Machine Learning · alternatives
Alternatives to Apache Spark MLlib
22 machine learning tools sit alongside Apache Spark MLlib in this directory. Below is what separates each from Apache Spark MLlib on the figures we hold, price, model, tier count and rating, and a direct comparison for every one.
- Alternatives listed
- 22
- With a free tier
- 18
- Cheaper to start
- -
- Apache Spark MLlib starts at
- Free
Why people look past Apache Spark MLlib
Nothing on the record flags a reason to move. Apache Spark MLlib has a free tier. People still switch over fit and workflow, and those are not things a catalogue entry can measure, which is what the comparisons below are for.
What each alternative does differently
Ordered by the aggregated rating on each catalogue entry. Differences are drawn from price, pricing model, tier count and rating, the fields the category listing carries. For a feature-level difference, follow the head-to-head link on each card: those pages read both full records.
Build, train, and deploy machine learning models at scale
- Publishes an entry price of $0.04/hour, where Apache Spark MLlib does not.
Unified ML platform to build, deploy, and scale AI models
- No free tier, where Apache Spark MLlib has one.
Microsoft's managed platform for training, tracking and deploying models on Azure
- Publishes an entry price of Free, where Apache Spark MLlib does not.
- Sold on a usage-based model rather than open-source.
Enterprise AI platform for automated machine learning
- No free tier, where Apache Spark MLlib has one.
- Publishes an entry price of Free, where Apache Spark MLlib does not.
- Sold on a subscription model rather than open-source.
Open-source MLOps platform for experiment tracking and orchestration
- Sold on a Open-source self-hosted, with paid hosted and enterprise tiers model rather than open-source.
LLM engineering platform for testing and evaluating AI agents in production
- Publishes an entry price of €29/month, where Apache Spark MLlib does not.
- Sold on a Tiered subscription with usage-based overage charges model rather than open-source.
Every Apache Spark MLlib alternative at a glance
A dash means the catalogue entry carries no figure, not that the answer is nothing.
| Tool | Entry price | Model | Tiers | Head to head |
|---|---|---|---|---|
| Apache Spark MLlib (this page) | Free | Open-source | - | |
| AWS SageMakerBuild, train, and deploy machine learning models at scale | Free, then $0.04/hour | - | - | vs Apache Spark MLlib |
| Google Vertex AIUnified ML platform to build, deploy, and scale AI models | On request | - | - | vs Apache Spark MLlib |
| Azure Machine LearningEnterprise-grade machine learning service | Free | Usage-based | 2 | vs Apache Spark MLlib |
| DataRobotEnterprise AI platform for automated machine learning | Free | Subscription | 2 | vs Apache Spark MLlib |
| ClearML | Free | Open-source self-hosted, with paid hosted and enterprise tiers | 1 | vs Apache Spark MLlib |
| Langwatch | Free, then €29/month | Tiered subscription with usage-based overage charges | 3 | vs Apache Spark MLlib |
| Haystack | Free | Open-source with optional paid enterprise support | 2 | vs Apache Spark MLlib |
| Semantic Kernel | Free | Open source, no pricing | 1 | vs Apache Spark MLlib |
| H2O.ai | Free | Freemium | 2 | vs Apache Spark MLlib |
| Dask | Free | Open-source | 1 | vs Apache Spark MLlib |
| IBM SPSS | Free, then $99/month | Subscription | 2 | vs Apache Spark MLlib |
| JMP | Free | Subscription | 2 | vs Apache Spark MLlib |
| Anaconda | Free, then $15/month | - | 3 | vs Apache Spark MLlib |
| BentoML | Free | Freemium | 2 | vs Apache Spark MLlib |
| Dataiku | Free | Freemium | 2 | vs Apache Spark MLlib |
| Comet ML | Free, then $19/month | Freemium | 4 | vs Apache Spark MLlib |
| Hugging Face | Free | - | - | vs Apache Spark MLlib |
| BigQuery ML | Free | Usage-based | 2 | vs Apache Spark MLlib |
| Domino Data Lab | Free | Subscription | 2 | vs Apache Spark MLlib |
| DVC | Free | Open-source | 2 | vs Apache Spark MLlib |
| Fal AI | $1.89/hour | Usage-based | 2 | vs Apache Spark MLlib |
| Groq | On request | Quote | - | vs Apache Spark MLlib |
Ratings are aggregated from third-party sources and imported with each catalogue entry; Softwr hosts no reviews of these products. How each figure is used is set out on the Apache Spark MLlib badges page.
