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Iogenstack

Production visibility

Observability, APM& site reliability

Metrics, logs and traces that explain what your systems are doing — with SLOs, actionable alerts and incident practices that help your team respond with confidence.

Discuss your observability What's included

Built with

checkout-apip95 latency · live● Healthy128 msSLO 300 msSERVICE MAP2.4k rpsgatewaycheckoutPostgreSQLpostgresRedisredisInstrumented withmetrics · logs · tracesPrometheusGrafanaOpenTelemetryDatadogAmazon CloudWatchAvailability SLO · 30d99.95%Error budget 72% leftAll monitors green12 checks · 0 firingTrace · GET /checkout64 msgateway64 mscheckout56 msredis GET2 msSELECT orders9 msOpenTelemetry · 1 in 10 sampled

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Animated illustration: Observability, APM & Site Reliability Engineering

Platforms & tools

Overview

Observability, APM & Site Reliability Engineering

We improve production visibility, detect application issues, investigate performance bottlenecks and increase system reliability through modern observability and site reliability engineering practices.

The problem we solve

When production slows down or fails, teams often find out from customers. Logs are scattered, dashboards don't answer the real question and alerts are either missing or so noisy they get ignored.

How we work

We instrument applications with OpenTelemetry, centralise logs, metrics and traces in your chosen platform, define service-level indicators and objectives with your team, and tune alerts around user impact.

What you get

Faster detection and diagnosis of production issues, a shared view of reliability through SLOs, alerts that mean something and a clear process for incidents and root-cause analysis.

Live preview

Know what production is doing.

Latency, errors, traces and SLOs for each service — and a clear trail from alert to root cause. This is an interactive sample with demo data.

Si

Signal Board

sre / checkout-service

Demo feedsample data
service map · OpenTelemetry● healthy

SLO · availability

99.95%

30-day SLI

  • target 99.9%
  • window 30d rolling
  • burn 0.6×
Error budget remaining64%

≈ 27m 39s of downtime left this window

Requests / s

▲ 8.2%

2,430

Error rate

▼ 0.3pt

0.12%

p95 latency

▼ 22.0%

169ms

Apdex

▲ 0.0

0.97

Latency percentiles (ms)

p50 p95 p99
−60m−45m−30m−15mnow

Distributed trace

4bf92f35… · 200ms
  • POST /checkout
  • checkout.handle
  • redis GET cart
  • SELECT orders
  • produce order.created
  • payments.authorize

6 spans · 5 servicescritical path: payments

Incident INC-2041

resolved

  1. Alert fired+0s

    SLO burn 14× · p95 > 300ms

  2. On-call paged+3s

    PagerDuty · ack in 41s

  3. RCA from traces+1m 48s

    slow SELECT on orders (no index)

  4. Auto-remediated+2m 10s

    runbook: add read replica

  5. Resolved+4m 32s

    burn rate back to 0.6×

logs · service=checkout tail -f

  • INFOPOST /checkout 200 182ms trace=4bf9…
  • INFOGET /cart 200 23ms
  • INFOauthorize ok amount=89.00 EUR
  • DEBUGcache hit cart:7f21 ratio=0.94
  • INFOproduced order.created partition=3
  • INFOPOST /checkout 200 171ms
SLI · SLO · SLA

Reliability you can measure

We measure what users feel (SLIs), set internal targets stricter than your customer promises (SLOs vs SLAs), and automate the response when the error budget starts to burn.

measured bystricter thanSLIWhat we measureAvailability99.97%p95 latency182 msError rate0.03%SLOInternal target99.9% availabilityp95 < 300 ms · 30-day windowError budget72% leftBurn rate 1.0×SLACustomer commitment99.5% monthlyContract with customersSafety margin vs SLO0.4 ptsCredits if breachedAvailability · 30-day windowsafety marginSLO 99.9%SLA 99.5%30 days agonowIncident responseNo alerts · budget healthyPythonauto_remediate.py› rollback(v2.4.1)› scale_out(+3 pods)armed · watchingOutcomeSLO on trackSLA safePrometheusGrafanaOpenTelemetryPython

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SLI
Service-level indicator — what we measure, e.g. successful requests or p95 latency.
SLO
Service-level objective — your internal target, e.g. 99.9% over 30 days.
SLA
Service-level agreement — the commitment to customers, kept looser than the SLO.
Capabilities

What's included

Visibility across applications and infrastructure, tied to reliability goals.

  • Application performance monitoring

    Request rates, latency and errors for every service, with drill-down into slow endpoints.

  • Logs, metrics & distributed tracing

    Centralised telemetry so you can follow a request across services and correlate signals.

  • OpenTelemetry instrumentation

    Vendor-neutral instrumentation for traces, metrics and logs across your languages and frameworks.

  • Datadog, Prometheus & Grafana

    Setup and integration of the observability platform that suits your team and budget.

  • CloudWatch, Azure Monitor & Google Cloud Observability

    Making the most of cloud-native monitoring for managed services and infrastructure.

  • API latency & error-rate analysis

    Finding the endpoints, queries and dependencies behind slow or failing requests.

  • Alerting, incidents & root-cause analysis

    Actionable alerts routed through PagerDuty or Opsgenie, incident runbooks and blameless post-incident reviews.

  • SLIs, SLOs & SLA reporting

    Service-level indicators measured from real traffic, objectives agreed with your team, error budgets, and reporting against the SLAs you commit to customers.

  • Automated remediation

    Python and runbook automation that handles known failure modes — restarts, scale-outs, rollbacks — before they become incidents.

  • Performance optimisation

    Infrastructure and application tuning guided by real production data.

Typical engagements

Where teams usually start

Common ways teams work with us on observability and reliability.

  • Observability foundation

    Instrumenting core services and centralising logs, metrics and traces in one place.

  • OpenTelemetry rollout

    Adopting OpenTelemetry so telemetry is portable across tools and vendors.

  • Dashboards that answer questions

    Service and business dashboards built around the questions on-call engineers actually ask.

  • Latency investigation

    Tracing slow API paths to their root cause and recommending targeted fixes.

  • SLOs & error budgets

    Workshops to define SLIs and SLOs, then reporting that tracks them over time.

  • Alert & incident tune-up

    Reducing alert noise and introducing runbooks and post-incident reviews.

FAQ

Questions about Observability & SRE

Which observability tools do you work with?
We work with OpenTelemetry, Datadog, Prometheus and Grafana, and with AWS CloudWatch, Azure Monitor and Google Cloud Observability.
What is the difference between monitoring and observability?
Monitoring tells you when known things go wrong. Observability — correlated metrics, logs and traces — helps you understand why, including problems you didn't anticipate.
What is the difference between an SLI, an SLO and an SLA?
An SLI is what you measure (for example, the share of successful requests). An SLO is your internal target for it (99.9% over 30 days). An SLA is the commitment you make to customers, usually looser than the SLO, so you are alerted and can act before it is at risk.
Do you provide 24/7 on-call coverage?
No. We help your team set up alerting, on-call processes, runbooks and incident reviews so you can respond effectively.

Ready to see what production is really doing?

Tell us about your stack and your biggest blind spots, and we'll propose an observability plan.

Discuss your observability