Catalog
concept#Observability#Reliability#DevOps#Integration#Platform

Application Operations

Operational and organizational principles for running applications in production with focus on stability, scalability and observability.

Application Operations defines the organizational and technical practices for running modern applications in production, covering deployment, monitoring, incident response, scaling, configuration management, and developer-operations collaboration.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Monitoring tools (e.g. Prometheus)Orchestrators (e.g. Kubernetes)CI/CD systems (e.g. GitHub Actions, GitLab CI)

Principles & goals

Automate repetitive operationsMeasure through reliable telemetryFast feedback and learning cycles
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Excess complexity from too many tools
  • False alerts lead to alert fatigue
  • Dependency on single platform components
  • Define and measure SLOs and SLIs
  • Avoid alert noise with well-tuned rules
  • Automate rollbacks and emergency actions

I/O & resources

  • Telemetry (logs, metrics, traces)
  • Automated CI/CD pipelines
  • Runbooks and operational playbooks
  • Stable releases and rollbacks
  • Incident reports and improvement actions
  • Capacity and cost reports

Description

Application Operations defines the organizational and technical practices for running modern applications in production, covering deployment, monitoring, incident response, scaling, configuration management, and developer-operations collaboration. The focus is on stable availability, fast recovery, and continuous runtime optimization. It is closely aligned with observability and reliability.

✔

  • Higher availability and stability
  • Faster incident response
  • Improved cost and capacity control

✖

  • Requires investment in automation and observability
  • Limits with legacy systems lacking telemetry
  • Coordination overhead between teams

  • Mean Time to Recovery (MTTR)

    Average time to recover after an incident.

  • Error rate

    Share of failed requests or transactions over time.

  • System utilization / capacity utilization

    Measurement of resource usage for scaling decisions.

Using observability with Prometheus

Prometheus collects metrics used for alerting and capacity planning.

Canary deployment on Kubernetes

Canary strategy reduces release risk by gradual rollouts.

Incident postmortem with runbook updates

Postmortems improve response processes and result in concrete runbook updates.

1

Introduce telemetry and monitoring instrumentation

2

Implement CI/CD pipelines with canary or blue/green strategies

3

Define runbooks, SLAs and escalation processes

4

Implement automation for repetitive operational tasks

5

Establish continuous monitoring and postmortems

⚠️ Technical debt & bottlenecks

  • Uninstrumented legacy components
  • Manual deployments and ad-hoc scripts
  • Monolithic components without scaling strategy
Monitoring latencyDeployment durationCross-team coordination
  • Alarms without metric context
  • Manual scaling instead of automatic rules
  • Deploying without canary tests in critical environments
  • Blind trust in default alerts
  • Insufficient data retention for postmortems
  • Missing ownership for operational processes
Knowledge in observability and monitoringExperience with CI/CD and deployment strategiesIncident response and troubleshooting skills
Availability and resilienceObservability and telemetryAutomatability of deployments
  • • Legacy systems without telemetry access
  • • Budget and operational limits
  • • Compliance and security requirements