GCP to AWS Migration Strategy: Complete Cross-Cloud Migration Guide

• Automated GCP resource inventory and dependency mapping
• AWS service mapping and architecture design
• Cost analysis and optimization recommendations
• Network connectivity setup (VPN/Direct Connect)

• AWS account setup with IAM roles and security policies
• VPC configuration with subnet and routing setup
• Target AWS services provisioning (RDS, S3, Redshift, etc.)
• Cross-cloud replication setup for zero-downtime migration

• Initial data transfer with optimized network paths
• Continuous replication setup for databases and storage
• Application code conversion (Cloud Functions to Lambda, etc.)
• Parallel testing in AWS environment

• Final data synchronization with consistency verification
• DNS cutover with gradual traffic migration
• Performance monitoring and optimization
• GCP resource decommissioning after validation period

Challenge: GCP and AWS have different API structures and authentication methods.

AI Solution: Automated API call conversion with 91% accuracy. Converts GCP client libraries to AWS SDK calls, handles authentication differences (GCP service accounts to AWS IAM roles), and updates error handling patterns.

Result: Application code migrated with minimal manual intervention.

Challenge: Cross-cloud data transfer can be slow and expensive.

AI Solution: Intelligent transfer optimization using AWS DataSync or Transfer Family with compression, parallel transfers, and bandwidth optimization. Automatically selects optimal transfer method based on data size and type.

Result: 5-10x faster data transfer with 40% lower egress costs.

Challenge: BigQuery and Redshift have different SQL dialects and optimization strategies.

AI Solution: Automated SQL conversion handling BigQuery-specific functions (ARRAY_AGG, STRUCT), partitioning to Redshift distribution keys, and query optimization for Redshift architecture. Converts 94% of queries automatically.

Result: Data warehouse migrated with equivalent or better performance.

Challenge: GCP and AWS have fundamentally different identity and access management systems.

AI Solution: Automated IAM policy conversion from GCP roles to AWS policies, service account to IAM role mapping, and security group configuration. Maintains principle of least privilege across platforms.

Result: Security posture maintained or improved during migration.

With AI-powered automation, GCP to AWS migration typically takes 3-5 weeks for most enterprise workloads. Traditional manual migration can take 6-12 months. Timeline depends on data volume, application complexity, and number of services. Simple migrations (single database, basic compute) can complete in 1-2 weeks, while complex multi-service architectures may take 6-8 weeks.

Yes, zero-downtime GCP to AWS migration is achievable using continuous replication and gradual cutover strategies. The approach involves setting up parallel AWS infrastructure, establishing real-time data replication between GCP and AWS, running both environments simultaneously during transition, and gradually shifting traffic using DNS or load balancer updates. Most applications experience zero user-facing downtime during the cutover.

AI-powered GCP to AWS migration costs 70% less than manual migration. For a typical 50TB workload, expect $75,000-$150,000 for automated migration vs $250,000-$500,000 for manual. Major cost factors include data egress fees from GCP (typically $0.12/GB), AWS data transfer in (free), migration tooling, and temporary dual-cloud operation. Many organizations see 20-40% lower ongoing costs on AWS due to better pricing for their specific workload patterns.

GCP-specific features require careful mapping to AWS equivalents. BigQuery's nested/repeated fields map to Redshift SUPER or JSON columns. Cloud Spanner's global consistency maps to Aurora Global Database. GCP's IAM conditions map to AWS IAM policy conditions. AI agents automatically identify GCP-specific features and recommend AWS alternatives, converting 85-90% automatically and flagging the rest for manual review with specific recommendations.

Phased migration is recommended for most organizations. Start with non-critical workloads to validate the process, then migrate core applications with established patterns. Typical phasing: (1) Development/test environments first, (2) Non-critical production workloads, (3) Core business applications, (4) Mission-critical systems. This approach reduces risk, allows learning from early phases, and maintains business continuity. Complete "big bang" migrations are only recommended for smaller, less complex environments.