Choosing the right cloud provider is one of the most important infrastructure decisions your business will make. Amazon Web Services (AWS) and Google Cloud Platform (GCP) are two of the leading cloud providers, each with distinct strengths and use cases. In this article, we'll break down the key differences to help you make an informed decision.
Market Position and Maturity
AWS launched in 2006 and has maintained its position as the market leader with approximately 32% market share. Its early start gave it time to develop the most comprehensive service catalog in the industry.
GCP entered the market in 2008 and has grown to approximately 10% market share. While smaller, Google leverages its expertise in data analytics, machine learning, and global infrastructure.
Core Compute Services
Virtual Machines
| Feature | AWS (EC2) | GCP (Compute Engine) |
|---|---|---|
| Instance Types | 400+ types | 150+ types |
| Custom Sizing | Limited | Fully customizable |
| Sustained Use Discounts | No (Reserved Instances instead) | Automatic |
| Preemptible/Spot | Spot Instances | Preemptible VMs |
Our recommendation: If you need highly specific instance configurations, GCP's custom machine types offer more flexibility. For standardized workloads with predictable usage, AWS Reserved Instances can provide better savings.
Containerization
Both platforms offer robust container solutions:
- AWS: EKS (Elastic Kubernetes Service), ECS (Elastic Container Service), Fargate
- GCP: GKE (Google Kubernetes Engine), Cloud Run
GKE is often considered the gold standard for managed Kubernetes, which makes sense given that Google originally developed Kubernetes. If Kubernetes is central to your architecture, GCP has a slight edge.
Database Services
Relational Databases
AWS RDS supports:
- PostgreSQL
- MySQL
- MariaDB
- Oracle
- SQL Server
- Amazon Aurora
GCP Cloud SQL supports:
- PostgreSQL
- MySQL
- SQL Server
AWS offers more database engine options, including their proprietary Aurora which delivers impressive performance for MySQL and PostgreSQL-compatible workloads.
NoSQL Options
- AWS: DynamoDB, DocumentDB, ElastiCache
- GCP: Firestore, Bigtable, Memorystore
DynamoDB is a battle-tested, serverless NoSQL solution that's hard to beat for most use cases. However, if you're building mobile applications, Firestore's real-time sync capabilities are exceptional.
Machine Learning and AI
This is where GCP truly shines. Google's expertise in AI/ML translates to:
- TensorFlow integration (Google created TensorFlow)
- Vertex AI for end-to-end ML workflows
- BigQuery ML for SQL-based machine learning
- Advanced pre-trained models for vision, language, and translation
AWS offers SageMaker and various AI services, but GCP's ML tools are generally considered more developer-friendly and deeply integrated.
Our recommendation: For AI/ML-heavy workloads, GCP provides a more cohesive experience. For traditional enterprise workloads, AWS's breadth of services is advantageous.
Networking
Global Infrastructure
- AWS: 33 regions, 105 availability zones
- GCP: 37 regions, 112 availability zones
Both have excellent global coverage, but GCP's private fiber network provides consistently low latency for global applications.
Load Balancing
GCP's global load balancer is a single anycast IP that routes traffic globally—a simpler model than AWS's regional approach. For truly global applications, this can simplify architecture significantly.
Pricing Considerations
Billing Models
AWS uses:
- On-Demand pricing
- Reserved Instances (1-3 year commitments)
- Spot Instances (up to 90% discount)
- Savings Plans
GCP uses:
- On-Demand pricing
- Committed Use Discounts (1-3 year commitments)
- Preemptible VMs (up to 80% discount)
- Sustained Use Discounts (automatic)
GCP's sustained use discounts automatically apply when you run instances for more than 25% of a month—no commitment required. This can lead to meaningful savings for consistent workloads without the complexity of reserved capacity planning.
Egress Costs
Data transfer out of the cloud is a significant cost consideration. Generally:
- GCP tends to have lower egress costs
- Both offer free egress to certain services within the same region
- CDN and dedicated interconnect can reduce costs on both platforms
Developer Experience
Console and CLI
AWS's console has more features but can feel overwhelming. GCP's console is generally considered cleaner and more intuitive, though with fewer advanced features.
Both offer robust CLIs (aws and gcloud), and both support Infrastructure as Code through Terraform and their native tools (CloudFormation for AWS, Deployment Manager for GCP).
Documentation
GCP's documentation is often praised for its clarity and modern presentation. AWS has more documentation due to its larger service catalog, but it can be harder to navigate.
When to Choose AWS
Choose AWS when you need:
- Maximum service variety: AWS has a service for almost everything
- Enterprise compliance: More compliance certifications and established enterprise relationships
- Specific AWS services: Aurora, Lambda (with broad triggers), or services with no GCP equivalent
- Existing AWS expertise: Your team is already trained on AWS
When to Choose GCP
Choose GCP when you need:
- Advanced data analytics: BigQuery is unmatched for large-scale data analysis
- Machine learning: Superior ML tools and TensorFlow integration
- Kubernetes: GKE provides the best managed Kubernetes experience
- Global applications: Superior global network and load balancing
- Cost efficiency: Automatic sustained use discounts and competitive pricing
Our Experience at Deckforge
At Deckforge Engineering, we've deployed production systems on both platforms. Our approach:
- Evaluate requirements first: What does your application actually need?
- Consider existing expertise: Training costs and learning curves matter
- Multi-cloud is an option: For some clients, we use both platforms strategically
- Start small: Begin with core services and expand as needed
Conclusion
There's no universal "best" cloud provider. AWS offers breadth and market maturity, while GCP provides cutting-edge data and ML capabilities with a cleaner developer experience.
For most businesses, either platform can serve your needs well. The key is understanding your specific requirements—compute needs, data volumes, ML ambitions, team expertise, and budget constraints—and choosing accordingly.
Need help evaluating cloud platforms for your project? Contact us to discuss your requirements with our engineering team.