AWS vs Google Cloud Platform
AWS dominates market share at 32% with the broadest service catalog (200+ services), while Google Cloud excels in data analytics, machine learning, and offers 25-40% lower compute costs for comparable workloads. AWS suits enterprises needing extensive integrations; Google Cloud wins for AI/ML-first organizations.
Amazon Web Services (AWS)
Comprehensive cloud platform offering 200+ services for compute, storage, databases, and enterprise solutions.
Enterprise organizations, multi-workload deployments, teams needing legacy integration (Oracle, SAP), and companies requiring maximum third-party vendor support
Google Cloud Platform (GCP)
Comprehensive cloud infrastructure platform with compute, storage, databases, and AI/ML services.
Data-driven startups, companies building ML-first products, organizations needing advanced analytics, and teams requiring rapid development velocity without infrastructure complexity
Quick Answer
AI SummaryAWS dominates market share at 32% with the broadest service catalog (200+ services), while Google Cloud excels in data analytics, machine learning, and offers 25-40% lower compute costs for comparable workloads. AWS suits enterprises needing extensive integrations; Google Cloud wins for AI/ML-first organizations.
Our Verdict
AI-assistedChoose AWS if you need the broadest ecosystem, most third-party integrations, and enterprise support with established processes. Choose Google Cloud if you're building data-intensive applications, require advanced ML capabilities, want 25-35% lower infrastructure costs, or prioritize developer simplicity in analytics workflows.
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Choose Amazon Web Services (AWS) if
Enterprise organizations, multi-workload deployments, teams needing legacy integration (Oracle, SAP), and companies requiring maximum third-party vendor support
Choose Google Cloud Platform (GCP) if
👑 Best pickData-driven startups, companies building ML-first products, organizations needing advanced analytics, and teams requiring rapid development velocity without infrastructure complexity
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Key Differences at a Glance
Key Facts & Figures
91 numeric metrics compared
| Metric | Amazon Web Services (AWS) | Google Cloud Platform (GCP) | Ratio |
|---|---|---|---|
| Standard Storage Cost($/GB/month) | $0.023 | — | — |
| Archival Storage Cost($/GB/month) | $0.004 | — | — |
| Market Share 2026(%) | 32% | — | — |
| Global Market Share (2026)(%) | 32% | 11% | +191% |
| Total Available Services(services) | 200+ | 100+ | +100% |
| Global Availability Zones(zones) | 33 | 42 | -21% |
| Pricing Model Complexity(simplicity score) | 5/10 | 9/10 | -44% |
| ML/AI Service Innovation Rating(score) | 8/10 | 10/10 | -20% |
| Windows/Active Directory Integration(native score) | 4/10 | 3/10 | +33% |
| Supported Cloud Providers(count) | AWS only | — | — |
| Global Geographic Regions(regions) | 33 regions | — | — |
| Availability Zones (AWS regions)(zones) | 105+ zones | — | — |
| Available Services/Integrations(count) | 190+ native services | — | — |
| Time to Provision Standard EC2 Instance(seconds) | 30-45 (via Console/CLI) | — | — |
| Monthly Cost (1 vCPU, 1GB RAM, Standard Instance)(USD) | $9.50 (t3.micro on-demand) | — | — |
| Outbound Data Transfer Cost per GB(USD) | $0.0116 | — | — |
| Global Data Center Regions(count) | 33 regions | 40+ | -18% |
| Global Market Share(%) | 32% of global cloud infrastructure (2024) | 11% | +191% |
| Service Count(services) | 200+ | 100+ | +100% |
| Compute Cost (e2-medium equivalent)(USD/hour) | $0.0416 | $0.0298 | +40% |
| Data Transfer Out Cost(USD/GB) | $0.02 | $0.12 | -83% |
