CloudifyX

Architecture & patterns

Solution areas we design and ship

Cards below summarize how we structure engagements. Underneath each pattern is Terraform, pipelines, observability, and identity work—documented so it survives handover.

Prefer a deeper dive on delivery? Start with secure landing zones, Kubernetes adoption tradeoffs, or the full engineering notes. For engagement shape, see services.

AWS reference architectures

Multi-account networking, centralized logging, EKS baselines, and data patterns that respect regulated boundaries.

  • Org-level guardrails with pragmatic exceptions
  • EKS + ingress + external DNS with clear ownership
  • Cross-account observability without noisy duplication

Azure enterprise platform patterns

Subscriptions, management groups, Entra ID integration, and AKS topologies aligned to Microsoft-centric estates.

  • Hub-spoke or vWAN models with clear peering rules
  • AKS hardening aligned to CIS-style baselines
  • Hybrid identity paths for staged migrations

GCP modernization patterns

Project/folder hierarchy, shared VPC, GKE, and data services for analytics-heavy or API-first modernization.

  • Workload identity and service account hygiene
  • GKE multi-team tenancy with quotas
  • Cost visibility for data-heavy pipelines

Hybrid & multi-cloud

When lift-and-shift is only chapter one: stable connectivity, identity, and traffic patterns across environments.

  • ExpressRoute / Direct Connect / Cloud VPN tradeoffs
  • DNS and certificate strategies that won’t surprise you
  • Migration sequencing that preserves rollback options

CI/CD pipeline patterns

Promotion flows, artifact integrity, and test gates that match how enterprise dependencies actually behave.

  • GitLab child pipelines and reusable jobs
  • Environment parity checks before production
  • Secrets and signing integrated into the path

Secure workload hosting

Defense in depth for internet-facing APIs and internal platforms—without turning every team into security experts.

  • WAF, DDoS, and edge patterns
  • mTLS and service identity where it earns its complexity
  • Vulnerability management hooks in CI

Scalable microservices foundations

Service boundaries, async patterns, and observability that keep distributed systems understandable under load.

  • API gateway vs mesh—decisions with evidence
  • SLOs for synchronous vs asynchronous paths
  • Load testing that reflects real user journeys
Plan a solutions workshop

Delivery rhythm

From architecture intent to production evidence

We avoid big-bang slides. Each phase produces working artifacts your teams can review, test, and extend.

  1. Step 1

    Assess

    Constraints, risk, dependencies

  2. Step 2

    Design

    Reference + guardrails

  3. Step 3

    Build

    IaC, pipelines, clusters

  4. Step 4

    Prove

    Load, security, failover

  5. Step 5

    Handover

    Runbooks, training, SLOs