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Nylas chose Granulate's real-time continuous optimization solution to optimize GCP workload performance and costs. Within 5 days and without any code changes or R&D efforts, Nylas achieved over 35% cost reduction leveraging 58% faster processing time while handling 35% more throughput.
Nylas is a pioneer and leading provider of productivity infrastructure solutions for modern software. Over 50,000 developers worldwide use the Nylas platform to quickly and securely build productivity features into their applications. With Nylas, developers get unprecedented access to rich communications data from their end-users, pre-built workflows that automate everyday tasks, embeddable UI/UX components for fast front-end development, and comprehensive security features - all delivered via a suite of powerful APIs that make integration easy.
Nylas solution provides real-time bi-directional sync with users’ email, calendar, and contact data. Nylas Sync service is the key component of Nylas’ platform, when an account is connected to Nylas, which happens thousands of times a second, the Sync engine starts pulling in email messages for the account, pulling new message arrivals in parallel, and prioritizing the most recent ones to achieve a better user experience. As a core piece of Nylas API puzzle, the Sync service is required to operate quickly and efficiently, is extremely sensitive to performance, and serves under aggressive SLA of “time to sync” or latency.
After learning the Sync service resource usage patterns and data flow, the Granulate agent began making real-time decisions at the runtime level to prioritize resource allocation mechanisms and queues to achieve optimized performance. As a result, immediately after activation, Nylas achieved a 58% reduction in latency, allowing the same cluster to handle 2X more syncing requests on the cluster level while reducing CPU utilization by +10%.
These results allowed Nylas’ cluster to handle the sync queue much faster, handling more requests with fewer machines and reducing CPU utilization per machine. This entails substantial cost reduction, alongside improved performance on the cluster.