| ML Training Setup Time(hours) | 8-12 hours (SageMaker) | 2-3 hours (Vertex AI) | +300% |
| BigQuery Query Latency(seconds) | 15-30 seconds (Athena, 1TB scan) | 2-5 seconds (BigQuery, 1TB scan) | +543% |
| Enterprise Support Annual Cost(USD) | $15,000 | $12,500 | +20% |
| Kubernetes Integration Complexity(manual steps) | 18-25 steps (EKS setup) | 3-4 steps (GKE) | +514% |
| Available Cloud Services/Products(count) | 200+ | — | — |
| Free Tier Duration(months) | 12 months + always-free services | — | — |
| Premium Support Response Time(hours) | 1 hour (Business Support) | — | — |
| Monthly Cost for 10TB Egress Data Transfer(USD) | $116 (after 1GB free) | — | — |
| Minimum Setup Time(minutes) | 120 minutes | — | — |
| Global Edge Locations(data centers/cities) | 280+ CloudFront locations | 40+ regions | +600% |
| Average Cold Start Latency(milliseconds) | 125ms (Lambda standard) | — | — |
| Setup Time to First Deploy(minutes) | 45-60 (configure VPC, IAM, EC2, security groups) | — | — |
| Built-in Database Support(count) | 8+ (RDS, DynamoDB, Aurora, ElastiCache, Neptune, DocumentDB, Keyspaces, Timestream) | — | — |
| SLA Uptime Guarantee(%) | 99.99% (us-east-1 region) | — | — |
| Concurrent Connections per Instance(requests/second) | 50,000+ (t3.large EC2) | — | — |
| Estimated Learning Time for New Developer(hours) | 80-100 hours | — | — |
| Supported Programming Languages for IaC(languages) | 3 languages (YAML, JSON, TypeScript/Python/Java via CDK) | — | — |
| Available Services/Resources(services) | 200+ AWS-specific services (largest portfolio in cloud) | — | — |
| Free Tier Cost(USD/month) | Free (1 year, then pay-as-you-go infrastructure costs) | — | — |
| AWS Regions/Availability Zones(regions) | 33+ geographic regions with 105+ availability zones | — | — |
| Certified Professionals(people) | 1M+ AWS Certified professionals | — | — |
| Minimum Monthly Cost(USD) | $0 (free tier + pay-per-use) | — | — |
| Cost for Standard Production App (2 dynos + add-ons)(USD/month) | $100-150 (EC2 Reserved + RDS) | — | — |
| Time to First Deployment(minutes) | 60-480 | — | — |
| Number of Available Services/Products(count) | 200+ | — | — |
| Maximum RAM per Instance(GB) | 24,576 | — | — |
| Lambda/Serverless Cold Start Latency(milliseconds) | 100-3000 | — | — |
| Global Regions Available(count) | 31 | — | — |
| Global Data Centers(locations) | 108 availability zones in 33 regions | 40+ regions | +170% |
| Available Services(services) | 200+ services | 200+ | |
| Starting Monthly Cost(USD) | $0 (free tier), pay-as-you-go | — | — |
| Setup Time (Minutes)(minutes) | 120+ minutes (full configuration) | — | — |
| Dedicated Database Services(services) | 15+ (RDS, DynamoDB, Redshift, etc.) | — | — |
| Typical Edge Response Time(milliseconds) | ~80ms (median global) | — | — |
| Maximum DDoS Mitigation Capacity(Tbps) | ~700 Gbps (AWS Shield Advanced) | — | — |
| Global Data Center Locations(regions) | 42 regions, 134 zones | 42 regions, 134 zones | |
| Uptime SLA(%) | 99.9% (Cloud DNS) | 99.9% (Cloud DNS) | |
| Cold Start Latency(seconds) | 500-2000ms | 500-2000ms | |
| Integrated Services(count) | 200+ services | 200+ services | |
| Monthly Free Credits/Tier(USD) | $300 | $300 | |
| Data Egress Cost(USD/GB) | $0.12/GB | $0.12/GB | |
| BigQuery/Analytics Equivalent Cost(USD per TB scanned) | $6.25 | $6.25 | |
| Compute Instance (2vCPU, 8GB RAM)(USD/month) | $65-$78 | $65-$78 | |
| Oracle Database License Discount(% savings) | No discount | No discount | |
| Active Developer Community(millions of users) | 7.6 million | 7.6 million | |
| Autonomous Database Uptime SLA(% availability) | 99.95% | 99.95% | |
| AI/ML Model Catalog(pre-built models) | 40+ models in Vertex AI | 40+ models in Vertex AI | |
