GCP vs AWS Service Mapping: A Cheat Sheet for Multi-Cloud Engineers

Quick Answer: Almost every core service has a direct counterpart — S3 becomes Cloud Storage, EC2 becomes Compute Engine, EKS becomes GKE, Redshift becomes BigQuery, Route 53 becomes Cloud DNS, SQS and SNS become Pub/Sub. The gcp vs aws services mapping is easy to memorise and misleading to trust: the semantics differ most in IAM, VPC structure, firewall behaviour and billing boundaries, and that is where multi-cloud engineers actually get hurt.

Why a Service Mapping Is Only Half the Job

Anyone moving between clouds eventually builds a table like the one below, usually by googling “GCP equivalent of S3” one service at a time. The table saves you an afternoon of console confusion and is genuinely useful in interviews, where being able to reason across platforms signals range.

What the table cannot tell you is where the equivalence stops. Object storage on both clouds behaves similarly until you hit ACLs, lifecycle rules and cross-account access — then the models diverge and an assumption copied from AWS fails in GCP. Networking is the same story with higher stakes: a design that is safe on AWS can be quietly permissive on GCP because the default firewall and identity behaviour is different.

So treat this as two documents: a mapping you learn for fluency, and a list of semantic differences you learn for survival. The second list is what interviewers probe and what production incidents are made of.

The GCP vs AWS Services Mapping Cheat Sheet

Equivalent Tasks, Side by Side

Fluency shows up in commands, not in tables. These are the same three tasks on both platforms:

# 1. Create a private bucket with uniform access (GCP) / blocked public access (AWS)
gcloud storage buckets create gs://acme-logs-2026 --location=asia-south1 --uniform-bucket-level-access
aws s3api create-bucket --bucket acme-logs-2026 --region ap-south-1 
  --create-bucket-configuration LocationConstraint=ap-south-1

# 2. List running compute
gcloud compute instances list --format="table(name,zone,machineType,status)"
aws ec2 describe-instances --filters Name=instance-state-name,Values=running 
  --query 'Reservations[].Instances[].InstanceId' --output table

# 3. Grant a service account / role the project-level equivalent
gcloud projects add-iam-policy-binding acme-prod 
  --member="serviceAccount:[email protected]" 
  --role="roles/storage.objectAdmin"
aws iam put-role-policy --role-name ci-pipeline --policy-name s3-writes --policy-document file://s3-writes.json

Notice the shape of the difference in step 3: GCP grants roles against a project (or folder or organisation) with a member string, while AWS attaches policies to a principal inside one account. Once that mental model clicks, half of the confusion in multi-cloud work disappears.

Where the Mapping Breaks: IAM

AWS identity is account-centric: roles and policies live inside an account, resource-based policies can be attached to individual services, and cross-account access is built on role assumption with trust policies. GCP identity is hierarchy-centric: an organisation contains folders and projects, permissions cascade downwards, and the primitive roles (Owner, Editor, Viewer) coexist with hundreds of predefined and custom roles bound at any level of the tree.

The practical consequences are worth memorising. In GCP, a user granted roles/editor at folder level inherits it across every project below — a blast radius far wider than most AWS engineers expect. Service accounts act as both identities and the thing being granted permissions, and long-lived JSON keys are the equivalent of a root access key sitting in a CI system: widely discouraged, increasingly blocked by organisation policy, and replaced with workload identity federation. In AWS terms you would reach for a role with an external ID; in GCP you reach for workload identity or impersonation. The instinct transfers; the syntax does not.

Where the Mapping Breaks: Networking

Three differences generate most of the incidents. First, a GCP VPC is global while subnets are regional — you do not create a VPC per region the way you do per region in AWS, and there is no availability-zone-scoped CIDR planning to do. Second, firewall behaviour: AWS security groups attach to network interfaces and are stateful and allow-only, while GCP firewall rules attach to the VPC and target instances by network tag or service account, evaluated against both ingress and egress, alongside hierarchical firewall policies. There is no direct NACL equivalent, and copying an AWS network design without rethinking rule targets is how teams end up either locked out or far too open. Third, load balancer scope — GCP’s global anycast load balancer fronts resources across regions by default, which is a genuinely different architectural capability, not a naming quirk.

If the networking side is where you are weakest, the AWS-side fundamentals are still worth understanding properly — our VPC explainer covers the concepts that then map across.

War Story: The JSON Key That Failed Security Review

A Pune team running a hybrid estate — applications on AWS, analytics on GCP — built a CI job that pushed data files to a GCP bucket using a service account JSON key stored as a repository secret. It worked for seven months. During a client security review ahead of a BFSI contract, the assessor asked a simple question: who can use this key, where else does it exist, and what happens when the engineer who created it leaves?

