Machine Learning · head to head
MLflow vs Apache Spark MLlib
MLflow
Machine Learning
Open source platform for managing the ML lifecycle
- From
- Free
- Rated
- -
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: MLflow requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves; 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: MLflow covers Experiment tracking, 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 MLflow and Apache Spark MLlib actually diverge.
| Attribute | MLflow | Apache Spark MLlib |
|---|---|---|
| Platforms | Web, Python API, REST API | Linux, macOS, Windows |
| Founded | 2018 | 1999 |
Identical on both: starting price (Free), pricing model (open-source), 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 MLflow
- Experiment tracking
- Model registry
- Model packaging
- Deployment
- Project organization
- TensorFlow
- PyTorch
- scikit-learn
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.
MLflow
- 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 MLflow
- Feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive datanot MLflow
- Batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does notnot MLflow
- Organisations that already run and pay for Spark, where adding a modelling step is cheaper than introducing a second platformnot MLflow
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
MLflow
- Requires infrastructure setup: teams must manage MLflow tracking server, database, and artifact storage themselves
- Basic UI and visualization: lacks rich interactive dashboards and real-time monitoring compared to commercial platforms
- Limited collaboration: no built-in role-based access control or multi-user management features
- Production monitoring gaps: drift detection, explainability, and alerting require separate dedicated tools
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
MLflow
Free- Open SourceFree
- Experiment tracking
- Model registry
- Deployment tools
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose MLflow if
- You need experiment tracking.
- You want to start without paying.
- You work on Web, Python API, REST API.
- You also want model registry.
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 MLflow or Apache Spark MLlib better?
- Neither clearly leads. MLflow 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, MLflow or Apache Spark MLlib?
- MLflow starts at Free and Apache Spark MLlib at Free.
- Does MLflow or Apache Spark MLlib run on more platforms?
- MLflow runs on Web, Python API, REST API. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use MLflow for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is MLflow best used for?
- MLflow 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 MLflow do that Apache Spark MLlib cannot?
- MLflow covers Experiment tracking, Model registry, Model packaging, Deployment. Apache Spark MLlib covers DataFrame-based pipelines, Distributed algorithms, Alternating least squares, Feature transformers.
Answered from the vendors’ own pages
MLflow: Is MLflow free to use?
Yes, MLflow is completely open-source and free. However, teams typically incur infrastructure costs for hosting and maintaining the MLflow tracking server. Databricks offers Managed MLflow as a commercial option for cloud deployment.
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.
MLflow: Can MLflow track experiments for different ML frameworks?
Yes, MLflow is framework-agnostic and works with TensorFlow, PyTorch, scikit-learn, XGBoost, and any other ML framework. This flexibility is a core design principle allowing teams to use diverse tools.
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.
MLflow: Does MLflow include a model registry?
Yes, MLflow Model Registry (added in 2018) provides a central model store with versioning, stage transitions, and deployment tracking. This enables production model governance and lineage tracking.
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.
MLflow: What are MLflow's main limitations?
MLflow requires significant infrastructure setup and maintenance. The UI is basic compared to commercial tools, collaboration is limited without third-party RBAC solutions, and production monitoring requires separate tools for drift detection and alerting.
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.
MLflow: Can MLflow handle LLM and agent tracing?
MLflow added LLM and agent tracing capabilities in recent versions, though the native support is limited compared to specialized LLM observability platforms that replaced weak LLM tracing.
SourceApache 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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