Machine Learning · head to head
Kubeflow vs Apache Spark MLlib
Apache Spark MLlib
Machine Learning
The machine learning library inside Apache Spark, for data that will not fit on one machine
- 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; Apache Spark MLlib the algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
- They diverge on capability: Kubeflow covers ML pipelines, Apache Spark MLlib covers DataFrame-based pipelines.
- Prices and features above were last checked on 30 August 2026.
Where they differ
Only the attributes on which Kubeflow and Apache Spark MLlib actually diverge.
| Attribute | Kubeflow | Apache Spark MLlib |
|---|---|---|
| Pricing model | Unknown | open-source |
| Platforms | Kubernetes | Linux, macOS, Windows |
| Founded | 2017 | 1999 |
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 Apache Spark MLlib
- DataFrame-based pipelines
- Distributed algorithms
- Alternating least squares
- Feature transformers
- Model selection
- Pipeline persistence
- Language bindings
- Runs in existing Spark deployments
What people use each for
The jobs each tool is most often brought in to do.
Kubeflow
- Machine learningnot Apache Spark MLlib
- Data analysisnot Apache Spark MLlib
- Model trainingnot Apache Spark MLlib
- Predictive analyticsnot Apache Spark MLlib
Apache Spark MLlib
- Training on a data set too large to hold on one machine, where sampling down would lose the rare events you care aboutnot Kubeflow
- Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot Kubeflow
- Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot Kubeflow
- Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot 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
Apache Spark MLlib
- The algorithm set has grown slowly and its gradient boosting does not match XGBoost or LightGBM in accuracy or speed, so teams routinely do feature engineering in Spark and then train elsewhere, which undoes the argument for using it at all.
- There is no deep learning in MLlib; neural network work on Spark requires a separate integration, and the DataFrame-centred interface is an awkward fit for it.
- Fitted models serialise into Spark's own format, so low-latency serving needs either a Spark session in the request path, which is far too slow, or a conversion through ONNX or MLeap, and this is where most Spark ML projects stall.
- Debugging is JVM cluster debugging: executor out-of-memory, shuffle spill, skewed partitions and serialisation failures, so an engineer without Spark operations experience spends more time tuning the cluster than improving the model.
- The cluster is the real cost and Spark holds executors for the duration of a job, so a badly partitioned training run pays for idle cores across the whole fleet while one straggler task finishes.
Pricing, plan by plan
Kubeflow
FreeNo published plan breakdown. See the Kubeflow review.
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
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 Apache Spark MLlib if
- You need dataframe-based pipelines.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want distributed algorithms.
Questions people ask
- Is Kubeflow or Apache Spark MLlib better?
- Neither clearly leads. Kubeflow starts at Free and Apache Spark MLlib at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
- Which is cheaper, Kubeflow or Apache Spark MLlib?
- Kubeflow starts at Free and Apache Spark MLlib at Free.
- Does Kubeflow or Apache Spark MLlib run on more platforms?
- Kubeflow runs on Kubernetes. Apache Spark MLlib runs on Linux, macOS, Windows.
- 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 Apache Spark MLlib is typically brought in for.
- What can Kubeflow do that Apache Spark MLlib cannot?
- Kubeflow covers ML pipelines, Training operators, Model serving, Jupyter notebooks. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.
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.
SourceApache Spark MLlib: What is the difference between spark.ml and spark.mllib?
spark.ml is the DataFrame-based interface and the one to use. spark.mllib is the older RDD-based package, kept for compatibility, in maintenance and receiving no new features.
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.
SourceApache Spark MLlib: Do I need a cluster?
Spark runs in local mode on one machine, which is useful for development, but if you are running on one machine you would generally be better served by scikit-learn or XGBoost, which are faster and more capable at that scale.
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.
SourceApache Spark MLlib: Can I use scikit-learn on Spark instead?
Yes, and it is often the better answer. You can distribute independent model fits across the cluster, or use pandas user-defined functions to run per-group models, keeping Spark for the data and a mature library for the modelling.
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.
SourceApache Spark MLlib: How do I serve an MLlib model in real time?
Not directly. Either convert the pipeline to a portable format such as ONNX or MLeap, or reimplement the scoring path. Starting a Spark session per request adds seconds of overhead and is not a serving strategy.
Apache Spark MLlib: Is it free?
The library is Apache 2.0 and costs nothing. The cluster it runs on is billed by your cloud provider or by Databricks, and that is the actual expense.
Related pages
More on Apache Spark MLlib
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