Production Readiness Checklist for a Cloud-Provider Agnostic Web Platform

What “Production Readiness” Means for a Cloud-Provider Agnostic Web Platform

Production readiness is the discipline of proving that your platform can be operated safely and predictably under real-world conditions: variable traffic, partial failures, routine upgrades, security incidents, and human mistakes. For a cloud-provider agnostic web platform, the bar is higher because you must avoid hidden dependencies on a single vendor’s load balancers, identity systems, managed certificates, DNS features, or proprietary observability agents. A production readiness checklist turns that goal into verifiable items that can be tested, reviewed, and continuously enforced through automation.

In this chapter, “cloud-provider agnostic” means you can run the same Kubernetes-based web platform on multiple environments (public clouds, private cloud, on-prem) with minimal changes. You may still use provider integrations, but they must be optional, replaceable, and documented. The checklist below is organized by operational outcomes: repeatable deployments, secure defaults, predictable networking, observability, incident response, and governance.

Checklist Structure: From Principles to Verifiable Controls

A useful checklist is not a list of aspirations; it is a set of controls that can be verified by code, tests, or audits. Each section below includes: what to verify, why it matters, and practical steps to implement it in a provider-neutral way. Treat each item as a gate in your release process: if it cannot be verified, it is not complete.

Platform Baseline and Cluster Standards

Define a “Supported Cluster Profile”

Verify: You have a documented Kubernetes version range, container runtime expectations, CNI requirements, and minimum node resources. Why: Multi-environment support fails when clusters drift in versions, CNI behavior, or feature gates. Steps:

• Publish a support matrix: Kubernetes versions (for example, N-2), required APIs (Ingress v1), and disallowed deprecated APIs.
• Standardize on a CNI behavior set (NetworkPolicy semantics, eBPF vs iptables expectations) and test it in CI using ephemeral clusters.
• Define baseline node sizing for ingress/data plane components and enforce via cluster provisioning templates (Terraform/Cluster API).

Namespace and Resource Segmentation

Verify: Clear separation between platform components, shared services, and application namespaces, with quotas and limits. Why: Noisy neighbors and accidental cross-team changes are common production failure modes. Steps:

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• Create namespaces for: ingress/edge, platform controllers, observability, and each application domain.
• Apply ResourceQuota and LimitRange per namespace to prevent runaway pods from exhausting cluster capacity.
• Use PodDisruptionBudgets for critical components (ingress controllers, gateways, DNS, cert managers) to keep availability during maintenance.

Configuration and Deployment Repeatability

GitOps or Equivalent “Single Source of Truth”

Verify: All Kubernetes manifests and platform configuration are versioned, reviewed, and applied automatically; manual kubectl changes are detected and reverted. Why: Provider-agnostic operations require reproducibility across clusters. Steps:

• Store manifests/Helm/Kustomize in Git with environment overlays (dev/stage/prod) and cluster overlays (cloudA/cloudB/onprem) only where unavoidable.
• Enforce pull-request reviews and policy checks (OPA Gatekeeper/Kyverno) before merge.
• Implement drift detection and reconciliation (GitOps controller) so clusters converge to the declared state.

Release Strategy and Rollback Plan

Verify: Every deployment has a defined rollout method and a tested rollback procedure that does not rely on provider-specific features. Why: Rollbacks are part of normal operations; if they are not rehearsed, they will fail during incidents. Steps:

• Choose a rollout approach per workload: rolling update, blue/green, or canary (implemented at the Kubernetes layer or via ingress routing rules).
• Keep immutable container images and tag by digest; avoid “latest”.
• Document rollback steps: revert Git commit, redeploy previous chart version, or switch traffic back to the stable service.

Environment Parity Without Vendor Lock-In

Verify: Differences between environments are intentional and minimal (for example, replica counts), not accidental (different ingress classes, different DNS behavior). Why: Provider-specific defaults often create “it works in staging” traps. Steps:

• Use the same ingress controller and configuration across environments; if you must vary, do so via explicit values files.
• Standardize on the same certificate management approach (for example, cert-manager) rather than cloud-managed certs that differ per provider.
• Run conformance tests after each environment change: ingress routing, TLS termination, health checks, and basic authz policies.

