Case Study: Qbeats achieves rapid, time-critical troubleshooting and improved reliability with Loggly

Qbeats Uses Loggly for Time-Critical Troubleshooting That Unlocks the Value of Information

qbeats is a cloud platform that dynamically values, prices, and matches time-sensitive content for investors. With about 20 services running on 120+ elastic VMs in AWS, even minute-long delays can trigger user complaints or trading losses, and qbeats’ small engineering and DevOps team struggled to aggregate and analyze logs from ephemeral servers. Their in-house Sentry setup couldn’t scale to millions of daily events, and running an ELK stack in-house would have been costly and time-consuming.

qbeats deployed Loggly in two days via syslog to aggregate logs from Python/Java/C++ apps, Nginx, and AWS across dev, QA, staging, and production. Using Loggly’s Dynamic Field Explorer, search, alerts, and hourly error digests, the team isolates cross-service issues faster, proactively resolves production problems, and improves code ownership. Results include greatly sped-up troubleshooting, reduced error volumes (from >100/hour to stretches of a week without hourly errors), and elimination of in‑house log management overhead—protecting revenue and customer satisfaction.

Qbeats

Maksym Markov

Vice President of Engineering