Case Study
Advancing Intelligence Analytics with AI
Cognyte eliminated infrastructure bottlenecks and saved $150K annually, enabling their security analytics team to run parallel model training and iterate 10x faster.
“SwarmOne boosted personnel efficiency by about 90%, significantly reduced training costs, and enhanced delivery, making us far more competitive in our market.”
Company
Cognyte
Industry
Security Analytics & Intelligence
Key Outcome
90% less engineering overhead
The Challenge
Infrastructure Overhead Slowing AI Innovation
Cognyte's AI team builds advanced models that power security analytics and intelligence operations. But their infrastructure was becoming the bottleneck - not the models themselves.
- Infrastructure bottlenecks delaying model training and forcing sequential experiment runs, limiting the team's ability to iterate quickly
- Dedicated engineering resources spending the majority of their time maintaining GPU clusters instead of building security analytics models
- Manual GPU provisioning creating unpredictable delays in the training pipeline, with each new experiment requiring hours of setup
- Difficulty scaling training infrastructure up and down with demand - leading to over-provisioning during quiet periods and resource shortages during peak experimentation
The Solution
Why Cognyte Chose SwarmOne
Cognyte needed a suite that would let their small but highly skilled team punch above their weight - running experiments in parallel, scaling on demand, and eliminating the DevOps tax entirely.
- Intelligent scheduling enabling parallel model training across GPU pools - running dozens of experiments simultaneously instead of one at a time
- Fully managed infrastructure eliminating DevOps overhead, freeing engineers to focus entirely on intelligence analytics model development
- Elastic scaling to match training demand without over-provisioning, automatically right-sizing resources for each workload
- Python SDK integration with the existing ML pipeline completed in under a week - minimal disruption, maximum impact
The Impact
Results That Speak for Themselves
90%
Less Engineering Overhead
$150K
Annual Savings
Parallel
Model Training
Engineering focus: 90% of engineering time redirected from infrastructure maintenance to model development and intelligence analytics innovation.
Parallel experimentation: Moved from sequential training runs to parallel execution, dramatically accelerating the pace of model iteration.
Cost efficiency: $150K in annual savings by eliminating dedicated GPU cluster management and over-provisioned resources.
Experience SwarmOne Today
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