Catalog
concept#Platform#Cloud#Observability#Reliability

Hybrid Operations

An operational model for consistent, SLO-driven operations across cloud, hosted and on-prem infrastructure.

Hybrid Operations connects operations across cloud, hosted and on-prem infrastructure, establishing consistent platform processes and SLO-driven reliability.
Emerging
High

Classification

  • High
  • Organizational
  • Architectural
  • Intermediate

Technical context

GitOps tools (e.g. Argo CD)Service mesh (e.g. Istio)Observability stacks (Prometheus, OpenTelemetry)

Principles & goals

Unified platform APIs across environmentsSLO-centered operations controlSeparation of control plane and data plane
Run
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Vendor lock-in from proprietary integrations
  • Misconfigured security zones due to unclear policies
  • Unexpected costs from incorrectly placed workloads
  • Standardize interfaces and deployment artifacts
  • Use SLOs to prioritize operational actions
  • Establish unified observability pipelines across all environments

I/O & resources

  • Defined SLOs and error budgets
  • Platform APIs and automation tooling
  • End-to-end observability (metrics, traces, logs)
  • Consistent operational processes across environments
  • Measurable availability and error budget reports
  • Documented runbooks and audit trails

Description

Hybrid Operations connects operations across cloud, hosted and on-prem infrastructure, establishing consistent platform processes and SLO-driven reliability. It combines platform architecture, observability and integrated deployment and runbook workflows. Organizations use Hybrid Operations to balance operational cost, resilience and regulatory constraints across heterogeneous environments.

✔

  • Increased resilience via cross-environment redundancy
  • Improved regulatory compliance through targeted data residency
  • More flexible cost management through workload placement

✖

  • Increased operational complexity and troubleshooting
  • Network and latency dependencies between environments
  • Potential tool and data inconsistencies without clear governance

  • SLO attainment rate

    Share of time service level objectives are met.

  • Mean time to recovery (MTTR)

    Average time to restore service after an outage.

  • Cross-environment deployment success rate

    Share of successful deployments across involved environments.

Hybrid deployment with GitOps and Argo CD

Argo CD drives synchronized deployments across multiple clusters (cloud + on‑prem) and enables unified release pipelines.

Service mesh for cross-cluster communication

A service mesh provides consistent routing, security and observability policies across different environments.

Policy-driven data localization

Data is automatically kept in appropriate regions or on‑prem systems based on policies to ensure compliance.

1

Analyze current infrastructure and data classification.

2

Define SLOs, policy baselines and network requirements.

3

Introduce a platform layer with unified APIs and observability.

4

Automate deployments and runbooks for cross-environment workflows.

⚠️ Technical debt & bottlenecks

  • Old monolithic components lacking cloud readiness
  • Patchwork integrations instead of stable APIs
  • Incomplete automation of critical operational workflows
Network latency between environmentsVisibility across heterogeneous observability stacksDivergent authentication and policy systems
  • Simply copying cloud configs to on‑prem without adaptation
  • No clear SLOs; all incidents treated equally
  • Excessive centralization that restricts local resilience
  • Underestimating network complexity
  • Missing automation for cross-environment deployments
  • Inconsistent monitoring metrics across systems
Platform architecture and multi-cloud experienceSRE/operations and SLO managementNetwork and security knowledge for hybrid topologies
Scalability across multiple locationsNetwork and data locality requirementsSLO and error-budget driven operational goals
  • • Regulatory requirements for data residency
  • • Limited network bandwidth between sites
  • • Existing legacy systems with limited automation