Operations | Monitoring | ITSM | DevOps | Cloud

There's a support ticket every SRE dreads: "is something wrong with my database?" The outage is bad enough. Worse is the possibility that the customer knew first. At Upstash, we treat that scenario as two failures rather than one: the incident itself, and the uptime monitoring gap that let a customer beat us to it. We write a postmortem for the gap, too.

Kubernetes is built for containers, and it’s been doing that since it used to run docker as an engine for its containers. But what if you want to add VMs to the mix? After all, containers are ephemeral and don’t require fixed IPs as they shift the identity toward labels, but VMs on the other hand are tied to IP addresses and in some cases MAC addresses. This brings us to this blog about VM migration and IP preservation.

Liftoff, a mobile advertising company, processes 1.5 trillion bid requests every month. Their platform touches 275 million unique devices daily across 150 geographies. At that scale, the proxy layer is a core part of the business. For years, Liftoff relied on a managed enterprise proxy vendor. It worked, until it didn’t.

Norsk Tipping is Norway’s state-owned gaming operator, running 2,500 to 3,000 production releases a year across iOS, Android, web and backend systems. Like every regulated organisation at scale, the platform team has to hold two things in tension: maintain strict deployment controls that stand up to audit, and keep the path to production open so that 100 engineers can ship safely.

Clover’s platform handles more than just payments: inventory, employee management, online sales, and customer loyalty programs are all running on a single monolith called the Clover Operating System (COS). Releasing updates to that platform reliably and without disrupting merchants is one of the hardest operational problems a platform team can face. For a decade, Clover ran HAProxy at the center of its infrastructure.

By benchmarking five GCP machine types across both Logstash and Kafka, LivePerson's observability team found that infrastructure selection (not just pipeline configuration) is one of the highest-leverage cost optimization decisions at scale.

We always knew that Airbnb’s engineering is operating on a completely different scale, and their new high-volume metrics pipeline is proof of that. This is one of those rare stories where scale and efficiency go hand in hand - they modernized their observability stack with open source components and reduced cost by an order of magnitude. Airbnb is now processing more than 100 million samples per second on a single production cluster.

Criteo handles 23 million requests per second (RPS) while maintaining peak performance and minimizing downtime. For most organizations, handling that level of traffic is just a theoretical stress test — a what-if scenario should their infrastructure ever be overwhelmed by an unexpected wave of requests. But for Criteo, 23 million RPS is just another Tuesday.