Multi-Cloud Deployments with GitHub Actions

Overview

Sketch OpenID Connect (OIDC)–oriented GitHub Actions patterns for Amazon Web Services (AWS) Identity and Access Management (IAM) / Elastic Container Service (ECS) / Elastic Kubernetes Service (EKS), Azure service principals / Azure Kubernetes Service (AKS), and Google Cloud Workload Identity Federation / Google Kubernetes Engine (GKE) / Cloud Run — without embedding long-lived cloud keys in the repository.

Modern deploy jobs federate identity: the job requests an OIDC token (id-token: write); the cloud exchanges it for a short-lived role. That role then updates ECS/EKS, AKS, GKE, or Cloud Run. Patterns differ by cloud, but the Actions shape is the same — authenticate, deploy an immutable artefact from Module 7, protect production with environments.

This is a core tutorial in Module 10 · Cloud Deployments of the REBASH Academy GitHub Actions for Cloud & DevOps Engineers series — written for Cloud, DevOps, Platform, and SRE engineers.

Prerequisites

• Terraform Pipelines with GitHub Actions

Learning Objectives

By the end of this tutorial, you will be able to:

• Explain OIDC federation vs static access keys
• Map AWS IAM roles to ECS/EKS deploy jobs
• Sketch Azure login (federated credentials) + AKS
• Sketch GCP Workload Identity + GKE / Cloud Run
• Scope identities per environment and branch

Architecture

This topic’s control points and relationships are shown below.

Theory

What it is

Multi-cloud deployment from GitHub Actions means workflows call cloud APIs with least-privilege, short-lived credentials:

Cloud	Identity pattern	Common targets
AWS	IAM OIDC provider → aws-actions/configure-aws-credentials	ECS update-service, EKS kubeconfig
Azure	App registration federated credential → azure/login	AKS kubelogin / kubectl
Google Cloud	Workload Identity Federation → google-github-actions/auth	GKE, Cloud Run deploy

GitHub’s OIDC issuer (https://token.actions.githubusercontent.com) is trusted by a cloud identity provider you configure once. Trust conditions should include repository, ref, and optionally environment claims so a compromised pull-request job cannot assume production roles.

Why it matters

Static keys in repository secrets leak, rarely rotate, and are often over-privileged. OIDC binds trust to sub / ref / environment so fork or feature-branch pipelines cannot deploy production. Multi-cloud teams need one mental model — federate, deploy SHA artefact, gate production — even when CLIs differ. Terraform (Module 9) often creates the OIDC providers and roles; deploy workflows only consume them.

How it works

1. Configure cloud trust for the GitHub OIDC issuer and subject conditions (repo:ORG/REPO:ref:refs/heads/main, or environment subjects).
2. Job sets permissions: id-token: write and runs the cloud’s auth action.
3. Deploy uses that session: update an ECS task definition, helm upgrade / kubectl on EKS/AKS/GKE, or gcloud run deploy with a digest.
4. Staging roles allow default-branch (or selected) pipelines; production roles require protected environments and required reviewers.
5. Never print tokens. Prefer environment variables for account IDs and cluster names; secrets only when federation is not available.

Reuse Module 7 digests across clouds for the same commit — do not rebuild a different image “for Azure” versus “for AWS”.

Key concepts and comparisons

Approach	Pros	Cons
OIDC / federation	Short-lived, auditable, branch-aware	Initial IdP setup
Static keys / PATs	Simple demos	Rotation, leak blast radius
Per-cloud jobs	Clear ownership	Duplicate structure — prefer reusable workflows (Module 14)

Common pitfalls

• Trusting * subjects on the OIDC provider (any repo can assume the role).
• Giving one role rights to all accounts and clusters.
• Running production deploy jobs without environment protection.
• Mixing long-lived keys “just for break-glass” without a separate process.
• Redeploying different image digests per cloud for the same commit.

