Redshift ML is typically brought in for.
- What can Kubeflow do that Amazon Redshift ML cannot?
- Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Amazon Redshift ML covers CREATE MODEL in SQL, Automatic model selection, Local inference, Bring your own model.
Answered from the vendors’ own pages
Kubeflow: Is Kubeflow free to use?
Yes, Kubeflow is free and open-source under Apache License 2.0. However, you pay for the underlying Kubernetes infrastructure, which typically costs $500 to $5,000 per month depending on scale and cloud provider.
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.
Kubeflow: Do I need Kubernetes expertise to use Kubeflow?
Kubeflow requires significant Kubernetes and DevOps expertise. The installation deploys dozens of services and CRDs, often requiring manual configuration and troubleshooting. Data scientists typically need to convert scripts to containerized components.
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.
Kubeflow: What platforms can Kubeflow run on?
Kubeflow runs on any Kubernetes-compliant cluster, including on-premise, AWS, Azure, Google Cloud, and hybrid environments. This multi-cloud portability is one of its key advantages over managed alternatives.
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.
Kubeflow: How does Kubeflow compare to managed services like SageMaker?
Kubeflow offers multi-cloud portability and lower long-term costs but requires more operational overhead. SageMaker provides a fully managed experience with better UI and less infrastructure work, but creates vendor lock-in to AWS.
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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