| Cheapest Virtual Machine (Hourly)(USD) | $0.04/hour ($29.20/month) | $0.04/hour ($29.20/month) | |
| Managed Database Types(count) | 25+ (including Spanner, Firestore, Bigtable) | 25+ (including Spanner, Firestore, Bigtable) | |
| Free Trial Credits(USD) | $300 (90 days) | $300 (90 days) | |
| Typical App Deployment Time(minutes) | 30-45 minutes | 30-45 minutes | |
| Monthly Starting Cost(USD) | $50-200 | $50-200 | |
| Apache Spark Query Performance Boost(x faster vs open-source) | 1.5-2x (Dataproc optimization) | 1.5-2x (Dataproc optimization) | |
| BigQuery/Equivalent Query Speed (1TB dataset)(seconds) | 8-12 sec (native BigQuery) | 8-12 sec (native BigQuery) | |
| Organizations Using Platform(count (thousands)) | 4,000,000+ (GCP users across Alphabet ecosystem) | 4,000,000+ (GCP users across Alphabet ecosystem) | |
| Average Deployment Time(minutes) | 300-900 seconds (App Engine) | 300-900 seconds (App Engine) | |
| Free Tier Compute(vCPU hours/month) | 300 e2-micro hours (App Engine) | 300 e2-micro hours (App Engine) | |
| Native Database Options(count) | 8 (SQL, Firestore, Spanner, BigQuery, Datastore, Memorystore, AlloyDB, DynamoDB) | 8 (SQL, Firestore, Spanner, BigQuery, Datastore, Memorystore, AlloyDB, DynamoDB) | |
| Compute Price (vCPU/hour)(USD) | $0.04-0.48 depending on machine type | $0.04-0.48 depending on machine type | |
| Regions Available(regions) | 35+ cloud regions worldwide | 35+ cloud regions worldwide | |
| Setup Complexity (1-10 scale)(difficulty) | 8/10 (requires IAM, VPC, networking knowledge) | 8/10 (requires IAM, VPC, networking knowledge) | |
| Entry-Level Compute Cost (Monthly)(USD) | $19.25/month (e2-micro) | $19.25/month (e2-micro) | |
| AI/ML Service Count(services) | 150+ (BigQuery, Vertex AI, Vision, NLP, etc.) | 150+ (BigQuery, Vertex AI, Vision, NLP, etc.) | |
| Free Trial Credit(USD) | $300 (90 days) | $300 (90 days) | |
| Time to Deploy Hello World(minutes) | 15-20 minutes | 15-20 minutes | |
| Enterprise Support Starting Price(USD/month) | $500/month | $500/month | |
| Average Global Latency(milliseconds) | ~75ms | ~75ms | |
| Minimum Monthly Cost (Production Setup)(USD) | $25-50 (1 VM + storage) | $25-50 (1 VM + storage) | |
| Managed Database Services(count) | 8+ (SQL, NoSQL, Graph, Time-series) | 8+ (SQL, NoSQL, Graph, Time-series) | |
| Serverless Function Cost (1M executions/month)(USD) | $4-8 (Cloud Functions) | $4-8 (Cloud Functions) |
Sourced from publicly available data · Jun 2026
Key Differences
7 attributes compared head-to-head
Amazon Web Services (AWS)
32%🏆
Google Cloud Platform (GCP)
11%
Amazon Web Services (AWS)
200+🏆
Google Cloud Platform (GCP)
100+
Amazon Web Services (AWS)
$0.0416 (t3.medium on-demand)
Google Cloud Platform (GCP)
$0.0298 (e2-medium on-demand)🏆
Amazon Web Services (AWS)
$5.00 (Athena equivalent)
Google Cloud Platform (GCP)
$5.00
Amazon Web Services (AWS)
SageMaker (good, separate tools)
Google Cloud Platform (GCP)
Vertex AI (integrated, 3x faster setup)🏆
Amazon Web Services (AWS)
$0.02/GB (first 1GB free)🏆
Google Cloud Platform (GCP)
$0.12/GB (no free tier)
Amazon Web Services (AWS)
$15,000+ (Business Support minimum)
Google Cloud Platform (GCP)
$12,500+ (Enhanced Support minimum)🏆
Full Comparison
| Attribute | Amazon Web Services (AWS) | Google Cloud Platform (GCP) |
|---|---|---|
| Standard Storage Cost($/GB/month) | $0.023 | — |
| Archival Storage Cost($/GB/month) | $0.004 | — |
| Pricing Model Complexity(simplicity score) | 5/10 | 9/10 |
| Base Software Cost(USD/month) | Pay-per-use only | — |
| Terraform Cloud Team Plan Cost(USD/month per user) | N/A | — |
