Uptime tells you whether your system is running.
Reliability tells you whether users can actually get things done.
At scale, it's common to see dashboards glowing green — 99.99% uptime, healthy services — while users are:
- failing to check out,
- stuck on login,
- receiving incorrect or partial data.
The problem isn’t bad luck.
The problem is that availability is not reliability.
Uptime is binary. Reliability is not.
Uptime / Availability answers:
Is the service responding?Reliability answers:
Can users successfully complete critical journeys under real-world conditions — latency, load, and failure?
A 200 OK response doesn’t guarantee:
- correct results,
- acceptable latency,
- a successful end-to-end workflow.
Why availability breaks down at scale
1) Partial failures are the norm
Large systems rarely fail all at once. Instead:
- one region degrades,
- one dependency times out,
- one tenant or user segment is affected.
Availability stays “up”.
User experience quietly collapses.
2) Averages hide real user pain
Users don’t experience averages.
They experience:
- p95 / p99 latency,
- long-tail timeouts,
- retry storms at the worst possible moments.
A green dashboard doesn’t mean a reliable product.
3) Degradation doesn’t show up in uptime metrics
Most incidents look like:
- exhausted connection pools,
- growing queues,
- cache thrashing,
- slow downstream dependencies.
All while uptime happily reports 100%.
4) Failures are inevitable — recovery is what matters
At scale, the question isn’t “Will the system fail?”
It’s “How quickly can we detect, contain, and recover?”
Reliability is about resilience, not perfection.
What to measure instead of just uptime
SLIs: measure outcomes, not just liveness
Focus on real user outcomes:
- success rate of critical user journeys (login, checkout, payment)
- p95 / p99 latency
- correctness signals (a 200 OK with wrong data is still a failure)
SLOs: define what “good enough” means
SLOs make reliability measurable:
- what percentage of requests must succeed?
- how slow is too slow?
Error budgets & burn rates: manage reliability like a product
- Error budgets allow controlled failure.
- Burn rates tell you when reliability risk is becoming urgent.
If you can’t tell whether users are succeeding within 30 seconds, you’re measuring infrastructure — not reliability.
Reliability gaps often stay hidden
Many of the failures that hurt users the most:
- don’t trigger alerts,
- don’t violate SLAs,
- don’t look like “incidents”.
They live in the seams between services and across end-to-end journeys.
A good way to surface these blind spots is to periodically step back and review:
- what you think you’re measuring vs. what users actually experience,
- whether SLOs map to real journeys,
- which failure modes are currently “silent”.
If you want a structured checklist for that kind of review, here’s a reliability audit workflow you can use:
OptyxStack Reliability Audit
Building for real reliability
Reliable systems usually share a few traits:
- SLOs tied to user journeys, not hosts or pods
- deployments gated by error budgets
- failure-aware design:
- explicit timeouts
- retries with jitter
- circuit breakers
- bulkhead isolation
- blameless postmortems focused on eliminating repeat failure modes
- observability deep enough to answer: > Are users succeeding right now?
Conclusion: Stop chasing uptime
Uptime tells you your system is alive.
Reliability tells you your system is working for users.
If your dashboards are green but incidents keep happening, you’re not unlucky — you’re measuring the wrong thing.
Are you measuring uptime, or are you measuring reliability?
Curious to hear how others approach this in production 👇
Top comments (1)
Nice a day!