Catch it before
it catches fire.
TierZero actively scans for reliability risks, performance degradation, and creeping observability costs that no alert would catch.
Watches what dashboards can't.
Surfaces slow degradation patterns that slip past threshold-based alerts and go unnoticed until something breaks.
Slow degradation detection
Catch latency creep and memory leaks before they trigger alerts.
Cross-service correlation
Individual metrics look fine. Together, they tell a different story.
Historical trend analysis
Compare against baselines from weeks ago, not just hours.
Heap usage growing linearly since deploy v3.8.2. At current rate, OOM kill expected within 4 days. Likely cause: unclosed DB connections in the session refresh path.
P99 latency drifting upward since Jan 28. Trace analysis shows increased time in inventory-check span. Correlated with 18% growth in catalog size — query is not paginated.
Intermittent 503s from Elasticsearch cluster. Node es-data-03 showing elevated GC pauses. Pattern matches pre-incident behavior from INC-892.
Daily compute spend broke out of the normal band 3 days ago after deployment v4.2.1 modified the auto-scaling policy. The new minimum instance count of 8 (previously 3) is running during off-peak hours when traffic doesn't justify it. Projected monthly overspend: $189K if uncorrected.
P99 latency jumped from 120ms to 340ms starting Jan 28. Trace analysis shows the regression is concentrated in the validate-payment span, where a new N+1 query was introduced. Each transaction now issues 12-15 individual DB lookups instead of a single batched query. No alert fired because P50 remains within SLO.
429 rate on api-gateway climbed from 0.1% to 2.4% over the past 5 days. A single tenant (org_8f3a) is responsible for 78% of the throttled requests. Their integration webhook is retrying on 429s without backoff, creating a feedback loop that's crowding out other tenants.
Finds the problem hiding in plain sight.
Detects unusual spend spikes, latency creep, and rising error rates before they compound into outages. Each anomaly comes with context and a suggested next step.
Cost anomalies
Catch unexpected spend increases before they hit your cloud bill.
Performance regression
Surface latency trends and throughput drops — with the commit that caused them.
Error rate analysis
Track error patterns and correlate with deployments and infrastructure changes.
