Dear reader, as your organization moves vital operations to the cloud, choosing the right provider is a complex yet critical decision. In this technology brief, I compare the two dominant infrastructure-as-a-service platforms—AWS and GCP—across 12 assessment criteria relevant to large enterprises. My goal is to provide unbiased evidence on their technical capabilities, business models, roadmaps and ideal use cases so you can craft an efficient multi-cloud strategy leveraging strengths of both.
I. Cloud Market Overview – Staggered Launches Result in Divergent Platforms
Let‘s recap key milestones in the dynamic public cloud industry…
AWS Origin Story
- Launched simple S3 storage + EC2 compute in 2006
- First mover status enabled years of uncontested growth
- By 2015, AWS neared $10B revenue with 30%+ cloud market share
GCP Playing Catch-up
- Late entry to market in 2008 initially slowed adoption
- Priority on big data, machine learning and APIs
- Revenue topped $10B in 2020; now firmly second behind AWS
Despite staggered timelines, both evolved into full-fledged platforms covering hundreds of enterprise IT needs:
| Category | AWS Capabilities | GCP Capabilities |
|---|---|---|
| Infrastructure | 170+ compute, storage and network services | 80+ core infrastructure services |
| Databases | Managed relational, key-value, graph, time-series | Cloud SQL, Firebase/Firestore, Spanner |
| Big Data | Redshift, EMR, Kinesis, Lake Formation | BigQuery, Dataflow, Dataproc, DataFusion |
| Machine Learning | SageMaker, Forecast, Personalize, Fraud Detector | Vertex AI, AutoML Tabular/Text/Video, TPUs |
| Management | Console, Config, CloudWatch, CloudTrail, Control Tower | Console, Config Manager, Monitoring, Logging, Access Transparency |
| DevOps | CodeCommit, CodeBuild, CodeDeploy, CodePipeline | Cloud Build, Source Repos, Cloud CDN, Argo Workflows |
Let‘s dig deeper into the technical and business differentiators…
II. Comparing Core Infrastructure – Global Scale vs Advanced Services
While similarities exist across basic functionality, architectural philosophies diverge noticeably…
AWS – Unmatched Scale and Reliability
- 84 Availability Zones (AZs) within 26 Regions
- The broadest and deepest cloud infrastructure globally
- Extensive compliance certification coverage
GCP – Strategic Technology Focus Areas
- 73 Zones within 24 Regions – trailing but catching up
- Specialized differentiation across ML, analytics, APIs
- Very competitive on security, privacy and sustainability
Drilling down, we see alternative approaches play out in the IaaS foundation:
| Category | AWS | GCP |
|---|---|---|
| Virtual Machines | EC2 (Elastic Compute) | Compute Engine |
| Object Storage | S3 (Simple Storage) | Cloud Storage |
| Block Storage | EBS (Elastic Block Store) | Persistent Disk |
| File Storage | EFS (Elastic File System) | Filestore |
| Load Balancing | ELB (Elastic Load Balancing) | Cloud Load Balancing |
| DNS Service | Route 53 | Cloud DNS |
While meeting 80%+ of typical IaaS needs, AWS out-invests GCP in niche services like batch computing, workflow orchestration and media processing. GCP counters with deeper open source leveraging Kubernetes, Istio service mesh and high-throughput data ingestion.
Both secure infrastructure through granular IAM policies, VPC service segregation and ISO/SOC-certified hardware. GCP recently introduced Confidential VMs leveraging AMD processors to encrypt in-memory data.
For core networking, storage and databases, performance prevails on either option:
| Workload | AWS | GCP |
|---|---|---|
| Max SQL IOPS | 320,000 (RDS on EC2) | 450,000 (N2 High Memory) |
| Max NoSQL IOPS | 1.5M (DynamoDB) | 2.8M (Datastore) |
| Max Filesystem IOPS | 100,000 (GP3 Storage) | 1M (Zonal SSD Persistent Disk) |
| Load Balancer Latency | 30ms (NLB) | 35ms (Network TCP) |
On regional availability, AWS maintains leadership reaching 87 metro areas globally – ideal for applications requiring local data residency. GCP trails at 34 metros but actively expands across Europe, Asia and Latin America.