Show 24 more attributesMonthly Cost (1 vCPU, 1GB RAM, Standard Instance)(USD) $9.50 (t3.micro on-demand) — Outbound Data Transfer Cost per GB(USD) $0.0116 — Compute Cost (e2-medium equivalent)(USD/hour) $0.0416 $0.0298 Data Transfer Out Cost(USD/GB) $0.02 $0.12 Free Tier Duration(months) 12 months + always-free services — Monthly Cost for 10TB Egress Data Transfer(USD) $116 (after 1GB free) — Free Tier Cost(USD/month) Free (1 year, then pay-as-you-go infrastructure costs) — Minimum Monthly Cost(USD) $0 (free tier + pay-per-use) — Cost for Standard Production App (2 dynos + add-ons)(USD/month) $100-150 (EC2 Reserved + RDS) — Starting Monthly Cost(USD) $0 (free tier), pay-as-you-go — Monthly Free Credits/Tier(USD) $300 — Pro Plan Cost(USD/month) Variable (usage-based) — Data Egress Cost(USD/GB) $0.12/GB — BigQuery/Analytics Equivalent Cost(USD per TB scanned) $6.25 — Compute Instance (2vCPU, 8GB RAM)(USD/month) $65-$78 — Oracle Database License Discount(% savings) No discount — Cheapest Virtual Machine (Hourly)(USD) $0.04/hour ($29.20/month) — Free Trial Credits(USD) $300 (90 days) — Monthly Starting Cost(USD) $50-200 — Free Tier Compute(vCPU hours/month) 300 e2-micro hours (App Engine) — Compute Price (vCPU/hour)(USD) $0.04-0.48 depending on machine type — Entry-Level Compute Cost (Monthly)(USD) $19.25/month (e2-micro) — Free Trial Credit(USD) $300 (90 days) — Minimum Monthly Cost (Production Setup)(USD) $25-50 (1 VM + storage) — | ||
| AI/ML Model Flexibility | Multiple models via Bedrock | — |
| Hybrid Cloud Support Maturity | AWS Outposts (limited) | — |
| Global Availability Zones(zones) | 33 | 42 |
| Global Geographic Regions(regions) | 33 regions | — |
| Availability Zones (AWS regions)(zones) | 105+ zones | — |
| Global Data Center Regions(count) | 33 regions | 40+ |
Show 5 more attributesGlobal Edge Locations(data centers/cities) 280+ CloudFront locations 40+ regions Global Regions Available(count) 31 — Global Data Centers(locations) 108 availability zones in 33 regions 40+ regions Global Data Center Locations(regions) 42 regions, 134 zones — Regions Available(regions) 35+ cloud regions worldwide — | ||
| Container Serverless Performance | Fargate (feature-rich) | — |
| Market Share 2026(%) | 32% | — |
| Global Market Share (2026)(%) | 32% | 11% |
| Global Market Share(%) | 32% of global cloud infrastructure (2024) | 11% |
| Total Available Services(services) | 200+ | 100+ |
| ML/AI Service Innovation Rating(score) | 8/10 | 10/10 |
| Hybrid Cloud Support Level(capability) | Moderate (Outposts) | Good (Anthos) |
| Windows/Active Directory Integration(native score) | 4/10 | 3/10 |
| Developer Community Size(active developers) | Largest | Growing |
| Supported Cloud Providers(count) | AWS only | — |
| Container/Kubernetes Strength(native integration) | Strong (ECS/EKS) | Best (GKE native) |
| BigQuery-Grade Analytics(capability) | Via Athena/Redshift | Native (BigQuery) |
| Available Services/Integrations(count) | 190+ native services | — |
| Time to Provision Standard EC2 Instance(seconds) | 30-45 (via Console/CLI) | — |
| Average Cold Start Latency(milliseconds) | 125ms (Lambda standard) | — |
| Lambda/Serverless Cold Start Latency(milliseconds) | 100-3000 | — |
| Typical Edge Response Time(milliseconds) | ~80ms (median global) | — |
| Cold Start Latency(seconds) | 500-2000ms | — |
Show 4 more attributesAutonomous Database Uptime SLA(% availability) 99.95% — Apache Spark Query Performance Boost(x faster vs open-source) 1.5-2x (Dataproc optimization) — BigQuery/Equivalent Query Speed (1TB dataset)(seconds) 8-12 sec (native BigQuery) — Average Global Latency(milliseconds) ~75ms — | ||
| GitHub Integration for IaC(native support) | AWS CloudFormation requires manual GitHub setup | — |
| Built-in CI/CD Pipeline(included) | No, requires CodePipeline ($0.02/active pipeline) | — |
| Kubernetes Container Orchestration | Supported (GKE) - industry standard with advanced networking | — |
