Monitoring & Observability Services

We build production observability stacks using Prometheus, Grafana, Loki, and OpenTelemetry. You get dashboards that answer real questions, alerts that only fire when action is needed, and full-stack visibility from infrastructure to application traces.

Observability is not just dashboards, it is the difference between finding out about an incident from your monitoring stack versus from a customer tweet.

Book a working session Request a quote

CollectMetrics, logs, traces
StoreTime series, logs
VisualiseDashboards
AlertRouted to on-call
ReviewTune after incidents

From signal to action.Telemetry becomes dashboards and alerts that reach someone who can act.

Pain points we fix

No visibility into system health

Without metrics and dashboards, you find out about problems from customers, not alerts. We build observability that gives you earlier awareness of problems.

Alerts fire constantly but nothing is actionable

Alert fatigue kills on-call effectiveness. We tune thresholds, add missing context, and eliminate noisy alerts that never need waking anyone up.

Grafana dashboards are outdated and unused

We redesign dashboards around the four golden signals (latency, traffic, errors, saturation) so they answer real operational questions.

No correlation between metrics, logs, and traces

We integrate Prometheus metrics, Loki logs, and Tempo/Jaeger traces into a unified observability stack with cross-linking.

Kubernetes cluster health is a mystery

We deploy kube-state-metrics, node-exporter, and pre-built Kubernetes dashboards so you see CPU, memory, pod restarts, and PVC usage at a glance.

No SLO tracking

We define SLIs and SLOs with Prometheus recording rules and Grafana panels so you always know your current error budget burn rate.

What's included

Prometheus deployment (or Prometheus Operator with Helm on Kubernetes) Grafana installation and datasource configuration Pre-built dashboards for Kubernetes, Node, JVM, PostgreSQL, and application metrics Custom application metrics instrumentation guidance (Go, Python, Node, Java) AlertManager configuration with severity routing and silence policies PagerDuty, OpsGenie, or Slack alert routing SLI/SLO definitions and error budget dashboards Loki log aggregation with Grafana Alloy or FluentBit agents Distributed tracing with OpenTelemetry and Tempo or Jaeger Long-term metric storage with Thanos or Mimir Grafana OnCall or on-call schedule integration Runbooks linked from alert annotations

Our process

01

Observability audit

We map your current monitoring, identify blind spots, review existing alert quality, and document which SLOs you are implicitly relying on but not measuring.
02

Stack design

We design the observability stack for your scale: Prometheus + Grafana for most teams, Thanos or Mimir for long-term storage at larger scale, and OpenTelemetry for trace instrumentation.
03

Deploy and instrument

Stack deployed, dashboards built, alerts written and tuned. Application teams get guidance on instrumenting custom metrics. Alert runbooks written in parallel.
04

SLOs and ongoing tuning

SLIs defined, SLO dashboards built, error budget alerts configured. We review alert quality after an initial period of production traffic and keep tuning to improve signal-to-noise.

Tech stack

PrometheusGrafanaAlertManagerLokiTempoJaegerOpenTelemetryThanosMimirkube-state-metricsnode-exporterFluentBit

Technologies we use for this

Each page explains how we run the tool in production and when we would pick something else.

Amazon EKS Azure AKS Datadog Google Cloud Google GKE Grafana Grafana Loki Istio Karpenter Kubernetes OpenTelemetry Prometheus

Who you will work with

Engagements are led by a named senior engineer, not passed to an anonymous team.

Saddique Khan

CEO, DevOps / SRE / Cloud Consultant Engineer

Meet the team

Results

We publish results only when a client has approved them. See how we publish case studies and a sample report.

Related sub-services

Grafana Dashboards Prometheus Alerting Kubernetes Monitoring

Engagement models

Project-based or ongoing managed operations. View all engagement models or get a quote.

Industries and locations

fintech saas ecommerce healthcare

FAQ

Prometheus vs Datadog, which should I use?

Prometheus is open source and integrates natively with Kubernetes. Datadog is a managed SaaS that is easier to start with, though costs can grow with scale. The right choice depends on team size, workload and budget.

What are the four golden signals?

Latency (how long requests take), traffic (how many requests per second), errors (rate of failed requests), and saturation (how full your system is, CPU, memory, queue depth). These four signals are a widely used starting point for monitoring.

What is an SLO and why does it matter?

A Service Level Objective is a reliability target, e.g. 99.9% of requests succeed within 500ms. SLOs translate reliability into a concrete error budget, making on-call and toil reduction decisions objective.

Can you integrate with our existing PagerDuty setup?

Yes. We configure AlertManager to route alerts to PagerDuty, OpsGenie, or Slack with severity-based routing, deduplication, and silences for maintenance windows.

What is OpenTelemetry?

OpenTelemetry is a vendor-neutral standard for collecting metrics, logs, and traces from applications. We use it to instrument services once and route telemetry to any backend, Grafana, Datadog, Jaeger, or others.

How do you handle monitoring for multiple environments?

Each environment gets its own Prometheus scrape targets with environment labels. Grafana datasources and dashboards are templated with environment variables so you switch context without rebuilding dashboards.

Ready to actually see what is happening?

Book a free call. We will review your current monitoring and highlight likely blind spots.