The shared stack
Prometheus, Loki, Grafana, and Alertmanager deployed as platform services, with retention, resource sizing, and multi-team access designed in.
Services
Observability & Incident Readiness
The tools exist, but incidents still involve guesswork, noisy alerts, missing dashboards, or unclear ownership.
Metrics, logs, dashboards, and alerts shaped around incident response. The goal is knowing what is happening and what to do next, which more signals rarely deliver on their own.
Observability usually fails in one of two ways: not enough signal, or so much signal nobody trusts it. We build the shared stack, then tune the useful part: alerts that mean something, dashboards responders actually open, and service-level patterns teams can own.
Scope
Where the work has to hold.
Every engagement is scoped to the pressure in front of you. These are the areas we usually need to make reliable for the change to stick.
Signals worth waking for
Alert design with intent: page on user-facing symptoms, ticket the rest, delete the noise. Runbooks linked from the alert, not a wiki search away.
Team-owned instrumentation
ServiceMonitors, per-service dashboards, and service-level alerts that application teams create and maintain themselves, with patterns to copy.
Typical observability model
An observability engagement usually combines the shared stack, the service-level signals teams own, and the alerting structure people rely on during incidents.
response cues
Incident visibility
Alert RoutingLog CorrelationRunbook LinksSignal Review
team-owned signals
Service observability
ServiceMonitors
Custom Dashboards
Custom Alerts
Instrumentation Guidance
platform signals
Shared stack
Prometheus
Loki
Grafana
Alertmanager
Engagement shape
The stack, the signals, or both.
Some teams need the stack stood up properly. Others have the stack and need the signal quality fixed.
Stack rollout
Deploy and size the metrics, logging, and alerting stack as part of the platform, replacing disconnected per-team tooling.
Alert quality reset
Audit what pages, why, and what responders actually do with it, then rebuild the alert set around symptoms and runbooks.
Instrumentation enablement
Give application teams the patterns and defaults to own their service dashboards and alerts confidently.
Outcomes we are aiming for
01
A platform observability baseline: cluster, node, and workload signals with dashboards from day one
02
Alerts that page on symptoms, with runbooks linked where responders need them
03
Teams that own their ServiceMonitors, dashboards, and alerts instead of filing tickets for them
From the blog
See how we think before you talk to us.
Deep dives from the engineering blog covering the tools and patterns this service is built on.
Observability
Kubernetes monitoring with Grafana: dashboards and alerts
Grafana installation, initial dashboard setup, along with some fundamentals and best practices.
5 min read
Observability
Kubernetes cost allocation with OpenCost
Kubernetes cost allocation with OpenCost: install it with Helm, query per-namespace showback from the Allocation API, and account for idle spend honestly.
8 min read
Kubernetes
KEDA on Kubernetes: event-driven autoscaling to zero
Use KEDA on Kubernetes to scale from queue depth, lag, and schedules, and understand when scale to zero is worth the cold-start trade-off.
9 min read
Start with the problem
Start with your last incident.
What alerted, what should have, and what you had to go digging for. That story usually scopes the work by itself.