How Forter Reduced Cloud Native MTTR and Engineering Toil with AI SRE 

Forter operates a real-time, AI-driven fraud prevention and identity trust platform for digital commerce. Processing decisions for more than 400,000 businesses and $2 trillion in transactions, their platform engineering team is tasked with maintaining an infrastructure that can handle massive throughput with near-zero latency and high availability.

The Challenge Forter Faced When Migrating to Kubernetes

When Forter began migrating from EC2 to Kubernetes, their platform team encountered significant pain points, including:

• Observability and Insight: Gaining visibility across all new Kubernetes clusters was described as “difficult, quite painful.”
• Cost Management: The new Kubernetes environment quickly became expensive. Their existing cost management solution was not effective for Kubernetes-specific optimization, making it difficult to balance costs without impacting reliability.

How Komodor Helped Overcome Those Challenges

Komodor addressed these challenges by consolidating tooling, simplifying the developer experience, and introducing Kubernetes-native capabilities:

• Centralized and Simplified Observability: Komodor provides a centralized view for all clusters and an out-of-the-box Health View Dashboard that presents information in Kubernetes-native terms (pods, services), eliminating the need for engineers to build custom dashboards or use complex CLI tools.
• Streamlined UI for DevOps Tools: Teams prefer the Komodor UI over native UIs because it makes viewing states and troubleshooting simpler.
• Cost Optimization: The platform team was able to rapidly begin a cost reduction initiative using Komodor’s right-sizing and pod placement features. This functionality was a critical addition that prevented the need to find and integrate a separate third-party vendor for Kubernetes cost optimization.
• AI-Powered Root Cause Analysis (Klaudia): This feature became the most valued, enabling engineers to quickly diagnose issues. Klaudia automatically correlates data from different sources (on top of logs, metrics, events, etc.) and provides a clear root cause, with a high accuracy rate of approximately 95%.

The Outcomes and Impact Komodor Had on Forter

The implementation of Komodor delivered several tangible and strategic benefits for Forter:

• Self-Service and MTTR Reduction: Developers gained the ability to troubleshoot and resolve their own issues more quickly, reducing escalations to the platform team. This saved the team from having to field Kubernetes escalations that would have otherwise consumed substantial engineering time.
• Reduced Toil and Context Switching: By automating troubleshooting and reducing escalations, the platform team significantly reduced their toil and context switching, freeing up time to focus on strategic projects.
• Strategic Capacity for Platform Team: The time saved allowed the small platform team to focus on high-value company initiatives.
• Skill Enablement: Komodor’s RCA feature actively teaches engineers as they use it, explaining the root cause logic and accelerating the team’s overall Kubernetes knowledge.