Trax’s mission is to enable brands and retailers to harness the power of digital technologies to produce the best shopping experiences for customers. Its industry-leading innovations and excellence in developing advanced technologies and autonomous data collection methods are driving positive shopper experiences and unlocking revenue opportunities at all points of sale.
Trax’s solution portfolio provides mission-critical metrics, analytics, and services that help customers save time and money by improving their shopping experience. Kubernetes is a key component of their infrastructure, enabling Trax to continuously innovate its solution while providing the scalability to consistently meet demand. Trax has grown to a large-scale multicloud, multi-cluster environment supporting customers in over 90 countries, including some of the globe’s largest enterprises.
At the beginning of the year, the Chief Financial Officer (CFO) laid out strong cost savings goals throughout the whole organization. For Mark Serdze, Director of Cloud Infrastructure, and his team, this meant quickly taking action to optimize their cloud costs.
Trax achieved fast results when reducing costs outside of Kubernetes, but it started hitting roadblocks when it came to optimizing everything within its clusters.
“We started optimizing manually with available metrics, using Vertical Pod Autoscaler (VPA), cluster logs, and our monitoring solutions,” explained Serdze. “This approach didn’t give us proper clarity and was challenging to scale efficiently without big development needs. This left us taking ad-hoc, reactive actions that were having minimal impact on our goals.”
Trax’s existing tooling also introduced friction into the optimization process. Even when team members identified potential cost-saving opportunities, the approval process to validate the best course of action was based on instinct rather than data. Due to the lack of visibility, some missteps led to increased workload, created internal tension, and introduced risks that could compromise service resilience.
It quickly became evident to Serdze and his team that their toolset lacked the intelligence and clarity to optimize their Kubernetes environments effectively.
Shortly after deploying PerfectScale by DoiT, Serdze and the team gained the cost visibility they were missing. Across Trax’s vast 200+ microservice environment, they had clear visibility into what resources each service needed, and were able to identify the most significant opportunities to eliminate waste.
The platform’s AI-guided intelligence allowed the team to take action to start reducing costs quickly. By comparing cost savings with overall resilience, they could adjust resources safely and efficiently without compromising performance.
“The cost optimization recommendations were key for us, telling us what actions to take with a clear understanding of the impact each change would have,” Serdze explained. “In one of our clusters, we were able to reduce cost by 75%, saving us over a hundred thousands dollars in yearly expenses.”
Furthermore, Trax was impressed by the comprehensive data and intelligence the solution provided across its entire environment. This implementation allowed it to upgrade its cost visibility toolset without impacting its budget.
“We were able to replace a FinOps tool we were using that didn’t provide granular cost details or offer guidance on how to optimize our environment,” explained Serdze. “PerfectScale is a tool built for the engineering teams, not just for finance, which made it easier for us to make the cost impacts we wanted.”
Kubernetes optimization to improve business metrics
After eliminating the wasted resources, Trax focused on identifying additional opportunities for cost optimization. The team delved into PerfectScale’s data, seeking ways to meaningfully impact their cost-centric business metrics.
“A key metric for us is ‘cost per processing,’ which is heavily affected by our Kubernetes efficiency,” said Serdze. “If it gets over a certain amount, we are under a lot of pressure to figure out why and to take actions to reduce it.”
PerfectScale has a unique feature that consolidates every replica of a service into a single view to provide a clear picture of the utilization trends across all replicas, which is especially useful for ephemeral workloads like Spark or Flink jobs. Trax leveraged this capability to understand better the heterogeneous utilization across the replicas of several of its heavily used services. This level of visibility helped it rearchitect some of these services to drive additional cost savings without impacting their resilience or availability.
“We were able to build multiple flavors of the service with different levels of resources and route the incoming requests to the proper service based on the size of the data,” explained Serdze. “This made a big impact on our ‘cost per processing’ metric. PerfectScale surfaced this data instantly, and without them, we would have spent countless hours evaluating hundreds of replicas to generate the same results.”
By adopting PerfectScale by DoiT, Trax achieved rapid, measurable results that aligned with both technical and financial goals:
Beyond the numbers, Trax gained strategic clarity. What once required hours of manual analysis and risky trial-and-error is now automated, guided, and precise. With PerfectScale by DoiT, Trax not only met significant cost targets but also set the foundation for long-term Kubernetes efficiency at scale.
“The support during the proof of concept (POC) made a big impact in helping us drive quick results,” said Serdze. “The PerfectScale team sat with us, helped us optimize, and ensured our success in using the platform. I have not seen this level of commitment from other vendors, and I am glad we found a partner we can rely on to help us keep our Kubernetes cost in check as we continue to scale.”
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From cost optimization to cloud migration, machine learning and CloudOps, we’re here to make the public cloud easy.
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