Case Study: Olo improves database query times by 50% and application throughput by 40% with Datadog Continuous Profiler

Olo improves application throughput and database query times with Datadog Continuous Profiler

Olo is a B2B SaaS platform that helps 600+ restaurant brands scale online ordering, guest engagement, and payments, reaching 85 million guests and processing over two million orders per day. As it migrated from a monolith to an API-first, .NET 6 microservices architecture on AWS, the company experienced falling application throughput and rising database query times with little visibility into root causes.

By deploying Datadog Continuous Profiler, Olo quickly pinpointed inefficient reflection usage and mis-mapped PostgreSQL enums in its services, fixed the problematic code paths, and optimized cache object construction. Those changes yielded roughly 40% higher application processing throughput and a 50% improvement in database query times, while speeding incident response, lowering cloud costs, and improving scalability and resiliency.

Olo

Mike Clark

Staff Engineer