Monitoring & Observability

Monitoring tells you whether a known condition is happening, using predefined metrics, dashboards, and alerts. Observability is the broader ability to ask new questions of your systems and understand why something is happening, by correlating metrics, logs, and traces. We build both: solid monitoring for the failure modes you expect, and rich observability for the ones you do not.

Metrics, logs, and traces. Metrics give you aggregated, low-cost signals over time; logs give you detailed, event-level context; and distributed traces show how a single request flows across services. We instrument all three and correlate them so you can pivot from a spiking metric straight to the exact log line and trace that explains it.

An SLI (Service Level Indicator) is a measured signal such as request success rate or p95 latency. An SLO (Service Level Objective) is the target you commit to for that indicator, for example 99.9% of requests served under 300ms. The error budget is the small amount of allowed failure below that target, which lets teams balance shipping features against protecting reliability.

We are tool-agnostic and work with your existing stack or recommend a fit. Common choices include Prometheus and Grafana, OpenTelemetry, Datadog, New Relic, Grafana Loki and Tempo, the ELK stack and OpenSearch, Jaeger, and native services like Amazon CloudWatch, Azure Monitor, and Google Cloud Operations. OpenTelemetry keeps instrumentation vendor-neutral so you avoid lock-in.

We alert on symptoms that affect users, not on every raw metric, and we tie thresholds to SLOs and error-budget burn rates rather than arbitrary numbers. We deduplicate and group related alerts, add clear runbooks and severity levels, and route through escalation policies so the right on-call engineer is paged with actionable context and noise is suppressed.

Yes. We unify telemetry from AWS, Azure, Google Cloud, Kubernetes, and on-premises systems into a single pane of glass. Using OpenTelemetry collectors and a central backend, you get consistent metrics, logs, and traces across every environment instead of separate, disconnected consoles per provider.

When metrics, logs, and traces are correlated and alerts carry context, teams detect issues sooner (lower MTTD) and diagnose them faster (lower MTTR). Engineers move directly from an alert to the offending service, deployment, or query instead of guessing. We also support blameless post-incident reviews with the data needed to prevent recurrence.

A focused rollout for a single application or service typically takes 2-4 weeks, covering instrumentation, dashboards, and core alerts. A broader multi-service or multi-cloud platform with SLOs and mature on-call practices usually takes 6-10 weeks depending on scope. We deliver incrementally so you gain visibility early and refine as we go.