How cloud cost observability delivered recommendations that lowered cluster costs by 50% for Jiffy.ai
Year
Built on AI and no-code technologies, JIFFY.ai's full-stack platform enables banks, financial services companies, and Fortune 500 organizations to solve their pressing operational and efficiency challenges. Their ready-to-deploy HyperApps help business users to improve customer experience and realize the true value of digital transformation in a fast-tracked timeline.
Industry
Region
Key Features
Cloud Optimization, K8s Optimization, Visualization/Reporting
Introduction
Automation is fuelling the next revolution of driving enterprise efficiency and operational excellence. Successfully empowering businesses with automated solutions requires technical architecture that can support compute and processing at scale. Businesses providing automation technology often see this as one of the biggest challenges they need to overcome in order to manage their costs and maintain a lean technology infrastructure. As one of the rapidly growing players in the no-code, automation space, Jiffy.ai realized the need for cloud cost observability, to augment their teams and support the culture of cost optimization.
Challenges Faced
The team at Jiffy.ai were looking for solutions that could help them gain a 360-degree view of their cloud costs. This was also exacerbated with the need for actionable insights, anomalies and a seamless system of integrating cost observability into the existing workflows. One of the biggest challenges they faced was the mounting costs of Kubernetes, and the lack of visibility into cluster utilization and inefficient resource allocation.The team understood this problem at its grassroots level, and needed a solution that would help them address these challenges. Jiffy began the engagement with Amnic in a POC which was able to deliver recommendations on Kubernetes workloads. This was the first step in enabling them to implement and run their workloads more efficiently.
“Consistently staying uptime and keeping a hawk’s eye view on cloud costs are challenging for a team building a world class product. The ability to gain a single pane view into all costs associated with our cloud infrastructure was absolutely necessary to keep up with the scale of growth. Additionally, instrumentation of costs, analysis and optimization all needed to seamlessly integrate into our existing workflows.”
Sekhar Prakash
Co-founder, Cloud Engineering and Ops, Jiffy.ai
Solution
Amnic was able to bring continuous cloud cost observability to Jiffy.ai though its SaaS platform. Jiffy.ai was able to connect their cloud and use the out of the box visualizations to understand how they can optimize their infrastructure and costs better.
Through its astute recommendations, Jiffy.ai was able to make significant changes that directly impacted their cloud costs. Some of the recommendations included.
Recommendations for Right-Sizing Instances and Pods: Amnic’s platform helped analyze Jiffy.ai's Kubernetes clusters comprehensively, identifying opportunities for right-sizing instances and pods to match workload demands accurately.
Cluster, Instance, and Node-Level Views: With granular visibility into cluster, instance, and node-level metrics, Amnic was able to pinpoint inefficiencies and areas for optimization effectively.
Greater Visibility into Kubernetes Utilization: By leveraging the visualizations and recommendations provided by Amnic, Jiffy.ai gained deeper insights into Kubernetes utilization patterns, allowing them to make informed decisions about resource allocation and scaling strategies.
Results
50% Lower Cluster Costs
The proactive optimization efforts, guided by Amnic's insights and recommendations, led to a remarkable 50% reduction in cluster expenses for Jiffy.ai, demonstrating the efficacy of the implemented strategies.
“Consistently staying uptime and keeping a hawk’s eye view on cloud costs are challenging for a team building a world class product. The ability to gain a single pane view into all costs associated with our cloud infrastructure was absolutely necessary to keep up with the scale of growth. Additionally, instrumentation of costs, analysis and optimization all needed to seamlessly integrate into our existing workflows.”
Sekhar Prakash
Co-founder, Cloud Engineering and Ops, Jiffy.ai
Amnic proved instrumental in addressing the challenge of escalating Kubernetes cluster costs effectively for Jiffy.ai. Cloud cost observability was not only able to drive substantial cost savings, but also created a system of being able to continuously monitor costs and performance of their cloud infrastructure.