Ingress and Edge Readiness (Provider-Agnostic)

Ingress Controller Installation and Hardening

Verify: Ingress controller is installed with least privilege, pinned versions, and predictable behavior across clusters. Why: The ingress layer is a high-risk boundary: misconfigurations can expose services or break routing. Steps:

• Pin ingress controller version and chart version; upgrade via controlled change windows.
• Run ingress controller with a dedicated ServiceAccount and minimal RBAC permissions.
• Set resource requests/limits and horizontal scaling rules for the controller deployment.
• Disable insecure defaults (for example, avoid wildcard host routing unless required).

Standardize IngressClass and Routing Conventions

Verify: Each Ingress resource targets an explicit IngressClass and follows consistent host/path conventions. Why: Multi-controller clusters and migrations are common; ambiguity causes traffic to land in the wrong place. Steps:

• Define a standard IngressClass name (for example, edge) and require it via policy.
• Adopt a host naming convention that works across DNS providers (no provider-only features).
• Use explicit path types and avoid controller-specific annotations unless they are documented and tested.

External Exposure Model: LoadBalancer, NodePort, or Gateway

Verify: You can expose the platform in at least two environments (cloud and on-prem) using a documented pattern. Why: Cloud LoadBalancer services may not exist on-prem; on-prem may require MetalLB, BGP, or an external reverse proxy. Steps:

• Document supported exposure patterns: Service type LoadBalancer (cloud), MetalLB (on-prem), or external L4 load balancer to NodePort.
• Keep the ingress controller service definition abstracted via Helm values so the same chart can switch service type per environment.
• Validate source IP preservation requirements and configure proxy protocol or X-Forwarded-For handling consistently.

Health Probes and Edge Monitoring Endpoints

Verify: Liveness/readiness probes exist for ingress components and every web workload; edge health endpoints are reachable from monitoring systems. Why: Without probes, Kubernetes cannot self-heal; without edge checks, you miss routing/TLS failures. Steps:

• Ensure ingress controller has readiness gates that reflect config reload success.
• For each web service, define readiness that checks dependencies minimally (for example, local process + critical downstream connectivity if required).
• Expose a synthetic check endpoint (for example, /healthz) and monitor it externally from multiple regions/providers if possible.

DNS, Certificates, and Domain Management

DNS Portability and Record Strategy

Verify: DNS records can be moved between providers without changing application routing logic. Why: DNS is a frequent lock-in point (alias records, proprietary health checks). Steps:

• Prefer standard A/AAAA/CNAME records; avoid provider-only alias features unless you have a fallback.
• Use ExternalDNS with multiple provider backends if you need automation; keep provider credentials scoped to specific zones.
• Set TTLs intentionally: lower TTL for cutovers, higher TTL for stable records to reduce query load.

Certificate Lifecycle Automation

Verify: TLS certificates are issued, renewed, and rotated automatically in every environment with the same toolchain. Why: Manual certificate operations cause outages and security gaps. Steps:

• Use cert-manager with ACME (public) or internal CA (private) and store Issuer/ClusterIssuer definitions in Git.
• Define a standard secret naming convention and reference it consistently in Ingress resources.
• Test renewal in staging by shortening durations (where safe) and verifying ingress reload behavior.

Security Readiness Beyond Service-to-Service Controls

Pod Security and Workload Hardening

Verify: Workloads run with non-root users, read-only filesystems where possible, and restricted capabilities. Why: Web platforms are common targets; container escape and privilege misuse are mitigated by strong defaults. Steps:

• Adopt Kubernetes Pod Security Standards (restricted) via namespace labels and policy enforcement.
• Set securityContext: runAsNonRoot, drop Linux capabilities, and set seccompProfile to RuntimeDefault.
• Use separate service accounts per workload; avoid default service account token mounting if not needed.

