Case Study: Forter reduces Kubernetes MTTR and engineering toil with Komodor

Forter reduces cloud native MTTR by 65% with Komodor

Forter, a fraud detection platform processing trillions in transactions, faced significant challenges with observability and cost management during their migration to Kubernetes. Their platform team struggled with poor visibility across clusters and rising expenses, prompting them to seek a solution from Komodor.

Komodor provided a centralized platform with an AI-powered root cause analysis feature called Klaudia, which simplified troubleshooting and offered cost optimization tools. This solution enabled Forter to reduce their mean time to resolution (MTTR) by approximately 65%, cut costs by 35%, and save over 60 engineering hours per month, allowing their team to focus on strategic initiatives.

Forter