Short version: when you join an early-stage company, there's a good chance you're staring at a system with no observability at all. No dashboards, no alerts, maybe a few stray print statements in the logs. I've walked into this more than once, and I've stopped trying to boil the ocean on day one.
The goal early on is simple. Get just enough visibility to answer "what is happening right now" without building a monitoring empire nobody asked for.
There are options to throw money at the problem, like buying a platform, but let's focus on the basics.
Start With Logs
If you do one thing first, make your logs structured. Trade the print statements for real, structured output so you can actually search and filter them later. This alone solves most of your day-to-day debugging, and it costs almost nothing to set up except your time.
While you're at it, give each log some context. A request ID, a user ID, the thing you were working on. A flat message tells you something happened. A few fields tell you what, to whom, and where to look next.
Keep the noise down, too. Log what you'd actually want to see at 2 AM, and skip the rest. Every line you write is a line you pay to store and read later.
Then a Handful of Metrics
You don't need a hundred metrics. You need the few that tell you the product is healthy. Latency, error rates, and whatever "throughput" means for your business will carry you a long way.
Measure what you'll actually look at. Everything else is just a bill waiting to happen.
Traces Can Wait
Distributed tracing is great (arguably required) once you have a bunch of services that need to be stitched together. Early on, with a monolith or a couple of services, good logs and a few metrics are plenty to start. I'd add traces when you're moving past an MVP and start caring about where the time goes across the system.
The one exception is if you're beginning a project on microservices from day one. Then tracing earns its place right away, because without it you're left guessing which service in the chain is the slow one. Otherwise, start simple and let tracing come later.
Pick Tools You'll Actually Use
I'm not going to name products here, because the best observability stack is the one your team will actually use. A fancy platform nobody looks at is worse than a plain one everybody does, and I've been there. Favor something you can query quickly, manage as code, and grow into.
Alerts That Don't Cry Wolf
The fastest way to get a team to ignore alerts is to send too many. So keep one rule above all the others. Every alert has to be actionable, with maybe an exception for a threshold warning on your most critical services. If nothing happens when it fires, delete it.
Lean toward alerting on the things your users actually feel, like errors and slow requests, rather than every internal number that twitches. And when one does fire, it should say what to do next, not just that something is wrong. A link to the right dashboard or a one-line runbook saves you at 3 AM.
A few habits that keep alerts honest:
- Use severity levels. Not everything is worth waking someone up for.
- Set thresholds that match reality, not a textbook. Review the historical data.
- Review them every so often. Remove the ones that never fire and fix the ones that fire constantly.
Getting Buy-In
The hard part was never the tooling. It's convincing the team to spend time on this when there are features to ship. What works for me is making it concrete. "Last outage took four hours to track down. With decent logging it's twenty minutes."
And these days that number keeps dropping. Point a tool like Claude Code or Codex at good structured logs and it will sift through them far faster than you'll ever scroll.
Observability isn't a project you'll finish. It's something you'll keep iterating on. Start small, keep what earns its place, and let it grow with you.