GCP vs AWS: Complete Technical Comparison Guide

Choose between Google Cloud Platform and Amazon Web Services based on services, pricing, ecosystem, and enterprise requirements.

Market Position

AWS: Market leader since 2006, widest service catalog, deepest enterprise adoption, most mature ecosystem

GCP: Launched 2008, strong in data analytics and machine learning, innovative pricing, excellent for containerized workloads

Market Share (2024): AWS 32%, Azure 23%, GCP 11%

Compute Services

AWS EC2 vs GCP Compute Engine:

• Both offer similar instance types and configurations
• AWS has more instance type varieties
• GCP offers sustained use discounts automatically
• GCP allows custom machine types without predefined sizes
• AWS spot instances vs GCP preemptible VMs (similar pricing models)

Serverless Computing:

• AWS Lambda vs GCP Cloud Functions
• Lambda supports more runtimes and triggers
• Cloud Functions v2 built on Cloud Run (better scalability)
• Both support containerized functions
• Lambda has 15-minute max execution, Cloud Functions has 60-minute max

Container Orchestration:

• AWS ECS/EKS vs GCP GKE (Google Kubernetes Engine)
• GKE generally considered more mature (Google created Kubernetes)
• GKE Autopilot fully manages cluster operations
• EKS integrates deeply with AWS services
• Both support multi-cluster management

Storage Services

Object Storage:

• AWS S3 vs GCP Cloud Storage
• S3 has more storage classes and lifecycle options
• Cloud Storage has simpler pricing structure
• Both offer 99.999999999% durability
• S3 more feature-rich, Cloud Storage easier to use

Block Storage:

• AWS EBS vs GCP Persistent Disks
• Similar performance characteristics
• GCP offers automatic encryption by default
• EBS snapshots vs GCP disk snapshots (similar functionality)
• Both support SSD and HDD options

File Storage:

• AWS EFS vs GCP Filestore
• EFS supports NFSv4 protocol
• Filestore offers higher performance tiers
• Both support automatic scaling

Database Services

Relational Databases:

• AWS RDS vs GCP Cloud SQL
• RDS supports more database engines (PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, Aurora)
• Cloud SQL supports PostgreSQL, MySQL, SQL Server
• Aurora (AWS proprietary) offers superior performance for MySQL/PostgreSQL workloads
• Cloud Spanner (GCP) provides globally distributed SQL database

NoSQL Databases:

• AWS DynamoDB vs GCP Firestore/Bigtable
• DynamoDB fully managed key-value and document database
• Firestore real-time document database optimized for mobile/web
• Bigtable high-performance NoSQL for analytics and time-series
• DynamoDB more mature with richer feature set

Data Warehousing:

• AWS Redshift vs GCP BigQuery
• BigQuery serverless architecture (no cluster management)
• Redshift requires cluster sizing and management
• BigQuery superior for ad-hoc analytics and ML integration
• Redshift better for predictable workloads with reserved capacity

Networking

Virtual Private Cloud:

• AWS VPC vs GCP VPC
• GCP VPC is global by default (subnets span regions)
• AWS VPC is regional (requires VPC peering for cross-region)
• Both support hybrid cloud with VPN and dedicated connections
• GCP VPC easier to set up for global applications

Load Balancing:

• AWS ELB (ALB/NLB/CLB) vs GCP Cloud Load Balancing
• GCP offers single global load balancer (anycast IP)
• AWS requires regional load balancers
• Both support HTTP/HTTPS, TCP, and UDP load balancing
• GCP load balancing simpler for multi-region applications

CDN:

• AWS CloudFront vs GCP Cloud CDN
• CloudFront larger edge network (400+ locations)
• Cloud CDN integrates tightly with GCP load balancers
• Both offer similar caching and SSL/TLS capabilities

Machine Learning and AI

Pre-trained Models:

• AWS offers SageMaker, Rekognition, Comprehend, Translate
• GCP offers AI Platform, Vision AI, Natural Language AI, Translation AI
• GCP generally considered more advanced in ML/AI capabilities
• Both provide AutoML capabilities

ML Training:

• AWS SageMaker vs GCP AI Platform
• SageMaker more comprehensive feature set
• AI Platform better integration with TensorFlow (Google created TensorFlow)
• Both support distributed training and hyperparameter tuning

Specialized Accelerators:

• AWS offers GPU instances (NVIDIA)
• GCP offers GPU instances plus TPUs (Tensor Processing Units)
• TPUs optimized for TensorFlow workloads
• AWS Inferentia chips for inference workloads

Data Analytics

Big Data Processing:

• AWS EMR (Elastic MapReduce) vs GCP Dataproc
• Both managed Hadoop/Spark services
• Dataproc faster cluster spin-up times
• EMR supports more big data frameworks
• Dataproc better integrated with BigQuery

Stream Processing:

