Achieve Real-World Business Outcomes with ML Quickly & Efficiently

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Talk to an ML Expert

Talk to a Wallaroo.AI machine learning expert today about your unique use case – and schedule your own ML demo.

This demo shows how you can get ML into your production environment faster to impact the bottom line.

  • Deploy models in seconds
  • Reduce compute costs by 80%
  • Real-time insights to ensure ongoing business value
  • Analyze data up to 12.5X faster
  • Iterate quickly and scale easily
  • A self-service toolkit to make your AI team more efficient

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“Wallaroo customers consistently shared that the tool made their teams more efficient and more effective at creating business value. This infectious evangelism is exactly what we felt firsthand when talking to users of Wallaroo.”
Priyanka Mitra
Principal at M12
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The Easiest Way to Deploy, Run, and Observe Your ML in Production at Scale

Wallaroo makes it affordable, easy and fast to get AI algorithms live against production data so your production can scale, and you can analyze data up to 12.5X faster while reducing compute costs by 80%.

ML models in seconds with a single line of Python code and/or a flexible API.


ML with up to 80% lower cost and easily scale to more data, more models, more complex models.


Identify sources of model underperformance and production bottlenecks in real-time.


Live models based on the latest data and insights with no downtime.


Scale Past the Competition with Wallaroo’s Unmatched Performance

Wallaroo quickly gets ML into your production environment to impact your bottom line. See how we stack up against the competition.

Talk to an ML Expert
*Based on results from ALOHA (ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation) model, a complex, open-source model used for benchmark and performance testing. Test involved scoring 2 Billion events a day (23K events every second), analyzed in batches running on 16-CPU server on GCP (Vertex), AWS (SageMaker) and Azure (Databricks)

Wallaroo is Trusted By:

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Wallaroo Drives Results: Case Studies


A Fortune 100 enterprise needed to deploy over 100 ML models to detect security breaches and the models had to be retrained and updated monthly. Before Wallaroo, the client’s process required 5 data scientists, often taking 2 weeks to deploy a model and needed 600 servers to run them.

Here’s what we achieved:

84% Reduction in computing costs
Data Scientists were freed to focus on innovating
Seconds to deploy updated models

Monetize Your Machine Learning

Wallaroo facilitates the last mile of your ML deployment by removing complexity, automating deployment tasks, reducing infrastructure cost and providing full observability. Now is the time to move your models past the last mile of deployment and impact your bottom line.

Ready to deploy, run and observe your ML in production at scale?

Talk to an ML Expert
Talk to an ML Expert