Platform teams often find themselves trapped: they build infrastructure but cannot answer "how reliable is it?" We break down how to define SLIs for internal platforms and negotiate SLOs with product teams.
We ran both tools on clusters ranging from 50 to 400 nodes. Here is where each one excels, where it struggles, and why the choice is about team culture, not technology.
Blameless postmortems are not just a document template. They require systematic changes to processes, metrics, and even the language teams use to describe failures. Here is a framework that works.
eBPF is no longer exotic — tools like Cilium, Pixie, and Tetragon make it accessible. We show how to get traces without code instrumentation and profile CPU with zero overhead in production.
Toil is work that scales linearly with service growth. We introduced a toil budget for every team and automated everything that exceeded 30%. Here is what happened.
The OTel Collector is the backbone of modern observability stacks. We share our battle-tested pipeline configuration for processing 2M spans/sec with filtering, sampling, and multi-backend export.
Resource requests and limits are only the beginning. Real capacity planning involves understanding burst patterns, node failure domains, and scheduling headroom. A practical guide to right-sizing clusters.
On-call rotations burn people out when done wrong. We document our approach to on-call that keeps engineers sane: escalation policies, runbooks, alert fatigue reduction, and compensation models.
Running PostgreSQL at scale requires more than replication. We cover our HA stack with Patroni, PgBouncer connection pooling, WAL archiving, and the failover scenarios that caught us off guard.
You do not need a Netflix-scale platform to benefit from chaos engineering. We walk through lightweight fault injection using LitmusChaos and simple scripts that exposed real production blind spots.