Isolation Forest
Distributed Spark/Scala implementation for unsupervised outlier detection.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is Isolation Forest?
A distributed Spark/Scala implementation of the isolation forest algorithm designed for scalable training and ONNX export, enabling efficient cross-platform inference for unsupervised outlier detection tasks.
Key differentiator
“The only Spark-based implementation of isolation forest with built-in support for scalable training and ONNX export, making it ideal for large-scale unsupervised outlier detection tasks.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The tool is primarily developed in Scala, which might limit its accessibility and integration for developers who are not familiar with or do not use Scala.
Setting up a distributed environment using Spark can be complex and requires significant infrastructure knowledge, which may pose challenges for teams without extensive experience in this area.
While designed to handle scalable training, performance degradation can occur when processing extremely large datasets due to the overhead of distributed computing and data shuffling across nodes.
Fit analysis
Who is it for?
✓ Best for
Teams working with Spark who need scalable outlier detection solutions
Projects requiring cross-platform inference capabilities via ONNX export
✕ Not a fit for
Applications that require real-time anomaly detection without batch processing
Scenarios where the overhead of distributed computing is not justified by dataset size
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Works well with
Next step
Get Started with Isolation Forest
Step-by-step setup guide with code examples and common gotchas.