The answers were uncomfortable. The key had been downloaded to two laptops during debugging, its permissions were broader than the job needed (roles/editor on the project, granted “so it would just work”), and rotation had never happened because the pipeline would break. The mapping assumption that caused it was an AWS one: in AWS, a CI system would normally assume an IAM role — no long-lived secret at all. The team had translated the storage step across clouds without translating the identity model, and the review caught exactly that.

Remediation took a sprint: workload identity federation so the CI job authenticated as itself with short-lived credentials, a least-privilege custom role limited to the target bucket, org-policy enforcement against service account key creation, and an alert if a key appeared. The company won the contract, and the engineer who drove the fix used the story in interviews for a platform role at ₹32 LPA. It is the clearest illustration we have of why a service mapping table is a starting point rather than an answer.

How to Use This Mapping in an Interview

Multi-cloud questions come in two flavours, and the mapping table only helps with one. The factual flavour — “what is the GCP equivalent of SQS?” — deserves a quick, confident answer with the caveat attached: Pub/Sub with pull subscriptions behaves more like SNS plus SQS than like either alone, and FIFO semantics do not map directly. Getting the caveat right signals that you have used both platforms rather than memorised a list.

The second flavour decides offers: a design decision under constraint. “Our application runs on AWS and we want analytics on GCP — how do you handle identity, data movement and egress cost?” The strong answer reasons from concepts rather than names: short-lived cross-cloud credentials through workload identity federation instead of stored keys, Pub/Sub or Dataflow for ingestion with explicit idempotency, Cloud Storage lifecycle rules applied deliberately rather than copied from an S3 design, and a clear statement of where the new platform’s semantics differ from the old one. Plenty of candidates can recite the table. Almost none can say where it stops being true, and that difference is what hiring managers are listening for.

Which Platform to Learn First for Multi-Cloud Work

Start with AWS unless you have a specific reason not to — the job volume in India is roughly double, the learning material is better, and every employer recognises it. Then learn GCP through the mapping you just read: same concepts, new names, plus the hierarchy and networking differences above. Three months of deliberate work is a realistic estimate for someone already productive on AWS, and the combination is worth real money because it is genuinely scarce — see the AWS vs GCP careers comparison for the full reasoning.

One piece of counter-intuitive advice: the actual multi-cloud lever is Terraform, not certifications on both platforms. Modules abstract provider differences, CI pipelines stay the same, and an employer sees a transferable skill rather than two badges. Our Terraform vs CDK vs CloudFormation comparison covers why it dominates the job market.

Be sceptical, too, of roles advertising “multi-cloud” as the core requirement. Most turn out to be strong in one platform and curious about the other. Depth on one plus working fluency on the second is how these careers actually develop, and it is how the salary bands in our Google Cloud salary guide get reached.

Training Resources

Build the second platform properly with SkilBrill Google Cloud Fundamentals Training — core services mapped against what AWS engineers already know, with the IAM and networking differences taught explicitly. For depth beyond the mapping, continue to Google Cloud Engineering Training.

FAQ

What is the GCP equivalent of AWS services?

S3 maps to Cloud Storage, EC2 to Compute Engine, EKS to GKE, Lambda to Cloud Functions and Cloud Run, RDS to Cloud SQL, Redshift to BigQuery, SQS and SNS to Pub/Sub, Route 53 to Cloud DNS, and CloudWatch to Cloud Monitoring. The mapping is close for compute and storage, and diverges most in identity and networking.

Is it easier to learn GCP if I already know AWS?

Yes — most concepts transfer within weeks. Expect the real friction in IAM’s resource hierarchy and VPC’s global model, because those are genuinely different designs rather than renamed equivalents.

Should I learn AWS or GCP first for multi-cloud roles?

AWS first for job volume and recognition, then GCP through the service mapping. Employers value depth on one platform plus working fluency on a second far more than two shallow certifications.

Do IAM and VPC work the same way on both clouds?

No, and this is the most common source of multi-cloud incidents. AWS IAM is account-centric with roles and policies; GCP IAM cascades down an org, folder and project hierarchy. A GCP VPC is global with regional subnets, and firewall rules target tags at the VPC level rather than instances directly.

Is multi-cloud a good career skill in India?

As a combination, yes — strong on one platform with genuine working knowledge of a second commands a premium because the profile is scarce. As a specialism it is weaker, since most employers want depth somewhere rather than breadth everywhere.

Written by SkilBrill Training Institute

October 15, 2026 · 10 min read