Secrets Management and Rotation

Verify: Secrets are not stored in plaintext in Git; rotation is supported without downtime. Why: Multi-cloud increases the chance of inconsistent secret handling. Steps:

• Use a provider-neutral secret manager pattern: External Secrets Operator with backends (Vault, cloud secret managers) or sealed secrets for Git storage.
• Design applications to reload secrets (SIGHUP, periodic refresh, or restart) and test rotation in staging.
• Restrict secret access via RBAC and namespace boundaries; audit secret reads where possible.

Supply Chain Security for Images and Manifests

Verify: Images are scanned, signed, and pulled from controlled registries; manifests are policy-checked. Why: Production incidents often start with compromised dependencies. Steps:

• Use an image registry strategy that works across environments (self-hosted or replicated registries).
• Scan images in CI and block critical vulnerabilities by policy; store SBOMs alongside artifacts.
• Adopt signature verification (for example, Sigstore/cosign) and enforce admission policies that require signed images.

Observability and Operational Telemetry

Golden Signals and SLO-Driven Monitoring

Verify: You can observe latency, traffic, errors, and saturation for edge and application layers, and tie them to SLOs. Why: Provider-agnostic operations require consistent signals regardless of where the cluster runs. Steps:

• Define SLOs per user-facing route (for example, 99.9% availability, p95 latency) and implement alerting based on error budgets.
• Instrument ingress metrics (requests, response codes, upstream latency) and application metrics (request duration, queue depth).
• Ensure dashboards are templated by cluster/environment so you can compare behavior across providers.

Centralized Logging with Correlation

Verify: Logs from ingress, platform components, and apps are centralized, searchable, and correlated by request ID/trace ID. Why: When incidents span multiple clusters/providers, you need consistent log structure. Steps:

• Adopt structured logging (JSON) and standard fields: timestamp, service, namespace, pod, request_id, user_agent.
• Deploy a portable log pipeline (Fluent Bit/Vector) to a backend you can run anywhere (OpenSearch, Loki, or a managed equivalent with export).
• Standardize retention and access controls; separate security/audit logs from application logs.

Tracing and Dependency Visibility

Verify: You can trace user requests through ingress to backend services and identify slow hops. Why: Multi-environment debugging needs consistent context propagation. Steps:

• Use OpenTelemetry SDKs/collectors and export to a backend that is not tied to one cloud.
• Ensure ingress adds or forwards trace headers; validate sampling policies do not drop critical incident data.
• Create runbooks that start from a user-facing symptom and walk through traces, logs, and metrics in a repeatable order.

Resilience of Platform Components (Operational, Not Pattern-Level)

Control Plane Add-ons and Upgrade Discipline

Verify: Critical add-ons (DNS, ingress controller, cert-manager, autoscalers, storage drivers) have version pinning, upgrade notes, and rollback paths. Why: Many outages are caused by add-on upgrades rather than application code. Steps:

• Maintain an “add-on bill of materials” with versions and compatibility notes per Kubernetes version.
• Upgrade in a staging cluster that matches production topology; run smoke tests for ingress routing and certificate issuance.
• Keep previous Helm releases and container images available for rollback; document the rollback trigger criteria.

Capacity Planning for Shared Components

Verify: Shared components have capacity targets and alerts (CPU, memory, connection counts, queue sizes). Why: Even if applications scale, shared layers can become bottlenecks. Steps:

• Set resource requests/limits for ingress and observability agents; avoid “best effort” for critical pods.
• Alert on saturation indicators: node pressure, pod evictions, DNS latency, certificate issuance queue delays.
• Run periodic load tests that include the edge layer and validate that autoscaling triggers as expected.

Data, State, and Storage Portability

StorageClass Strategy and Stateful Workloads

Verify: Stateful components can run on multiple storage backends with minimal changes, and you understand the portability limits. Why: Storage is a major source of cloud lock-in. Steps:

• Define a small set of StorageClasses with consistent names across clusters (for example, fast, standard), mapped to provider-specific implementations.
• Document which workloads require ReadWriteMany vs ReadWriteOnce and validate that the target environments support them.
• Test backup/restore procedures in each environment and verify recovery time objectives (RTO) and recovery point objectives (RPO).

