Choosing a cloud provider is one of the most consequential infrastructure decisions a company makes. Migrating between providers after significant investment costs six figures and months of engineering time. Yet most teams choose based on what they already know or what their first hire happens to be experienced with, rather than evaluating which provider actually fits their workload, budget, and growth trajectory.
This guide provides a practical comparison of AWS, Google Cloud Platform (GCP), and Microsoft Azure for business applications, focusing on the criteria that actually matter rather than feature-list comparisons.
Evaluating Core Compute and Container Services
Compute is typically 40% to 60% of your cloud bill. The differences between providers here are material.
Virtual machines. AWS EC2 has the broadest instance type selection: over 750 instance types covering every conceivable combination of CPU, memory, GPU, and storage. GCP Compute Engine offers custom machine types where you specify exact vCPU and memory ratios, which can reduce waste when standard sizes do not match your workload. Azure Virtual Machines integrates deeply with Windows workloads and Active Directory. For most web applications, all three are equivalent. The differentiation emerges in specialized workloads: EC2 has the best GPU instance availability for ML training, GCP’s custom types save 20% to 35% on memory-heavy workloads, and Azure’s reserved instances offer the deepest discounts for committed spend (up to 72% off on-demand pricing for 3-year commitments).
Managed Kubernetes. Amazon EKS, Google GKE, and Azure AKS all provide managed Kubernetes. GKE is the consensus best-in-class: it was built by Google (the creators of Kubernetes), offers Autopilot mode that handles node management entirely, includes integrated service mesh and CI/CD, and consistently receives the highest marks for reliability and ease of upgrade. EKS is the most popular by market share but requires more operational effort (node group management, add-on configuration). AKS is a solid middle ground and the natural choice for teams already invested in Azure.
Serverless compute. AWS Lambda pioneered the serverless category and has the deepest ecosystem (API Gateway, Step Functions, EventBridge). GCP Cloud Functions and Cloud Run offer a simpler developer experience; Cloud Run in particular is excellent because it runs any containerized application without requiring serverless-specific code changes. Azure Functions has strong integration with the Microsoft ecosystem (Logic Apps, Power Automate). For pure serverless workloads, Lambda’s ecosystem is strongest. For teams that want serverless simplicity with container flexibility, Cloud Run is the best option available.
Pricing comparison. For a standard web application workload (4 vCPU, 16 GB RAM, running 24/7), approximate monthly costs with 1-year committed pricing: AWS $120 to $140, GCP $110 to $130, Azure $115 to $135. GCP is typically 5% to 15% cheaper for sustained workloads due to automatic sustained-use discounts that require no upfront commitment. AWS and Azure require purchasing reserved instances or savings plans to achieve comparable rates.
Database Services and Data Platform Capabilities
Your database choice often locks you into a provider more tightly than your compute choice.
Managed relational databases. AWS RDS supports PostgreSQL, MySQL, MariaDB, Oracle, and SQL Server with automated backups, read replicas, and Multi-AZ failover. Aurora (AWS’s proprietary engine compatible with PostgreSQL and MySQL) offers 3 to 5x throughput improvement and Aurora Serverless v2 scales to zero for intermittent workloads. GCP Cloud SQL provides PostgreSQL, MySQL, and SQL Server with Alloy DB as Google’s high-performance PostgreSQL-compatible option (competitive with Aurora on benchmarks). Azure offers Azure SQL Database (SQL Server based) with unique features like automatic tuning and threat detection, plus Azure Database for PostgreSQL with a Citus-based hyperscale option.
For most applications, RDS PostgreSQL on AWS or Cloud SQL PostgreSQL on GCP are interchangeable. Aurora and AlloyDB offer performance advantages for high-throughput workloads (above 10,000 transactions per second). Azure SQL Database is the clear choice if your team has SQL Server expertise.
NoSQL and document databases. DynamoDB (AWS) is the gold standard for key-value and document workloads at scale: single-digit millisecond latency, automatic scaling, and virtually unlimited throughput. Google Cloud offers Firestore (document database with real-time sync, excellent for mobile and web apps) and Bigtable (wide-column store for analytical workloads). Azure provides Cosmos DB, which is unique in supporting multiple data models (document, key-value, graph, column-family, and table) through a single service with global distribution and configurable consistency levels.
Data warehousing and analytics. Google BigQuery is the strongest cloud data warehouse: serverless (no cluster management), pay-per-query pricing (you only pay when you run queries), and exceptional performance on large analytical queries. AWS Redshift is a more traditional approach (provisioned clusters) with Redshift Serverless as a newer option. Azure Synapse Analytics combines data warehousing with Spark-based data processing. For analytics-heavy workloads, BigQuery’s pricing model and ease of use make GCP the strongest choice.
Machine learning platforms. AWS SageMaker provides an end-to-end ML platform covering data labeling, training, deployment, and monitoring. Google Vertex AI leverages Google’s ML heritage and offers the tightest integration with TensorFlow and JAX, plus access to Google’s TPU hardware for training large models. Azure Machine Learning integrates with the Microsoft research ecosystem and offers strong AutoML capabilities. GCP has the edge for teams doing cutting-edge ML research. AWS has the edge for enterprise ML deployment at scale. Azure has the edge for teams using Microsoft’s ML frameworks and tools.
