How Coralogix cut compute costs by 45% with 0 R&D efforts

29%
Reduced CPU
utilization
45%
Reduced
compute cost
15%
Increased
throughput
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Coralogix turned to Granulate's real-time continuous optimization solution to support the company's fast growth and provide both benefits, increase performance, and reduce infrastructure costs. Following Granulate's activation, Coralogix achieved a 45% cost reduction as well as 30% processing time reduction, reduced CPU utilization, and an increase in throughput.

About

Coralogix helps software companies avoid getting lost in their log data by automatically figuring out their production problems. Coralogix’s machine learning powered platform turns your cluttered log data into a meaningful set of templates and flows.

“Within 2 weeks and without any customization whatsoever, we started to experience unbelievable performance improvement results that helped us achieve significant cost reduction”

Ariel Assaraf, CEO

The Challenge

Coralogix’s fast-growth and quickly expanding customer base leads to a significant increase in the amount of data and records that the company needs to process. They built a scalable AWS EKS based cluster to support the intensive compute needs. This EKS cluster serves trillions of log processing rules over hundreds of Terabytes of data every second. Coralogix was hard-pressed to ensure that it was achieving optimum performance, efficiency, and customer experience with its cloud applications, while also efficiently spending their cloud budget. Coralogix was looking for a solution to support their fast growth and provide reduced infrastructure costs and increased performance and capacity to support future growth.

The Results

Following Granulate’s activation, Coralogix saw immediate performance improvements.

Monitoring their performance in their Grafana showed 30% reduction in the average rules processing time, along with a throughput increase of 15% and a 29% reduction to CPU utilization. These performance results led to an automatic reduction of the cluster size by 45% due to Kubernetes HPA policies while maintaining the same QOS as before Granulate.

The following graph shows the 29% reduction in CPU utilization leading to a 45% cluster size reduction with CPU utilization and the number of Kubernetes pods with Granulate (green and orange) vs. without (yellow and red).

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Cut compute costs, with 0 code changes

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