Backup, Restore, and Disaster Recovery Drills

Verify: You can restore critical data and platform configuration into a new cluster in a different environment. Why: Provider-agnostic claims are unproven without cross-environment recovery. Steps:

• Back up Kubernetes objects (namespaces, CRDs where appropriate) and persistent volumes using portable tools (Velero or storage-native snapshots with documented alternatives).
• Run quarterly restore drills into an isolated cluster; measure time to restore and validate application correctness.
• Store backups in a location accessible across providers (object storage with replication or a neutral storage endpoint).

Identity, Access, and Governance for Humans and Automation

RBAC Model and Least Privilege

Verify: Human and CI/CD access is scoped, audited, and consistent across clusters. Why: Multi-cluster operations amplify the blast radius of overly broad permissions. Steps:

• Define roles by job function (viewer, operator, platform-admin) and bind them to groups from your identity provider.
• Use short-lived credentials where possible; avoid long-lived kubeconfigs on laptops.
• Enable audit logging and regularly review privileged actions (clusterrolebindings, secret reads, exec into pods).

Policy as Code for Guardrails

Verify: Policies prevent risky configurations before they reach production. Why: Provider-agnostic platforms need consistent guardrails across environments. Steps:

• Enforce required labels/annotations, IngressClass usage, and resource requests/limits via Gatekeeper/Kyverno.
• Block privileged pods, hostPath mounts, and hostNetwork unless explicitly approved.
• Require approved container registries and disallow mutable tags.

Operational Runbooks and Incident Readiness

Runbooks for Common Web Platform Incidents

Verify: On-call engineers have step-by-step runbooks that map symptoms to checks and mitigations, independent of cloud provider consoles. Why: In a provider-agnostic posture, you must be able to operate from Kubernetes and your observability stack. Steps:

• Create runbooks for: ingress not receiving traffic, DNS misrouting, certificate renewal failures, sudden 4xx spikes, and latency regressions.
• Include exact commands and queries (kubectl, log queries, metric panels) and expected outputs.
• Maintain an “emergency access” procedure with approvals and time-bounded elevated permissions.

Change Management and Maintenance Windows

Verify: Platform changes are tracked, reviewed, and scheduled; emergency changes are documented after the fact. Why: Most production instability comes from uncontrolled change. Steps:

• Use a change template: what changes, risk, rollback, validation steps, and owner.
• Automate pre-flight checks: policy compliance, manifest diff, and smoke tests.
• After changes, require validation: synthetic checks, key dashboards, and error budget impact review.

Practical Step-by-Step: Turning the Checklist into a Release Gate

Step 1: Encode the Checklist as Tests

Convert checklist items into automated checks that run on every pull request and before production sync. Examples include: validating Ingress resources have an IngressClass, ensuring every Deployment has resource requests/limits, and confirming Pod Security settings meet your baseline.

# Example: policy check in CI (conceptual) 1) Render manifests (Helm/Kustomize) 2) Run kubeconform for schema validation 3) Run policy engine tests (Gatekeeper/Kyverno) 4) Fail build if violations exist

Step 2: Add a “Pre-Prod Verification” Job

Create a job that runs against the target cluster and verifies runtime conditions: ingress controller pods ready, cert-manager healthy, DNS records present, and synthetic HTTP checks passing. Keep it provider-neutral by using Kubernetes APIs and HTTP checks rather than cloud console calls.

# Example checks (conceptual) kubectl -n ingress get pods kubectl -n cert-manager get pods kubectl get ingress --all-namespaces curl -fsS https://your-domain.example/healthz

Step 3: Require Evidence in Pull Requests

For changes that affect production readiness (ingress, certificates, DNS automation, policies), require PR evidence: links to CI results, rendered manifest diffs, and updated runbooks. This creates an audit trail and prevents “tribal knowledge” operations.

Step 4: Schedule Regular Readiness Reviews

Production readiness is not a one-time milestone. Schedule periodic reviews where you re-run disaster recovery drills, validate backup restores, rotate secrets, and test cluster upgrades in staging. Track checklist compliance as a living scorecard per cluster and per environment.