Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Datadog's bill has a way of tripling the quarter you actually start using it. Here are eight alternatives we've run in production and what each one costs you.
The metrics stack you self-host is free software plus a real ops bill. Datadog hands you everything and mails you the invoice. Here's how we pick.
A field guide to the OSS observability stack in 2026, sorted by pillar, with honest notes on where self-hosting pays off and where the ops burden bites.
One vendor sends the invoice, the other sends the ops work. Here's how we pick between Datadog and the Grafana stack without regretting it later.
Both promise to find your slow query at 3am. One bills by data ingested, the other by host-hour. Here's how that shakes out in a real ops budget.
We've run both in production for years. Here's the honest split on pricing traps, tracing quality, and which one you actually want for your team size.
We ran both across a few hundred services. One wins on auto-instrumentation and root-cause, the other on breadth and time-to-first-dashboard. Here's the split.
Most SLI dashboards track things nobody notices. Here's how we picked the handful of signals that map to real user pain, and dropped the vanity metrics.
The dashboard said the database was fine. It wasn't. Here's how pg_stat_statements found the query eating 40% of our Postgres CPU.
Free memory is a lie and load average doesn't see memory stalls. How Pressure Stall Information gives you a direct, early signal of memory contention — and how we wired it into alerts and autoscaling.
Cause-based alerts page you for things that don't matter and miss things that do. How we rebuilt alerting around SLO burn rates — multi-window, multi-burn-rate — and cut pages while catching more real pain.
The "three pillars" framing misses the point — what matters is correlating across them. The patterns that earn their place and the tooling decisions that pay back.