Hands-on Lab

Create a workspace for this tutorial.

mkdir -p ~/rebash-github-actions/module-10/.github/workflows && cd ~/rebash-github-actions/module-10/.github/workflows

Focus: OIDC-ready jobs for AWS and Azure shapes (file-only)

Step 1 – Multi-cloud workflow

mkdir -p .github/workflows
cat > .github/workflows/multi-cloud.yml << 'EOF'
name: Multi-cloud deploy shapes
on: [workflow_dispatch]
permissions:
  contents: read
jobs:
  aws_oidc:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      id-token: write
    environment: aws-staging
    steps:
      - run: echo "configure-aws-credentials when trust exists"
  azure_oidc:
    runs-on: ubuntu-latest
    permissions:
      contents: read
      id-token: write
    environment: azure-staging
    steps:
      - run: echo "azure/login with OIDC when federated credential exists"
EOF

Step 2 – Confirm separate environments and id-token

grep -E 'id-token: write|aws-staging|azure-staging' .github/workflows/multi-cloud.yml

Final step – Cleanup note

# File-only — no cloud calls
# Keep ~/rebash-github-actions/ for later tutorials

Validation

• Lab commands run under ~/rebash-github-actions/module-10/.github/workflows/
• You can explain each Theory section in your own words
• You used modern tooling where it applies to this topic
• You can describe one production failure mode for this topic

Code Walkthrough

Production practice for Multi-Cloud Deployments with GitHub Actions always combines:

1. Inspect before you change (status, plan, logs, dry-run)
2. Prefer reversible, documented changes (Git, IaC, drop-ins, version pins)
3. Capture evidence (command output, pipeline logs) for handovers
4. Prefer current tools and APIs over legacy shortcuts
5. Least privilege — escalate credentials only when required

Keep runbooks short enough to follow under pressure. Automate checks; keep humans for judgement.

Security Considerations

• Treat credentials and tokens for github-actions as privileged — never commit them
• Prefer short-lived auth (OIDC, roles, SSO) over long-lived keys
• Validate blast radius before apply/deploy/delete operations
• Restrict who can approve production changes
• Collect audit logs; limit who can read sensitive traces

Common Mistakes

Trusting * subjects on the OIDC provider (any repo can assume the role).

Validate assumptions against the Theory section and official docs before changing production.

Giving one role rights to all accounts and clusters.

Lab shortcuts (open security groups, admin roles, skip approvals) must not ship unchanged.

Changing production without a rollback path

Always know how to revert (previous artefact, prior release, state rollback, DNS failback).

Best Practices

• Encode Multi-Cloud Deployments with GitHub Actions changes as code and review them in pull requests
• Pin versions (images, modules, actions, provider plugins)
• Separate environments with clear promotion gates
• Alert on symptoms with runbooks attached
• Destroy lab resources; tag everything with owner and expiry where possible

Troubleshooting

Symptom	Likely cause	Fix
Auth / permission denied	Wrong identity, policy, or scope	Check caller identity, roles, and least-privilege policies
Timeout / no route	Network, DNS, security group, or endpoint	Trace path, DNS, and allow-lists before retrying
Drift / unexpected plan	Manual change or wrong state/workspace	Reconcile desired vs actual; avoid click-ops on managed resources
Pipeline/job red	Flaky step, cache, or missing secret	Read failing step logs; bisect recent workflow/config changes
Cost spike	Idle load balancer, NAT, oversized compute	Inventory billable resources; stop/delete labs promptly

Summary

Multi-Cloud Deployments with GitHub Actions is essential for Cloud and DevOps engineers working with github-actions. Practise the lab until the inspection and change path is muscle memory, then continue the track.

Interview Questions

1. How do you structure workflows for AWS and Azure without duplication?
2. What OIDC settings differ per cloud provider?
3. When are reusable workflows better than copy-paste jobs?
4. How do you prevent credential mix-ups across clouds?
5. Which checks should every cloud deploy share?

Sample answer — question 2

Verify environment names, id-token permissions, and the cloud-specific login action configuration.

Sample answer — question 4

Isolate roles per cloud/environment and keep production approvals separate.

Related Tutorials

• Course overview
• Security Scanning and Supply Chain

References

• OIDC with cloud providers · AWS configure-aws-credentials · Azure login · google-github-actions/auth