III. Specialized Strengths – ML/AI Supremacy vs Unmatched Integrations
Delving beyond infrastructure, vast platform ecosystems have emerged…
GCP – ML/AI Supremacy
With TensorFlow, Kubeflow and AutoML originations, GCP dominates machine learning:
- Vertex AI – Unified MLOps environment
- BigQuery ML – SQL interface for creating models
- Cloud TPUs – Hardware acceleration for model training
- Exclusive access to latest Google ML research
AWS – Unmatched Integrations
AWS cultivated a partner ecosystem unrivaled in breadth and depth:
- Security – CloudWatch, GuardDuty, Macie
- Migration – DMS, Application Discovery Service
- Containers – ECS, EKS integrates major tools like Datadog
- Industry Solutions – Amazon Retail, Amazon Health, Amazon FinSpace
RICH: No public cloud can match the vertical expertise encoded into these industry platforms.
For startups and mid-market firms, AWS third-party maturity lowers risks of adopting cloud infrastructure. With GCP, you gain groundbreaking ML but sacrifice some configurability.
IV. Comparing Business Factors – Pricing, Support and Strategic Vision
Beyond technical specifics, commercial influences matter greatly…
Pricing – Devil in the Details
Abstracting complex rate cards, a typical multi-Region 100 VM production footprint would cost:
| Service | AWS | GCP | Effort to Optimize |
|-|-|-|
| EC2 m5.2xlarge VMs | ~$95K/month | ~$77K/month | High |
| GCP n2-standard-8 VMs | | | Low |
Apply reservations, sustained use discounts and specialized instances to trim ~30% expenses on either. Cost efficiency favors time investment – complex on AWS but rewarded, straightforward on GCP.
Support – Open Source vs Premium Models
AWS offers business, enterprise and professional tiers with escalated response times and designated account managers costing 10-15% extra.
GCP includes email and chat with standard contracts – no upcharges – but lacks account management. However, GCP contributes extensively to communities around solutions like Kubernetes and Istio. AWS cultivates more proprietary capabilities around SageMaker, Alexa and Kindle.
Strategic Vison – Ever-Expanding Breadth vs Technical Innovations
AWS seems destined to offer every infrastructure service imaginable; GCP strategically funds paradigm shifts in data analytics, ML and Web 3.0.
Google Cloud also presses the industry toward carbon neutrality and ethical AI advancements. In contrast, Amazon and AWS face external criticism around sustainability and workplace culture.
V. Recommendations – Crafting an Intelligent Multi-Cloud Strategy
Synthesizing insights from this evaluation, I suggest a multi-cloud approach maximizing strengths of both platforms:
Eight Best Practices for Multi-Cloud Excellence
- Standardize environments & tools for consistency across clouds
- Encapsulate services in containers & API layers for portability
- Classify workloads by infrastructure sensitivity, data regulations
- Analyze cost, capacity and feature trade-offs continuously
- Automate deployments, scalability and failover recovery
- Govern through role segregation, tagging conventions and change controls
- Orchestrate unified visibility via management tools across clouds
- Skill for cloud platforms generally – avoid custom integration logic
Strategic Workload Placement Guide
| Application Type | Target Cloud Vendor | Rationale |
|---|---|---|
| Mobile apps, gaming | GCP | Cloud Firestore, Cloud CDN, Apigee |
| Big data pipelines | GCP | BigQuery, Dataflow stream processing |
| General business systems | AWS | Broader ecosystem, enterprise support |
| ML model development | GCP | Vertex AI, Cloud TPU pods |
| Global digital presence | AWS | Wider region footprint |
| Digital marketing analytics | GCP | Looker BI, Data Studio |
| Security operations, fraud detection | AWS | GuardDuty threat detection |
This blueprint aligns your portfolio to cloud provider strengths today while keeping future migration options open.
VI. Closing Thoughts
Neither AWS nor GCP can fulfill 100% of enterprise cloud infrastructure needs exclusively.
AWS leads in services count, partner integrations and global reach – fuelling startups and established IT organizations equally with robust foundational technologies. GCP counterpunches with concentrated investments in ML, analytics and market-disruptive capabilities.
Approaching your cloud program as collaborators rather than competitors extracts the most business value short-term while retaining flexibility long-term. A multi-cloud management platform like Morpheus or VMware CloudHealth simplifies day-to-day operations by centralizing visibility, security policies and workload orchestration.
I hope illuminating key technical and business differences between AWS and GCP – plus recommendations on intelligently leveraging both platforms – proves useful navigating your own cloud transformations in 2023 and beyond!
This independent cloud infrastructure briefing brought to you by Rich Bowen – 25 year technology executive and cloud architect.
