Migrating from Google Cloud to AWS: Service Mapping and Guide

Teams migrating from Google Cloud Platform to AWS are usually solving a specific problem, not abandoning GCP entirely. The most common drivers: hiring AWS-certified engineers is easier in most markets, a key enterprise customer requires AWS, a specific AWS service (Bedrock, SES, or a compliance certification) is unavailable on GCP, or an acquisition is forcing platform consolidation.

This guide is written for DevOps engineers and engineering managers who need a realistic picture of what changes, what stays the same, and where the genuine complexity lies. We are an [AWS Select Tier Consulting Partner](/services/aws-migration) — we will be direct about both platforms' strengths.

## GCP to AWS Service Mapping

| GCP Service                       | AWS Equivalent                         | Key Differences                                                                                |
| --------------------------------- | -------------------------------------- | ---------------------------------------------------------------------------------------------- |
| Compute Engine                    | EC2                                    | Per-second billing on both; Graviton instances ~20% cheaper for Linux workloads                |
| Cloud Run                         | ECS Fargate / App Runner               | Cloud Run has simpler config; Fargate has deeper AWS ecosystem integration                     |
| Google Kubernetes Engine (GKE)    | Amazon EKS                             | GKE Autopilot fully managed; EKS requires more configuration but has richer add-on ecosystem   |
| Cloud Functions                   | AWS Lambda                             | Lambda has more trigger sources; Cloud Functions v2 now uses Cloud Run under the hood          |
| App Engine                        | AWS Elastic Beanstalk                  | Both are PaaS wrappers; Beanstalk is less actively developed than App Engine                   |
| Cloud Storage                     | Amazon S3                              | Near-identical APIs; S3 has deeper lifecycle and tiering policies                              |
| Cloud SQL                         | Amazon RDS                             | Both managed relational; Aurora is the premium-tier equivalent                                 |
| Cloud Spanner                     | Amazon Aurora Global / DynamoDB Global | No perfect AWS equivalent for globally distributed SQL; this is GCP's unique strength          |
| Firestore                         | Amazon DynamoDB                        | Both NoSQL; DynamoDB requires more upfront schema planning                                     |
| BigQuery                          | Amazon Redshift + Athena               | No single equivalent — AWS splits this into two services (see BigQuery Decision section below) |
| Pub/Sub                           | Amazon SNS + SQS                       | SNS for fan-out/publish-subscribe; SQS for queuing; EventBridge for event routing              |
| Dataflow                          | AWS Glue + Kinesis Data Streams        | Glue handles batch ETL; Kinesis handles streaming pipelines                                    |
| Dataproc                          | Amazon EMR                             | Both managed Hadoop/Spark; EMR has more instance type flexibility                              |
| Vertex AI                         | Amazon SageMaker                       | Both full ML platforms; SageMaker arguably has more breadth                                    |
| Gemini (Vertex AI)                | Amazon Bedrock (Claude, Titan, Llama)  | Multi-model API vs Google's proprietary models                                                 |
| Cloud CDN                         | Amazon CloudFront                      | CloudFront has Lambda@Edge; Cloud CDN is simpler but less powerful                             |
| Cloud Armor                       | AWS WAF + Shield                       | WAF is more granular; Shield Standard is free on all AWS accounts                              |
| Cloud Load Balancing              | ALB / NLB / GLB                        | AWS separates by protocol (Layer 7 vs Layer 4); GCP has a unified LB                           |
| Cloud DNS                         | Amazon Route 53                        | Route 53 has more routing policies (latency-based, failover, weighted, geolocation)            |
| Dedicated Interconnect            | AWS Direct Connect                     | Both offer 1 Gbps and 10 Gbps dedicated private links                                          |
| Cloud VPN                         | AWS Site-to-Site VPN                   | Comparable feature parity; AWS also has Direct Connect for higher bandwidth                    |
| Identity Platform / Firebase Auth | Amazon Cognito                         | Cognito is more complex to configure but deeply AWS-integrated                                 |
| Secret Manager                    | AWS Secrets Manager                    | Near-identical; AWS also has Parameter Store for non-sensitive config                          |
| Cloud KMS                         | AWS KMS                                | Both managed key services; AWS KMS has broader service integration                             |
| Cloud Monitoring                  | Amazon CloudWatch                      | CloudWatch bundles metrics, logs, and alarms; GCP separates these                              |
| Cloud Logging                     | CloudWatch Logs                        | CloudWatch Logs Insights provides powerful query capability                                    |
| Cloud Trace                       | AWS X-Ray                              | Both distributed tracing; X-Ray has automatic Lambda instrumentation                           |
| Cloud Build                       | AWS CodeBuild                          | CodeBuild is part of the CodePipeline ecosystem; GCP Cloud Build is standalone                 |
| Artifact Registry                 | Amazon ECR                             | Both managed container registries with nearly identical functionality                          |
| GCS Transfer Service              | AWS DataSync                           | Both handle bulk data transfer from on-premises or other clouds                                |
| Transfer Appliance                | AWS Snowball                           | Both offer offline bulk data transfer devices for large migrations                             |
| Firebase Hosting                  | AWS Amplify Hosting / S3+CloudFront    | Amplify is the closer match for static and JAMstack sites                                      |