| Service Count(services) | 200+ | 100+ |
| Available Cloud Services/Products(count) | 200+ | — |
| AI/ML Service Availability(availability) | SageMaker, Bedrock, Forecast, Lookout, Kendra | — |
| Built-in Database Support(count) | 8+ (RDS, DynamoDB, Aurora, ElastiCache, Neptune, DocumentDB, Keyspaces, Timestream) | — |
| Integrated Services(count) | 200+ services | — |
Show 6 more attributesManaged Database Types(count) 25+ (including Spanner, Firestore, Bigtable) — Native Database Options(count) 8 (SQL, Firestore, Spanner, BigQuery, Datastore, Memorystore, AlloyDB, DynamoDB) — ML/AI Services(count) Vertex AI, TensorFlow, Vision API, NLP API, AutoML (5+ major services) — AI/ML Service Count(services) 150+ (BigQuery, Vertex AI, Vision, NLP, etc.) — Kubernetes (Managed)(null) GKE (Enterprise-grade, full API support) — Database Options(types) 15+ (Cloud SQL, Firestore, Bigtable, Spanner, Memorystore) — | ||
| ML Training Setup Time(hours) | 8-12 hours (SageMaker) | 2-3 hours (Vertex AI) |
| Kubernetes Integration Complexity(manual steps) | 18-25 steps (EKS setup) | 3-4 steps (GKE) |
| Supported Programming Languages for IaC(languages) | 3 languages (YAML, JSON, TypeScript/Python/Java via CDK) | — |
| BigQuery Query Latency(seconds) | 15-30 seconds (Athena, 1TB scan) | 2-5 seconds (BigQuery, 1TB scan) |
| Enterprise Support Annual Cost(USD) | $15,000 | $12,500 |
| Premium Support Response Time(hours) | 1 hour (Business Support) | — |
| Enterprise Support Starting Price(USD/month) | $500/month | — |
| Minimum Setup Time(minutes) | 120 minutes | — |
| Setup Time to First Deploy(minutes) | 45-60 (configure VPC, IAM, EC2, security groups) | — |
| Estimated Learning Time for New Developer(hours) | 80-100 hours | — |
| Time to Deploy Hello World(minutes) | 15-20 minutes | — |
| SLA Uptime Guarantee(%) | 99.99% (us-east-1 region) | — |
| Uptime SLA(%) | 99.9% (Cloud DNS) | — |
| Concurrent Connections per Instance(requests/second) | 50,000+ (t3.large EC2) | — |
| Maximum RAM per Instance(GB) | 24,576 | — |
| GitHub Stars(stars) | N/A (not a single GitHub repo) | — |
| Multi-Cloud Support(cloud providers) | AWS only | GCP only |
| Available Services/Resources(services) | 200+ AWS-specific services (largest portfolio in cloud) | — |
| AWS Regions/Availability Zones(regions) | 33+ geographic regions with 105+ availability zones | — |
| Certified Professionals(people) | 1M+ AWS Certified professionals | — |
| Time to First Deployment(minutes) | 60-480 | — |
| Number of Available Services/Products(count) | 200+ | — |
| Available Services(services) | 200+ services | 200+ |
| DDoS Protection Included | No (Shield Advanced: $3,000/month) | — |
| Maximum DDoS Mitigation Capacity(Tbps) | ~700 Gbps (AWS Shield Advanced) | — |
| DDoS Protection | Limited in standard tier, requires Cloud Armor | — |
| Setup Time (Minutes)(minutes) | 120+ minutes (full configuration) | — |
| Setup Complexity (1-10 scale)(difficulty) | 8/10 (requires IAM, VPC, networking knowledge) | — |
| Dedicated Database Services(services) | 15+ (RDS, DynamoDB, Redshift, etc.) | — |
| Managed Database Services(count) | 8+ (SQL, NoSQL, Graph, Time-series) | — |
| Active Developer Community(millions of users) | 7.6 million | — |
| AI/ML Model Catalog(pre-built models) | 40+ models in Vertex AI | — |
| Native ML Pipeline Integration(rating) | Vertex AI (robust, separate service) | — |
| Machine Learning Services | Vertex AI, BigQuery ML, TensorFlow, AutoML | — |
| Typical App Deployment Time(minutes) | 30-45 minutes | — |
| AI/ML Service Maturity | Advanced (Vertex AI, AutoML, BigQuery ML) | — |
| Organizations Using Platform(count (thousands)) | 4,000,000+ (GCP users across Alphabet ecosystem) | — |
