Choosing the right cloud: AWS vs Azure vs Google Cloud

The cloud market has three dominant players: Amazon Web Services (AWS), Microsoft Azure and Google Cloud Platform (GCP). All three offer compute, storage, databases, networking, analytics and AI services on demand, but their histories, strengths and ecosystems differ. This post provides a concise comparison of the platforms, highlights standout services and helps you decide which environment best fits your next project.

Platform overviews

🌐 Amazon Web Services (AWS)

Launched in 2006, AWS pioneered public cloud computing and remains the market leader. It provides hundreds of services spanning infrastructure, applications and developer tools. Key categories include:

• Compute: EC2 virtual machines, Lambda serverless functions, Fargate for container orchestrationdatacamp.com.

• Storage: S3 object storage, EBS block storage and AWS Backupdatacamp.com.

• Databases & analytics: Relational Database Service (RDS), DynamoDB NoSQL, Redshift data warehouse and Glue ETLdatacamp.com.

• Networking & CDN: Virtual Private Cloud, Direct Connect and CloudFrontdatacamp.com.

• Security & monitoring: IAM, CloudTrail, WAF, Shielddatacamp.com.

• AI/ML: SageMaker for machine learning, Lex for chatbots, Rekognition for image analysisdatacamp.com.

• DevOps & migration: CodePipeline, CodeDeploy, CloudFormation, Snowball, Outpostsdatacamp.com.

Strengths: breadth of services, mature ecosystem, broad global coverage and enterprise support. AWS is often the first choice for startups and enterprises needing every tool imaginable.

☁️ Microsoft Azure

Azure launched in 2010 and is popular among enterprises already using Microsoft products. It offers:

• Compute: Virtual Machines, Azure Kubernetes Service and Azure Functionsdatacamp.com.

• Networking: Virtual Network, Load Balancer and ExpressRoute private connectivitydatacamp.com.

• Storage & databases: Blob Storage, Azure Files, Cosmos DB (multi‑model NoSQL) and SQL Databasedatacamp.com.

• AI/ML: Azure Machine Learning, Cognitive Services (vision, speech, language) and Bot Servicesdatacamp.com.

• IoT & edge: IoT Hub, Sphere and Edge Zonesdatacamp.com.

• Security & identity: Azure Active Directory, Defender for Cloud and Key Vaultdatacamp.com.

• DevOps & integration: Azure DevOps, Logic Apps and API Managementdatacamp.com.

• Hybrid & multi‑cloud: Azure Arc, Azure Stack and Site Recoverydatacamp.com.

Strengths: seamless integration with Windows Server, Active Directory and Office 365; strong enterprise support; hybrid capabilities (Arc/Stack) for on‑premises workloads.

🔵 Google Cloud Platform (GCP)

GCP started in 2008 and leverages Google’s internal infrastructure. It’s renowned for data analytics and machine learning. Key services include:

• Compute: Compute Engine virtual machines, App Engine PaaS, Cloud Run serverless and container orchestration via Google Kubernetes Enginedatacamp.com.

• Storage & databases: Cloud Storage, Cloud SQL, Bigtable and Firestoredatacamp.com.

• Data & analytics: BigQuery data warehouse, Dataflow streaming/batch pipelines, Dataproc for Hadoop/Spark, and Looker for BIeginnovations.com.

• AI/ML: Vertex AI platform and pre‑trained APIs (Vision, Speech, Natural Language)eginnovations.com.

• Networking & dev tools: Virtual Private Cloud, Cloud Load Balancing, Cloud Build and Cloud Deploy.

Strengths: cutting‑edge data and AI services, open‑source leadership (Kubernetes, TensorFlow), attractive pricing and integration with Google’s ecosystem.

Service comparison table

Category	AWS	Azure	Google Cloud
Compute	EC2, Lambda, Fargate	Virtual Machines, AKS, Functions	Compute Engine, GKE, App Engine/Run
Serverless	Lambda, Fargate	Functions, Logic Apps	Cloud Run, Cloud Functions
Containers	ECS/EKS, Fargate	Azure Kubernetes Service (AKS)	Google Kubernetes Engine (GKE)
Storage	S3, EBS, EFS	Blob, Files, Queue	Cloud Storage, Persistent Disks
Relational DB	RDS (MySQL/PG/SQL Server)	SQL Database	Cloud SQL, Spanner
NoSQL	DynamoDB	Cosmos DB	Bigtable, Firestore
Data Warehouse	Redshift	Synapse Analytics	BigQuery
Analytics & ETL	Glue, Kinesis, EMR	Data Factory, Stream Analytics	Dataflow, Dataproc, Dataplex
AI/ML	SageMaker, Rekognition, Lex	Azure ML, Cognitive Services	Vertex AI, AutoML, AI APIs
DevOps	CodePipeline, CloudFormation	Azure DevOps, ARM, Bicep	Cloud Build, Cloud Deploy
Hybrid & Edge	Outposts, Snowball, Local Zones	Arc, Stack, Sphere	Anthos (GKE on‑prem), Edge TPUs

Note: This table highlights comparable flagship services; each provider offers dozens more options in each category.

Choosing the right platform

1. Breadth vs. depth – If you need every possible service (IoT, robotics, industrial) and global coverage, AWS’s catalogue is hard to beat. Azure has similar breadth but leans toward enterprise integration. Google focuses on depth in analytics and machine learningeginnovations.com.

2. Ecosystem alignment – Teams already using Windows Server, .NET or Active Directory will find Azure integration seamless. Startups building AI products may gravitate to GCP because of BigQuery, Dataflow and Vertex AI. Companies with existing AWS expertise may stick with EC2, Lambda and RDS.

3. Hybrid and multi‑cloud – Azure Arc/Stack and AWS Outposts support on‑prem/hybrid deployments, while GCP’s Anthos offers multi‑cloud Kubernetes management. Evaluate which solution fits your hybrid strategy.

4. Pricing – All three providers offer pay‑as‑you‑go pricing, reserved instances and discounts. AWS and Azure often price by region; GCP tends to have simplified networking costs and sustained‑use discounts. Pricing varies by workload; use the providers’ calculators to estimate costs.

5. Compliance and regions – Ensure the provider has data centres in regions you need and meets industry‑specific compliance (HIPAA, FedRAMP, GDPR).

Conclusion

The cloud landscape isn’t one‑size‑fits‑all. AWS, Azure and Google Cloud all provide robust compute, storage, analytics and AI services, but they emphasise different strengths:

• AWS offers the broadest service catalogue and longest track record. It’s a safe choice for companies wanting comprehensive functionality and global reach.

• Azure excels at hybrid deployments and enterprise integration, making it attractive to organisations deeply invested in Microsoft’s ecosystem.

• Google Cloud stands out for data analytics, machine learning and open‑source innovation, appealing to data‑driven teams and developers favouring Kubernetes and TensorFlow.

When choosing a platform, prioritise your project’s requirements—compute models, data volumes, tooling preferences and existing infrastructure. In many cases, organisations adopt a multi‑cloud strategy, running workloads on different providers to leverage each one’s unique strengths.