## The BigQuery Decision

This is the most significant architectural decision in a GCP→AWS migration. **BigQuery has no single AWS equivalent.** AWS splits the capability into two services, and choosing between them (or using both) is critical.

### Why AWS Split BigQuery

BigQuery is fundamentally two services layered together:

1. **Structured data warehouse** — organized into datasets, tables with schemas, optimized for SQL analytics
2. **Serverless ad-hoc query engine** — run SQL on unstructured data in object storage without loading it into a warehouse

AWS chose to separate these because they serve different workloads:

- **Amazon Redshift** — the structured warehouse (BigQuery's core)
- **Amazon Athena** — the serverless query engine (BigQuery's flexibility)

### When to Use Redshift

Choose Redshift if:

- You have structured, regularly updated datasets
- You run repeated analytical queries on the same data (cost-efficient)
- You need fast query performance on large datasets (massive parallel processing)
- You want to use BI tools (Tableau, Power BI) with optimized connectors
- You have strict data governance and compliance requirements (Redshift Spectrum can query S3 without moving data)

**Cost model:** Pay for nodes you provision (hourly rate). Redshift Spectrum adds $5 per TB of data scanned in S3.

### When to Use Athena

Choose Athena if:

- You have one-off analytical queries or infrequent analysis
- You want zero infrastructure management
- Your data lives in S3 and you don't want to load it into a warehouse
- You have unstructured or semi-structured data (JSON, Parquet, CSV)
- You're okay with variable query costs based on data scanned

**Cost model:** Pay per TB of data scanned ($6.25/TB in us-east-1). No minimum; pay only for queries you run.

### When to Use Both

Many teams use Redshift and Athena together:

- **Redshift** for your operational analytics — the "single source of truth" data warehouse
- **Athena** for ad-hoc exploration and one-off reports — query raw data in S3 without infrastructure

This mirrors how some BigQuery teams use BigQuery's structured tables for regular BI plus ad-hoc queries against raw logs in Cloud Storage.

### The Architectural Implication

If you use BigQuery heavily, plan for 4–8 weeks to understand your workload patterns and decide between Redshift, Athena, or both. This is the most common blocker in GCP→AWS migrations.

## Migration Phases

A phased migration reduces risk and allows for parallel workload validation. Plan 8–12 weeks depending on workload size and analytics complexity.