• AWS Kinesis vs GCP Pub/Sub + Dataflow
• Kinesis Data Streams for real-time data ingestion
• Pub/Sub messaging + Dataflow stream processing
• Dataflow based on Apache Beam (unified batch/stream)
• Kinesis more feature-complete for streaming analytics

Data Pipeline:

• AWS Glue vs GCP Dataflow/Dataprep
• Glue serverless ETL service
• Dataflow unified batch and stream processing
• Dataprep visual data preparation tool
• Both support scheduled and triggered workflows

Developer Tools

CI/CD:

• AWS CodePipeline/CodeBuild/CodeDeploy vs GCP Cloud Build
• AWS offers full suite of integrated DevOps tools
• Cloud Build simpler, integrates with GitHub, Bitbucket
• Both support Docker container builds

Infrastructure as Code:

• AWS CloudFormation vs GCP Deployment Manager
• CloudFormation more mature with larger template library
• Both support Terraform as third-party alternative
• CloudFormation better documentation and community support

Monitoring and Logging:

• AWS CloudWatch vs GCP Cloud Monitoring (formerly Stackdriver)
• CloudWatch more feature-rich for AWS resources
• Cloud Monitoring better for Kubernetes workloads
• Both support custom metrics and alerting

Pricing Models

Compute Pricing:

• AWS: On-Demand, Reserved Instances (1-3 years), Savings Plans, Spot Instances
• GCP: On-Demand, Committed Use Discounts (1-3 years), Sustained Use Discounts (automatic), Preemptible VMs
• GCP sustained use discounts apply automatically (no upfront commitment)
• AWS Reserved Instances require capacity planning
• GCP generally 10-20% cheaper for compute with sustained use

Data Transfer:

• AWS charges for data egress between regions and to internet
• GCP charges for data egress to internet, free between regions in same multi-region
• GCP generally cheaper for global data transfer
• Both offer free tier for inbound data transfer

Storage Pricing:

• AWS S3: Multiple storage classes with different pricing
• GCP Cloud Storage: Simpler pricing with fewer storage classes
• GCP Nearline/Coldline competitive with AWS S3 Glacier
• Both charge for API requests and data retrieval

Enterprise Features

Global Infrastructure:

• AWS: 32 regions, 102 availability zones
• GCP: 39 regions, 118 availability zones
• AWS broader geographic coverage overall
• GCP stronger presence in Asia Pacific
• Both expanding rapidly

Compliance Certifications:

• Both meet major compliance standards: SOC 1/2/3, PCI DSS, HIPAA, GDPR, ISO 27001
• AWS slightly more compliance certifications overall
• GCP strong in financial services compliance
• Both offer region-specific compliance options

Support Plans:

• AWS: Developer (29/month), Business (100/month or 10%), Enterprise (15,000/month or 10%)
• GCP: Basic (free), Standard (150/month or 3%), Enhanced (500/month or 5%), Premium (negotiated)
• AWS Enterprise Support includes Technical Account Manager
• GCP Premium Support offers faster response times

Marketplace:

• AWS Marketplace: 12,000+ software listings
• GCP Marketplace: 2,000+ solutions
• AWS Marketplace more mature and comprehensive
• Both support private marketplace for enterprise procurement

Migration Considerations

From On-Premise to Cloud:

• AWS Migration Hub comprehensive migration tooling
• GCP Migrate for Compute Engine for VM migration
• Both offer database migration services
• AWS Direct Connect vs GCP Cloud Interconnect for hybrid connectivity

Between Cloud Providers:

• Data egress costs significant consideration
• Multi-cloud architectures possible but add complexity
• Kubernetes provides some workload portability
• Both offer professional services for migration assistance

Best Use Cases

Choose AWS if:

• Need widest service selection and maturity
• Existing enterprise investment in AWS ecosystem
• Require specific AWS-only services (Aurora, DynamoDB)
• Prefer regional isolation for compliance
• Want largest partner ecosystem

Choose GCP if:

• Heavy data analytics and machine learning workloads
• Kubernetes-native applications
• Global applications requiring simpler networking
• Want automatic sustained use discounts
• Prefer simpler pricing model
• BigQuery for data warehouse needs
• TensorFlow ML workloads

Hybrid AWS and GCP:

• Use AWS for core infrastructure and broad service needs
• Use GCP for analytics (BigQuery) and ML workloads
• Leverage Kubernetes for cross-cloud portability
• Higher complexity but avoids vendor lock-in

Bottom Line

AWS offers the broadest service catalog, deepest enterprise features, and most mature ecosystem. GCP excels in data analytics, machine learning, Kubernetes, and offers simpler, more transparent pricing with automatic discounts. Choose AWS for comprehensive cloud needs and ecosystem depth. Choose GCP for analytics-heavy workloads, containerized applications, and cost optimization through automatic discounts.

Quick Decision: If unsure, start with AWS for wider service options. Adopt GCP for specific workloads like analytics and ML where it excels.