OpenTelemetry & observability stack (metrics • logs • traces)
We standardize signals and speed up diagnosis: metrics, logs and traces in a single data model, the Collector for routing, semantic attributes and intentional sampling. Result: less noise, faster RCA and clear SLOs.
One data model
OTLP for metrics, logs and traces — easier correlation and less vendor lock-in.
Governance & control
Sampling, cardinality limits and retention policies keep storage costs in check.
End-to-end tracing
Request path across microservices + user and release context.
Success criteria
SLIs, SLO targets and error budgets — data-informed product decisions.
What we deliver with OpenTelemetry
From instrumentation to operations: consistent attributes, the Collector, signal correlation and SLO dashboards. We start with explainable methods and quick wins.
Application instrumentation
SDKs and auto-instrumentation for popular languages. Shared attribute and tag conventions (e.g., service.name, http.target, db.system) so correlations are meaningful.
Collector & routing
Central Collector: batching, filtering, enrichment, head/tail sampling. Route to multiple backends without touching app code.
Metrics, logs & traces
One standard carries three signals. Link traces to metrics (exemplars) and tie events to releases and feature flags.
Dashboards & alerting
SLO dashboards, error budget burn-down, thresholds with seasonality. Incident priority by user impact.
Cost control
Cardinality reduction, trace sampling, retention policies and compression — cost visibility across ingest/retention/query stages.
Integrations
Works with Prometheus, Grafana, Jaeger and the OpenTelemetry Docs.
7 building blocks of effective observability
1. Semantic attributes
Unified naming and tags enable cross-service correlations and reporting.
2. Intentional sampling
Head/tail sampling with conditions (errors, high latency) — savings without losing signal value.
3. Correlation
Join traces with metrics/logs, link to deploys and feature flags.
4. SLIs/SLOs
Quality contracts for services, error budgets and release decisions.
5. Cost governance
Cardinality limits, per-signal retention and query cost monitoring.
6. Security
RBAC, PII masking, TLS/OTLP, access auditing and compliance.
7. Operability
Runbooks, on-call, post-mortems and continuous threshold tuning.
Implementation plan (7–14 day pilot)
Fast impact and a foundation you can scale. Iterative delivery with transparent trade-offs.
Discovery
Service & signal map, priorities and SLO goals. Pilot scope and risks.
Instrumentation
SDK/auto-instrumentation, attributes and the Collector. Baseline dashboards.
Correlation & alerts
Signal joins, thresholds and seasonality. Alerts routed to the right queues.
Report & roadmap
Impact, costs, retention & sampling recommendations. Scale-up plan.
How we measure success
Shorter time-to-diagnose, fewer escalations, lower MTTR and reduced storage costs. Reports align outcomes to SLO targets, while error budgets guide release decisions.
Further reading: OpenTelemetry Docs · Prometheus Docs · Grafana Docs · Jaeger Docs
See also: AIOps: anomaly detection, correlation & RCA · API Integrations
FAQ — quick answers
Do we need to migrate current dashboards and agents?
How do you control data costs?
On-prem or cloud?
How long is the pilot and what do we get?
Want consistent observability without lock-in?
Free 20-minute consultation — we’ll review your signals, outline a pilot and show quick wins.