What is observability?
Observability is what lets an engineer answer a new question about production without deploying new code. Monitoring tells you that a known metric crossed a threshold. Observability lets you work out why checkout is slow in one region, which dependency adds latency, or which release changed the error pattern, by correlating metrics, logs and traces.
The goal is shorter incidents and fewer of them, measured in time to detect and time to recover. It is also a calmer on-call rotation: alerts that fire only when users are affected, dashboards that point toward the cause, and runbooks that tell the engineer on call what to do next.
Our scope runs from instrumentation in the code to the alert on an engineer’s phone. It covers telemetry collection, storage and retention, dashboards, SLOs, alert routing and incident review practice. It is one of the services of our DevOps consulting practice, and it feeds release decisions directly.
Signs you need better observability
Most teams already pay for monitoring tools. The problem is rarely a missing tool. It is data that does not answer the questions people actually ask during an incident, collected without a decision about what it is for.
Customers report outages before your alerts do
If support tickets arrive before pages, your alerts measure infrastructure health instead of what users experience.
On-call engineers mute alerts to get sleep
Alert fatigue comes from thresholds on causes, such as CPU, rather than on symptoms. Real problems then hide among false alarms.
Incidents start with an hour of log searching
Without traces and consistent request identifiers, engineers search logs service by service to rebuild what a single request did.
Every team uses a different tool and format
Metrics in one product, logs in another, traces nowhere: correlating them during an incident depends on who happens to be on call.
The telemetry bill grows faster than traffic
Unbounded log volumes and high-cardinality metrics inflate costs. Retention and sampling were never decided, only inherited from defaults.
These signs reinforce each other: noisy alerts lead to muted alerts, which lead to outages reported by customers. Fixing alerting on its own rarely holds unless the telemetry underneath it is fixed at the same time.
What our observability engagement covers
We start from your last incidents rather than from a tool catalogue. For each one, we check what data existed, what was missing and how long diagnosis took. That review decides where new instrumentation pays off first, and which existing data can simply be dropped.
Telemetry audit and incident review
Existing metrics, logs, traces and alerts mapped per service, recent incidents replayed, and gaps ranked by time lost during diagnosis.
Instrumentation with OpenTelemetry
Services instrumented once with open standards, with consistent attributes and trace context, so data can go to the backend you choose.
SLOs and error budgets
Service level indicators chosen with product owners, objectives agreed, and error budget burn used to drive alerts and release decisions.
Alerting that leads to action
Symptom-based alerts on SLO burn, routed by ownership and linked to a runbook, with noisy and orphaned alerts removed.
Dashboards and a retention policy
Service dashboards built for incident use, plus retention, sampling and cardinality rules that keep costs proportional to value.
Delivery happens in your existing stack, whether it is Prometheus and Grafana, a managed cloud service or a commercial platform. We do not ask you to change vendor unless the current one blocks a requirement you have stated, such as retention or trace support.
Which alerts actually matter?
An observability audit typically takes 1 to 2 weeks and ends with an incident-based gap list and a prioritized plan.
How we build observability: four phases
We work service by service, starting with the one that causes the most incidents or the heaviest on-call load. Each phase is useful on its own, so value shows up in the first weeks rather than at the end.
PHASE 01
Review
Incidents, alerts, telemetry and on-call load analyzed per service.
PHASE 02
Instrument
OpenTelemetry, consistent attributes and traces across service boundaries.
PHASE 03
Define
SLIs, SLOs, error budgets and burn-rate alerts agreed.
PHASE 04
Operate
Runbooks, dashboards, incident reviews and on-call handover.
The last phase is where habits form. We join your incident reviews, refine alerts that fired without leading to action, and help the team retire dashboards nobody opens. After handover, SLO reports give leadership a plain view of reliability that does not depend on reading graphs.
Which signal answers which question?
Metrics, logs and traces are not interchangeable. Each answers a different question at a different cost, and a sound setup uses each one where it is strongest instead of logging everything and hoping search will be enough.
| Signal | Answers | Cost driver | Use it for |
|---|---|---|---|
| Metrics | Is something wrong, and how badly? | Number of label combinations | SLOs, alerting, capacity trends |
| Logs | What exactly happened in this component? | Volume and retention period | Error detail, audit events, debugging |
| Traces | Where did this request spend its time? | Sampling rate and span count | Latency across services, slow dependencies |
| Change events | What changed just before the problem? | Low, few records | Deploys, flag switches, config changes |
Change events are the most underused signal. Marking every deployment, feature flag switch and infrastructure change on the same timeline as your metrics often answers the first question of an incident straight away. Our guide to audit and traceability of infrastructure changes shows how to collect them reliably.
Risks of an observability overhaul
Observability projects tend to fail quietly: the data arrives but nobody uses it, or the bill arrives before the insight does. These are the risks we manage explicitly, with an owner for each one.
Cost growing faster than insight
We set retention, sampling and label rules before instrumenting widely, and review the bill per service so spending follows usefulness.
SLOs that nobody owns
An objective without an owner is just a number on a dashboard. Each SLO gets a product and an engineering owner and a review cadence.
Removing alerts that were quietly useful
Before deleting noisy alerts, we check their history against incidents, then log retired alerts for a trial period instead of paging.
Sensitive data in logs and traces
Personal data and secrets leak into telemetry easily. We add scrubbing at collection time and agree what may be recorded.
Good signals also make releases safer, because they give rollout tooling a reason to stop. Our guides on automated rollback driven by production signals and on observability and alerting in pipelines show how the same data protects deployments.
Observability pricing
Every engagement starts with a diagnosis of your telemetry and recent incidents, and ends with a fixed quote. Durations are typical, not promised, and scale mainly with the number of services to instrument.
| Format | Duration | Price |
|---|---|---|
| Observability audit | 1 to 2 weeks | Fixed quote after diagnosis |
| SLOs and alerting on core services | 6 to 12 weeks | Fixed quote after the assessment |
| Enablement support after handover | 30 days | Fixed quote after the assessment |
What changes the price: number of services and languages to instrument, current telemetry stack, data volumes and how many teams share on-call.
The quote covers senior consultants only, a defined set of services and the deliverables of each phase. Observability platform subscriptions, storage and data ingestion are billed by your vendor or cloud provider, and we estimate their impact before instrumentation widens.
Observability FAQ
What is the difference between monitoring and observability?
Monitoring checks known conditions, such as a full disk or a high error rate. Observability gives enough context, through correlated metrics, logs and traces, to investigate failures nobody predicted, without adding new code first.
Do we need to replace our monitoring vendor?
Usually not. Instrumenting with OpenTelemetry keeps data portable, so most improvements happen in instrumentation, SLOs and alert design. We suggest a change only when cost, retention or a missing capability becomes a real constraint.
How many SLOs should a service have?
Few. Two or three per user-facing service is a sensible start, typically availability and latency of the requests users care about most. More objectives dilute attention and make error budgets harder to act on.
Can you reduce alert noise without missing incidents?
Yes. We replace cause-based thresholds with alerts on SLO burn rate, check removed alerts against incident history, and log retired alerts for a trial period before deleting them for good.
Will tracing slow down our services?
Not noticeably when it is configured well. OpenTelemetry instrumentation is designed to be light, and head or tail sampling keeps the volume of stored traces proportional to what you actually need to debug.
Who writes the runbooks?
Your engineers, with us. A runbook is only trusted when the people on call helped write it. We provide the template, review each one against a real alert and keep it next to the alert definition.