AI · head to head
Arize AI vs Apache Spark MLlib

Arize AI
AI
AI engineering platform for observability and evaluation of agents and LLM apps
- From
- Free
- Rated
- -

Apache Spark MLlib
Machine Learning
Scalable machine learning on Apache Spark
- From
- Free
- Rated
- -
The short version
- Each has a real cost: Arize AI the free plan caps trace spans at 25,000 per month with only 15 days of retention.; Apache Spark MLlib apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
- They diverge on capability: Arize AI covers End-to-end tracing, Apache Spark MLlib covers Classification.
Where they differ
Only the attributes on which Arize AI and Apache Spark MLlib actually diverge.
| Attribute | Arize AI | Apache Spark MLlib |
|---|---|---|
| Pricing model | freemium | open-source |
| Platforms | web, api | Linux, macOS, Windows |
| Category | AI | Machine Learning |
| Founded | Unknown | 1999 |
Identical on both: starting price (Free), free tier (Yes), user rating (Not yet rated).
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 Arize AI
- End-to-end tracing
- Evaluation framework
- Prompt testing and improvement
- Alyx AI engineering agent
- Custom dashboards
- Data warehouse integrations
Only in Apache Spark MLlib
- Classification
- Regression
- Clustering
- Collaborative filtering
- Feature engineering
- Apache Spark
- Hadoop
- Kafka
What people use each for
The jobs each tool is most often brought in to do.
Arize AI
- Tracing and debugging production AI agentsnot Apache Spark MLlib
- Running large-scale evaluations across traces and sessionsnot Apache Spark MLlib
- Improving prompts before production rolloutnot Apache Spark MLlib
- Storing and querying GenAI traces alongside a data warehousenot Apache Spark MLlib
Apache Spark MLlib
- Machine learningnot Arize AI
- Data sciencenot Arize AI
- Distributed computingnot Arize AI
Where each one falls short
Documented limitations, not opinions. Every one is a constraint you would hit in normal use.
Arize AI
- The free plan caps trace spans at 25,000 per month with only 15 days of retention.
- Self-hosted deployment and Data Fabric integration are restricted to the custom-priced Enterprise tier.
- Pricing beyond the $50/month Pro plan requires a custom quote, making cost planning less transparent at scale.
- Advanced compliance features like HIPAA are only available on Enterprise.
Apache Spark MLlib
- Apache Spark MLlib is Apache 2.0 licensed and free with no paid tier from the Apache project itself; SLA-backed support has to be sourced from a third party such as a managed Spark vendor rather than from Apache.
Pricing, plan by plan
Arize AI
Free- AX FreeFree
- 25k trace spans/month
- 1 GB storage/month
- 15-day retention
- AX Pro$50/month
- 50k trace spans/month
- 10 GB storage/month
- 30-day retention
- AX Enterprise$undefined/mo
- Custom trace spans, storage, and retention
- SaaS or self-hosted deployment
- Managed agents and Data Fabric
Apache Spark MLlib
FreeNo published plan breakdown. See the Apache Spark MLlib review.
Which should you pick?
Choose Arize AI if
- You need end-to-end tracing.
- You want to start without paying.
- You work on web, api.
- You also want evaluation framework.
Choose Apache Spark MLlib if
- You need classification.
- You want to start without paying.
- You work on Linux, macOS, Windows.
- You also want regression.
Questions people ask
- Is Arize AI or Apache Spark MLlib better?
- Neither clearly leads. Arize AI 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, Arize AI or Apache Spark MLlib?
- Arize AI starts at Free and Apache Spark MLlib at Free.
- Does Arize AI or Apache Spark MLlib run on more platforms?
- Arize AI runs on web, api. Apache Spark MLlib runs on Linux, macOS, Windows.
- Can I use Arize AI for free?
- Both have a free tier, so you can try either at no cost before committing.
- What is Arize AI best used for?
- Arize AI is most often used for tracing and debugging production ai agents, running large-scale evaluations across traces and sessions, improving prompts before production rollout, storing and querying genai traces alongside a data warehouse. Of those, tracing and debugging production ai agents and running large-scale evaluations across traces and sessions are not what Apache Spark MLlib is typically brought in for.
- What can Arize AI do that Apache Spark MLlib cannot?
- Arize AI covers End-to-end tracing, Evaluation framework, Prompt testing and improvement, Alyx AI engineering agent. Apache Spark MLlib covers Classification, Regression, Clustering, Collaborative filtering.
Answered from the vendors’ own pages
Arize AI: What does Arize AX cost?
Arize AX offers a free plan, a Pro plan at $50/month, and a custom-priced Enterprise plan, with pricing based on trace spans, storage, and retention needs.
SourceApache Spark MLlib: How much does Apache Spark MLlib cost?
MLlib is completely free and open source, licensed under the Apache License Version 2.0. There are no subscription, licensing, or usage fees.
SourceArize AI: Is there a free plan, and what are its limits?
The AX Free plan includes 25,000 trace spans and 1 GB of storage per month with 15-day retention, plus unlimited users, evaluations, and experiments.
SourceApache Spark MLlib: What licensing does MLlib use?
MLlib is licensed under Apache License Version 2.0, making it freely available for all users regardless of organization size or use case.
SourceArize AI: What deployment options are available?
Free and Pro plans are SaaS-only, while Enterprise customers can choose SaaS or self-hosted deployment with custom SLAs.
SourceApache Spark MLlib: How do I use MLlib?
MLlib is built into Apache Spark. Download Spark, which includes MLlib as a module, and deploy on your choice of infrastructure including Hadoop, Mesos, Kubernetes, standalone, or cloud.
SourceRelated pages
More on Apache Spark MLlib
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