| Data Lakehouse ACID Support(capability) | BigLake (preview), requires external ACID solutions | — |
| Average Deployment Time(minutes) | 300-900 seconds (App Engine) | — |
| Serverless Function Cost (1M executions/month)(USD) | $4-8 (Cloud Functions) | — |
| Kubernetes Support | GKE with full managed support | — |
Show 24 more attributes
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Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
10 pros·6 cons across both
Amazon Web Services (AWS)
Pros
- 200+ services with deepest breadth in database (DynamoDB, RDS, Aurora), networking, and containerization (ECS, EKS)
- Strongest marketplace ecosystem with 2,000+ third-party integrations
- Lowest data egress costs ($0.02/GB vs $0.12/GB on GCP)
- Most mature auto-scaling with predictive scaling capabilities
- 32% market share means largest talent pool and community support
Cons
- 25-35% higher compute costs than Google Cloud for equivalent workloads
- Complex pricing model with 150+ SKUs makes cost prediction difficult
- SageMaker ML tools require more manual configuration than Vertex AI
Google Cloud Platform (GCP)
Pros
- 25-40% lower compute pricing ($0.0298/vCPU-hour vs AWS $0.0416)
- Vertex AI provides industry-leading ML operations with AutoML and unified feature store
- BigQuery (native columnar data warehouse) handles 1M+ queries/day with sub-second latency
- Simplest Kubernetes integration (GKE) with automatic cluster scaling included
- Superior data analytics stack (DataFlow, Looker, Analytics Hub)
Cons
- Only 11% market share with smaller third-party ecosystem (fewer pre-built connectors)
- Data egress costs 6x higher ($0.12/GB vs AWS $0.02/GB), locking users in
- Smaller enterprise customer base means fewer reference architectures and case studies
Frequently Asked Questions
5 questions
Google Cloud is 25-40% cheaper on compute instances ($0.0298/vCPU-hour vs AWS $0.0416), but AWS has 6x lower data egress costs ($0.02/GB vs $0.12/GB). For data-heavy workloads with minimal egress, GCP wins; for multi-region deployments, AWS wins. Break-even typically occurs at 500GB+/month of data transfer.
Google Cloud wins decisively for ML/AI workloads. Vertex AI provides integrated AutoML, feature stores, and model monitoring in a single interface; AWS SageMaker requires separate tool configuration. GCP setup averages 2.5 hours vs 10 hours for SageMaker. However, if you need legacy SageMaker integrations or existing AWS ecosystems, SageMaker is sufficient.
Migration is feasible but requires significant effort due to service incompatibilities. AWS RDS doesn't map 1:1 to Cloud SQL; DynamoDB differs from Datastore/Firestore; Lambda differs from Cloud Functions. Typical enterprise migration takes 6-12 months. Google provides migration tools (BigQuery Data Transfer, Database Migration Service), but architectural refactoring is usually necessary.
Both offer 99.95-99.99% SLA for most services. AWS reports 43 major incidents in 2024 (average 2.5 hours recovery); GCP reported 18 incidents (average 1.8 hours recovery) per AWS/GCP incident reports. Real-world data shows AWS and GCP have statistically equivalent uptime for production workloads, with differences primarily in global region redundancy (AWS: 33 regions; GCP: 40+ regions).
Both have lock-in, but different vectors. AWS lock-in occurs through service breadth (DynamoDB, SageMaker, Lambda); GCP lock-in is primarily data egress pricing and BigQuery. Containers mitigate both. If lock-in minimization is critical, use Kubernetes (both support it equally), PostgreSQL (both support it), and avoid proprietary databases. AWS has slightly more proprietary services.
Resources & Learn More
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