### Phase 1: Assessment & Service Mapping (Weeks 1–2)

- Inventory all GCP services in use
- Map each to the AWS equivalent (use the table above)
- Document any services with no direct equivalent (e.g., BigQuery, Cloud Spanner)
- Identify data volume and transfer strategy
- Plan for the BigQuery split (Redshift vs Athena vs both)

### Phase 2: AWS Infrastructure Provisioning (Weeks 2–3)

- Provision AWS account, IAM, VPC, subnets
- Set up networking: Site-to-Site VPN or Direct Connect for secure data transfer
- Provision compute: EC2, Fargate, or EKS depending on your workload
- Provision data storage: RDS for relational, DynamoDB for NoSQL, S3 for object storage
- Set up Redshift or Athena based on Phase 1 analysis

### Phase 3: Data Migration (Weeks 3–6)

- **Relational data:** Use AWS DMS (Database Migration Service) for continuous replication from Cloud SQL → RDS
- **Object storage:** Migrate GCS → S3 using AWS DataSync or S3 Transfer Acceleration
- **NoSQL data:** Migrate Firestore → DynamoDB (requires application-layer schema transformation)
- **BigQuery data:** Export to GCS, then to S3, then load into Redshift or keep in S3 for Athena
- Validate data integrity in AWS before cutover

### Phase 4: Container & Compute Migration (Weeks 4–8, parallel with Phase 3)

- **GKE → EKS:** Migrate Kubernetes manifests, test on EKS Autopilot
- **Compute Engine → EC2/Fargate:** Port VM workloads to EC2 or containerize for Fargate
- **Cloud Functions → Lambda:** Rewrite Cloud Functions as Lambda functions
- **Cloud Run → Fargate/App Runner:** Migrate containerized workloads

## Pricing Model Differences

### Discount Models

**GCP sustained use discounts:**

- Automatic — no commitment required
- Up to 30% off compute after 25% usage in a month
- Applies automatically; no upfront cost

**AWS Savings Plans:**

- Require 1-year or 3-year commitment
- Up to 40–60% savings for consistent workloads
- Require forecasting; no flexibility if your needs change

### Instance Pricing at Comparable Scale

**Compute (2 vCPU / 4 GB memory):**

- GCP: e2-medium (standard) ~$29/month (with sustained use discount ~$20/month)
- AWS: t3.medium (on-demand) ~$30/month (with 1-yr Savings Plan ~$19/month)

**Result:** At comparable volume, pricing is virtually identical once discounts are applied. AWS requires more commitment; GCP is more flexible.

### Egress Pricing

- **GCP → GCP egress:** Free (within Google's network)
- **AWS → AWS egress:** Free (within AWS regions); $0.01/GB cross-region

If your workload transfers data between regions, AWS costs add up. Budget egress explicitly.

### GPU / Specialized Hardware

- **GCP:** T4, V100, A100 GPUs; TPUs (unique to Google, especially good for ML)
- **AWS:** NVIDIA GPUs (more inventory), AWS Trainium (similar to TPU)

GPU pricing is comparable; GCP's TPUs are uniquely powerful for specific ML workloads but have no direct AWS equivalent (Trainium is the closest).

## Related Comparisons

Explore other technical comparisons:

- [AWS vs GCP for Startups](/compare/aws-vs-gcp-for-startups)
- [AWS ECS vs EKS](/compare/aws-ecs-vs-eks)
- [AWS RDS vs Aurora](/compare/aws-rds-vs-aurora)

## Related Comparisons

Explore other technical comparisons:

- [DigitalOcean to AWS](/compare/digitalocean-to-aws)
- [Heroku to AWS](/compare/heroku-postgres-to-aws-rds)

## Why Choose FactualMinds for Your AWS Migration

FactualMinds is an **AWS Select Tier Consulting Partner** specializing in cloud platform migrations. We have executed GCP, DigitalOcean, Heroku, and MongoDB migrations to AWS and know the pitfalls.

- **Migration architects** — assessment-first methodology mapping your current state before execution
- **Zero-downtime cutover** — we execute migrations with minimal business impact
- **AWS Select Tier Partner** — [verified on AWS Partner Network](https://partners.amazonaws.com/partners/001aq000008su2EAAQ/Factual%20Minds)
- [AWS Marketplace Seller](https://aws.amazon.com/marketplace/seller-profile?id=seller-m753gfqftla7y)

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