OSS + ELC EU 2026

Uber runs about 4 million containers on over 150,000 hosts. Our fleet includes a mix of cgroupv1 and v2 hosts, multiple cloud providers, and multiple Linux kernel versions. Tracking container performance at this massive scale is a difficult engineering challenge, including the cost of storing them. We currently process 60 million data points every second to keep everything running smoothly.

We run Cadvisor and open-source Nvidia and AMD GPU metrics exporters to reliably and affordably collect and store these metrics. This infrastructure is vital for our real-time autoscaling, which ensures our services stay online. We also use these metrics to track memory usage and keep costs low for our AI workloads. Despite maintaining all the critical use cases, we have saved around $ 4 million compared to last year by changing how we collect and store data.

We will share how we use these metrics to detect and prevent system failures before they affect users. Attendees will learn practical patterns for operating container metrics at scale. We will also discuss the hard lessons we learned while building a reliable metrics system for a complex global fleet.