Networking, Security, and Compliance
These services are less visible but create the deepest lock-in.
Global network. Google operates the largest private network backbone of the three providers, with over 100 points of presence and subsea cables. GCP’s premium tier routes traffic on Google’s private network from the edge closest to the user, reducing latency by 20% to 40% compared to standard internet routing. AWS and Azure offer similar premium networking but at higher cost. For latency-sensitive global applications, GCP’s network is a genuine advantage.
Identity and access management. AWS IAM is the most granular and most complex: fine-grained policies with hundreds of service-specific permissions. It is powerful but has a steep learning curve and is a common source of misconfigurations. GCP IAM uses a simpler role-based model with predefined roles that cover most use cases. Azure Active Directory (Entra ID) integrates with on-premises Active Directory, making it the natural choice for enterprises with existing Microsoft identity infrastructure.
Compliance certifications. All three providers hold the major certifications: SOC 1/2/3, ISO 27001, PCI DSS, HIPAA, FedRAMP. Azure has the broadest compliance portfolio (over 100 compliance offerings) and is the only provider with IL5 authorization for Department of Defense workloads across all regions. AWS GovCloud provides dedicated regions for government workloads. GCP has strong healthcare and financial services compliance but a narrower set of government-specific certifications.
Data residency. If your data must remain in a specific country, verify that the provider has a region there. AWS operates in 33 geographic regions. Azure in 60+ regions (the most of any provider). GCP in 40 regions. Azure’s broader geographic coverage matters for companies with strict data residency requirements in countries where AWS and GCP have no presence.
See also: The AI Technology Stack: Models, Frameworks, and Infrastructure Guide
Cost Management and Financial Operations
Cloud costs are the second largest line item for many technology companies, after payroll.
Pricing models. All three offer on-demand (pay by the hour/second), committed use (1 to 3 year commitments for 30% to 72% discounts), and spot/preemptible instances (60% to 90% discounts with interruption risk). GCP uniquely offers sustained-use discounts that apply automatically: if you run an instance for more than 25% of the month, the price per hour decreases incrementally, reaching a 30% discount at 100% utilization. No commitment required.
Egress costs. Data transfer out of the cloud is one of the most complained-about costs. AWS charges $0.09/GB for the first 10 TB of internet egress. GCP charges $0.08 to $0.12/GB depending on destination. Azure charges $0.087/GB. Google recently introduced a free 200 GB/month egress tier for Cloud Run. For data-heavy applications (video streaming, file distribution, API-heavy services), egress costs can be 15% to 25% of total cloud spend. Consider CloudFront (AWS), Cloud CDN (GCP), or a third-party CDN (Cloudflare) to reduce egress costs.
Cost visibility and tools. AWS Cost Explorer and GCP Cost Management both provide detailed breakdowns and forecasting. Azure Cost Management integrates with Power BI for custom reporting. All three support budget alerts. For serious cost optimization, use a third-party tool like Vantage, CloudZero, or Infracost that provides cross-cloud visibility and anomaly detection.
Billing support and negotiation. For annual cloud spend above $100,000, negotiate directly with the provider’s sales team. Custom discounts of 15% to 40% off list prices are common for committed spend. AWS offers Enterprise Discount Programs. GCP offers custom pricing through its sales team. Azure offers Enterprise Agreements. Do not pay list price at scale.
Decision Framework by Company Profile
Rather than abstract comparisons, here are specific recommendations by company type.
Startup building a web/mobile SaaS product. GCP or AWS. GCP if you are a small team that values simplicity (Cloud Run, BigQuery, and Firestore cover most needs with minimal configuration). AWS if you anticipate needing a broad range of services as you grow (the breadth of the AWS ecosystem is unmatched). Both offer startup credits: AWS Activate provides up to $100,000 in credits. Google for Startups provides up to $200,000 in credits.
Enterprise with existing Microsoft infrastructure. Azure. The integration with Active Directory, Office 365, Teams, and the Microsoft security stack makes Azure the path of least resistance. Hybrid cloud capabilities (Azure Arc, Azure Stack) are the most mature for extending on-premises infrastructure to the cloud.
Data and ML-focused company. GCP. BigQuery for analytics, Vertex AI for ML, and TPU access for training give GCP the strongest data platform. Google’s data engineering tools (Dataflow, Dataproc, Pub/Sub) are consistently well-designed and well-integrated.
Regulated industry (healthcare, finance, government). Azure for the broadest compliance coverage and government certifications. AWS for teams that prefer its operational maturity and broader service catalog. GCP for teams that prioritize performance and cost efficiency and can work within its compliance coverage.
Multi-cloud strategy. If you intentionally want to avoid lock-in, build on Kubernetes (GKE, EKS, or AKS) with a provider-agnostic database (self-managed PostgreSQL on VMs) and abstract cloud services behind an interface layer (Terraform for infrastructure, application-level adapters for object storage and queuing). This increases operational complexity by 30% to 50% but provides genuine portability.
The cloud provider decision should be driven by your specific workload requirements, team expertise, compliance needs, and financial constraints rather than by marketing materials or defaults. If you are evaluating cloud providers for a new project or considering a migration and want an objective technical assessment, contact our team to discuss your architecture needs.