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Easy Production AI at Scale: Any Model, Any Hardware, Anywhere

Purpose built for production AI, so AI teams stay lean and nimble. Enabling you to get to value fast for your cloud analytics, edge AI and gen AI initiatives.
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faster time to value
0 x
more deployments with minimal effort
0 x
reduction in deployment costs
0 %

90% of AI initiatives fail to produce ROI.

Over 100 Chief Data Officers report these key challenges:

Lack of automation, engineering bandwidth
Infrastructure complexity, cost, availability
No feedback loop from deployment to business impact

We help solve that.

DEPLOY, SERVE, OBSERVE, AND OPTIMIZE ML

From your notebook to real-world results with minimal engineering or delays

Deploy in seconds Self-Service toolkit to deploy and scale ML

Easy-to-use SDK, UI, and API for fast, repeatable, low-code/no-code ML operations

Scalability + performance Blazingly Fast Inference Server

Distributed computing core written in Rust-Lang uses up to 80% less infrastructure

Continuous Optimization Advanced Observability

Comprehensive audit logs, advanced model insights, full A/B testing

How it works

Wallaroo.AI Enterprise Platform for Production AI at Scale Your Data, Your Ecosystem. Our Software. Our Team.

Designed for Data Scientists and ML Engineers alike

Data
Sources
Load &
prep data
Develop
ML models
Wallaroo Production
ML Platform
Explore
Outcomes
Data Lake
What makes Wallaroo.AI different?

Breakthrough speed and agility in the cloud or at the edge

Wallaroo unified inference platform orchestration illustration | Wallaroo.AI

Flexible integration with your tools and infrastructure

  • Integrates with your ML toolchain (notebooks, model registries, experiment tracking, etc.)
  • Intuitive APIs and Connectors for custom workflow integrations
  • Works with specialized ML acceleration libraries
Wallaroo unified inference platform orchestration illustration | Wallaroo.AI

Ultrafast Rust-based Server that lowers cost by 50% – 80%

  • 3X- 13X faster Batch Inferencing
  • Real-time Inferencing Latency as low as 1 microsecond
  • 5X more efficient data handling
  • Workload autoscaling
  • (As benchmarked with the ALOHA open-source model, various TF, XGBoost, and Computer Vision models in AWS, Azure, GCP, and Databricks)

    Centralized model management, observability, and optimization

  • Collaborative Workspaces with access control
  • Automated feedback loop for ML monitoring and redeployment
  • Integrated Model Validation with A/B testing and Canary deployments
  • Easy-to-use, unified UI
  • Learn more about the Wallaroo.AI platform
    Only Wallaroo.AI

    Go from Python Notebook to Results, Fast

    Efficient

    Easily scale number of live models by 10X with minimal effort and via automation

    • Focus on business outcomes
    • Deploy models in seconds via automation and self-service
    • Robust API and data connectors means easy integration
    Flexible

    Up to 12X faster inferencing, 80% lower cost, and free up 40% of AI team’s time

    • Target x86/ARM/CPU/GPU via simple config
    • Don’t be blocked on GPU availability
    • Sub-millisecond latency + efficient analysis of large batches
    Easy

    Continuously observe, optimize AI in production to get to value 3X faster

    • Troubleshoot live models in real-time
    • Retrain and hot-swap live models
    • Centrally observe and manage local or remote pipelines

    Turbocharge your AI workloads in your own local cloud, a remote cloud, or at the edge

    Your Data Science /
    ML Environments
    Wallaroo
    Operations Center
    Your Cloud /
    On Prem
    Wallaroo
    Server
    Your Cloud /
    On Prem
    Wallaroo
    Server
    Remote Cloud /
    On Prem
    Wallaroo
    Server
    Edge
    Locations
    Wallaroo
    Server
    Fortune 100

    A Fortune 100 enterprise needed to deploy over 100 ML models to detect security breaches. Plus, the models had to be retrained and updated monthly.

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    Healthcare

    A medical device manufacturer was looking for a simple and scalable way to deploy and manage models to thousands of endpoint devices.

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    Financial Services

    One of the top 10 banks in the world needed to analyze billions of streaming events in real time while making it easy and fast to update cybersecurity models.

    Read more

    Food Service

    An online food delivery firm wanted to know instantly when their fraud detection models started to drift.

    Read more
    Software

    A large software company wanted to focus their sales team on the highest propensity prospects by generating better insights from calls.

    Read more
    Entertainment

    The Detroit Lions sought to maximize revenue for live event spot ticket prices during the course of the National Football League (NFL) season.

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    Military

    The US Military needed to analyze petabytes of daily IoT data in the cloud and across millions of edge devices, including drones and ships, to swiftly detect security anomalies.

    Read more
    Real Estate

    One of the top 5 real estate investment trusts (REITs) in the U.S. needed to scale dynamic pricing to hundreds of regions and thousands of locations.

    Read more
    Software

    An analytics and management software firm wanted to optimize their own customers’ complex buying journeys, integrating both first-party and third-party data into their analysis.

    Read more

    Latest News

    Get Your AI Models Into Production, Fast.

    Unblock your AI team with the easiest, fastest, and most flexible way to deploy AI without complexity or compromise. 

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    Platform
    Platform Learn how our unified platform enables ML deployment, serving, observability and optimization
    Technology Get a deeper dive into the unique technology behind our ML production platform
    Solutions
    Solutions See how our unified ML platform supports any model for any use case
    Computer Vision (AI) Run even complex models in constrained environments, with hundreds or thousands of endpoints
    Resources