Logging & Tracing Services

We build centralised logging and distributed tracing stacks, ELK, Grafana Loki, and OpenTelemetry, so every engineer can search any log from any service in a browser, and trace any slow request across every microservice it touched.

Logging is often under-invested until an incident exposes the gaps. We fix the fundamentals: structured logs, sensible retention, trace IDs in every log line, and alerts on what matters.

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

Logs are only accessible by SSHing into servers

Centralised logging means every team member can query logs from any service in a browser, no SSH, no server access required.

Log volumes drive huge costs in managed services

We implement structured logging, log sampling, and routing policies so high-volume debug logs do not cost as much as critical errors.

No correlation between logs and traces

We embed trace IDs in log lines and configure Grafana to correlate Loki logs with Tempo traces so you can jump from a log line to the full request trace.

ELK stack is slow and expensive to operate

For some teams, Loki plus Grafana can meet the same goals at lower cost and operational complexity, depending on workload. We help you evaluate the trade-offs.

No distributed tracing

Without traces, debugging a slow request across many microservices means searching logs in multiple places. OpenTelemetry adds traces through SDK instrumentation.

Unstructured logs are impossible to query

We help teams move to structured JSON logging with consistent field names, making log queries in Kibana or Grafana easier.

What's included

ELK Stack deployment (Elasticsearch, Logstash/Beats, Kibana) or Grafana Loki FluentBit or Logstash agent deployment as DaemonSet on Kubernetes Log routing by severity, namespace, or service to different storage tiers Kibana or Grafana dashboards for log analysis and error rate tracking Log retention policies and cost-optimised index lifecycle management (ILM) Structured logging guidelines and team onboarding OpenTelemetry SDK instrumentation for distributed tracing (Go, Python, Node, Java) Trace storage with Grafana Tempo or Jaeger Correlation: trace IDs in log lines, Grafana Explore cross-navigation Log-based alerting (Kibana Watcher or Grafana alerting) Audit log collection to support compliance (CloudTrail, Azure Monitor) Log scrubbing for personal data (PII) in log lines

Our process

01

Logging and tracing audit

We assess what is currently being logged, where it goes, how long it is retained, and what is missing. We document unstructured logging hotspots and tracing blind spots.
02

Stack selection and architecture

ELK vs Loki vs managed (Datadog Logs, CloudWatch) evaluated against your scale, cost tolerance, and team skills. Architecture documented with data flow diagrams.
03

Deploy and instrument

Log agents deployed, pipelines configured, OpenTelemetry SDKs integrated into services, and initial dashboards built from real traffic.
04

Alerting and compliance

Log-based alerts configured, ILM policies set for cost control, PII scrubbing implemented, and log archive retention checked against your requirements.

Tech stack

ElasticsearchKibanaLogstashFluentBitGrafana LokiGrafana TempoJaegerOpenTelemetryAWS CloudWatch LogsAzure Monitor

Technologies we use for this

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

ELK Stack Falco Grafana Grafana Loki OpenTelemetry

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.

Engagement models

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

Industries and locations

fintech saas ecommerce healthcare

FAQ

ELK Stack vs Grafana Loki, which should I use?

Loki is often cheaper and simpler to operate because it indexes labels, not log content. ELK full-text indexes log content, which suits free-text search but typically needs more storage and operational effort. Loki often fits well unless full-text search across large log volumes is critical.

What is OpenTelemetry?

OpenTelemetry is a CNCF project and vendor-neutral standard for telemetry collection. You instrument your code once with the OpenTelemetry SDK and can route metrics, logs, and traces to many backends, such as Grafana, Datadog or Jaeger, without re-instrumenting application code.

How do you handle personal data in logs?

We configure log scrubbing pipelines to remove or hash PII fields (email, IP, user ID) before logs are written to storage. Audit logs requiring PII are stored in a separate, access-controlled index. Whether the result meets GDPR obligations is a question for your data protection lead or legal adviser.

What is distributed tracing?

Distributed tracing follows a single user request through every microservice it touches, timing each hop and showing where latency or errors occur. It makes debugging slow requests in a microservice architecture practical.

How much does centralised logging cost?

It depends on log volume, retention and storage choice. Self-hosted Loki with object storage is typically cheaper per GB than managed log services, but adds operational effort. Check current vendor pricing for a like-for-like comparison.

Can you migrate us from CloudWatch Logs to a self-hosted stack?

Yes. We can deploy Fluent Bit as a DaemonSet, route logs to Loki or Elasticsearch, replicate alerts from CloudWatch Alarms to AlertManager, and deprecate CloudWatch Log groups gradually.

Ready to see what your services are doing?

Book a free call. We will review your logging setup and recommend a cost-conscious path forward.