Cheaper ways to solve the same problem
Free to start (18)
These publish a tier that costs nothing, so they can be evaluated before any money changes hands.
- AWS SageMaker , Free, then $0.04/hour
- Azure Machine Learning , Free
- ClearML , Free
- Langwatch , Free, then €29/month
- Haystack , Free
- Semantic Kernel , Free
- H2O.ai , Free
- Dask , Free
What you would be giving up
Apache Spark MLlib is most often brought in for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Anything replacing it has to cover the ones you actually depend on, and a cheaper tool that misses one of them is not cheaper.
Two things this page cannot settle. It compares on price, model, tier count and rating, because those are the fields the category listing carries, feature-level differences need both full records, which is what the head-to-head pages load. And the ratings are third-party aggregates imported with each entry rather than reviews written here, so a 0.2 difference between two tools is noise rather than a finding.
If Apache Spark MLlib is broadly right and the question is cost, the Apache Spark MLlib pricing breakdown covers every tier and what each one adds. If you want the whole field rather than a shortlist, the Machine Learning category lists everything the directory holds, and best machine learning tools ranks them.
Apache Spark MLlib runs on linux, macos, windows. Platform coverage is not carried on the category listing for the alternatives, so it is one more thing to check on each head-to-head page rather than here.
Questions about Apache Spark MLlib alternatives
- What are the main alternatives to Apache Spark MLlib?
- 22 other machine learning tools are listed in this directory, led by AWS SageMaker, Google Vertex AI, Azure Machine Learning, DataRobot. They are ordered by the aggregated rating on each catalogue entry, not by any Softwr ranking.
- What is the best free alternative to Apache Spark MLlib?
- 18 of the alternatives listed here can be used without paying: AWS SageMaker, Azure Machine Learning, ClearML, Langwatch, Haystack.
- Is there a reason to switch away from Apache Spark MLlib?
- Nothing in the data flags one. Apache Spark MLlib has a free tier. Fit and workflow are the usual reasons to move, and those are not things this record can measure.
- What would I give up by switching from Apache Spark MLlib?
- Apache Spark MLlib is most often brought in for training on a data set too large to hold on one machine, where sampling down would lose the rare events you care about, feature engineering and model fitting in one job over tables already in the lake, avoiding an extract and a second copy of sensitive data, batch scoring of hundreds of millions of rows on a schedule, where throughput matters and per-request latency does not, organisations that already run and pay for spark, where adding a modelling step is cheaper than introducing a second platform. Anything you replace it with has to cover the ones you actually rely on, the side-by-side comparisons linked from each alternative below put the two feature records against each other.
- Is there an open-source alternative to Apache Spark MLlib?
- Dask, DVC are recorded with an open-source licence model.
- How were these Apache Spark MLlib alternatives chosen?
- They are the tools filed in the same category, Machine Learning, ordered by the aggregated rating on each entry. There is no editorial shortlist, and nothing on this page is paid: no sponsored placement runs on alternatives pages and the order cannot be bought. Softwr has not used these products.
- Where can I compare Apache Spark MLlib against one of these directly?
- Every alternative below has a side-by-side page against Apache Spark MLlib covering price, platforms, features and what each one is used for. Those pages read the full record for both products rather than the summary shown here.
- Does this list cover every machine learning tool?
- No. It covers what this directory holds in the Machine Learning category, 22 tools beside Apache Spark MLlib. The category page lists the rest of the catalogue as it grows.