What is observability with DevOpsArk?
Observability with DevOpsArk means metrics, logs and traces attached to one service model, so an engineer can follow a symptom to its cause across services, clusters and clouds without changing tools or losing context.
What this solution addresses
- Three signal types live in three systems with three identity models.
- Retention is too short to answer questions about last month.
- Alerting uses thresholds that were guessed at launch.
- Log volume makes the relevant line unfindable.
- Telemetry spend rises while diagnostic capability falls.
Shared identity is what makes cross-signal navigation possible.
Pattern extraction makes a brand-new error visible on first occurrence.
Correlated incidents replace individual threshold notifications.
Stage by stage, with the modules that deliver each one
Every stage links to the modules that implement it, so the path from outcome to capability is explicit.
Collect
Metrics, logs and traces ingested via OpenTelemetry and the common exporters, labelled with one identity model.
Correlate
Metric to trace to log and back, plus deployments and configuration changes on the same timeline.
Detect
Behavioural baselines instead of guessed thresholds, with related detections grouped into one finding.
Explain
Ark reads the patterns, correlates them with change, and presents the reasoning with the evidence attached.
Everything involved in this solution
Observability
Metrics, logs and traces in one plane
Monitoring
Infrastructure and application monitoring
Log Management
Centralised logs with structure and retention
AI Log Analysis
Find the line that matters
Alerting
Alerts that are worth waking up for
Anomaly Detection
Detection without hand-written thresholds
Observability: frequently asked questions
Observability is the property of a system that lets you understand its internal state from what it emits (metrics, logs and traces) including for failures nobody anticipated. Monitoring tells you something is wrong; observability lets you find out why.
Monitoring evaluates known signals against expected ranges. Observability lets you ask new questions of the system after the fact. Most teams need both, and the useful distinction is whether you can investigate something you did not predict.
No. DevOpsArk can read from existing Prometheus and Loki deployments and add cross-cluster aggregation, correlation, retention and analysis on top.
By managing cardinality deliberately (the labels driving cost are surfaced with their price), and by sampling on the tail so slow and failing traces are kept while routine successful traffic is not.
Some. OpenTelemetry instrumentation gives the best traces, but auto-instrumentation and service mesh telemetry provide useful coverage where changing code is not practical.
Background on this topic
What is observability, and how is it different from monitoring?
A definition of observability that does more work than "the three pillars": what property you are actually trying to obtain, and how to tell whether you have it.
Monitoring vs observability: a distinction worth keeping
The difference between monitoring and observability, why the distinction is more than marketing, and what each one is actually for.
Logs, metrics and traces: which signal answers which question
What each telemetry type is genuinely good at, what it costs, and how to decide where a given piece of information belongs.
AI log analysis: making millions of lines legible
How log pattern extraction works, why it finds errors that search never will, and how to use novelty and rate detection without generating a new source of noise.
Talk through observability for your estate
A 30-minute conversation with a platform engineer about what you have and what would actually change.