AWS vs Azure: Side-by-Side Cloud Service Comparison

Provides resizable compute capacity in the cloud.

• Key Considerations:
  • AWS (EC2): Widest range of instance types (general purpose, compute/memory/storage optimized, accelerated computing like GPUs/FPGAs, Graviton ARM-based processors). Mature ecosystem. Diverse pricing models (On-Demand, Spot, Reserved Instances, Savings Plans). Greater market share and often more third-party tool integrations.
  • Azure (VMs): Strong integration with Microsoft ecosystem (Windows Server, SQL Server, Active Directory via Entra ID). Azure Hybrid Benefit offers cost savings for existing Windows Server/SQL Server licenses. Comprehensive instance types, including specialized HPC and SAP HANA instances. Similar pricing models (Pay-as-you-go, Reserved Instances, Spot VMs).
• Common Use Cases: Web hosting, application servers, batch processing, backend processing for applications, development and testing environments, disaster recovery.

Automatically adjusts the number of compute resources based on demand. Both offer scheduled, dynamic, and predictive scaling.

Serverless compute to run code in response to events without managing servers.

• Key Considerations:
  • AWS (Lambda): Pioneer in serverless, extensive integrations with other AWS services. Supports multiple runtimes. Features like Lambda Layers for sharing code, Provisioned Concurrency for managing cold starts, and Lambda Extensions. More mature monitoring and tooling ecosystem generally.
  • Azure (Functions): Excellent integration with Azure services and Microsoft developer tools (Visual Studio, VS Code). Supports multiple languages and hosting plans (Consumption, Premium, Dedicated). Durable Functions for stateful serverless workflows. Strong support for .NET.
• Common Use Cases: Real-time file processing, data transformation, event-driven backends, IoT data processing, chatbots, scheduled tasks.

Services for running and managing containerized applications. EKS/AKS for managed Kubernetes. ECS is AWS's proprietary orchestrator. Fargate/Container Instances offer serverless container execution.

A repository to store and manage container images, supporting Docker and OCI formats.

Provision compute resources for batch job requirements, often using managed compute environments or Spot Instances/VMs.

Scalable and durable object storage for a wide variety of data.

• Key Considerations:
  • AWS (S3): Highly mature, vast feature set (versioning, lifecycle policies, replication, S3 Object Lock, S3 Storage Lens). Multiple storage classes (Standard, Intelligent-Tiering, Glacier tiers). Strong consistency model. Extensive ecosystem integration.
  • Azure (Blob Storage): Multiple access tiers (Hot, Cool, Archive) with different pricing and SLAs. Features like versioning, lifecycle management, immutability. Azure Data Lake Storage Gen2 is built on Blob Storage for big data analytics.
• Common Use Cases: Data lakes, backup and archive, static website hosting, big data analytics, content distribution, application data.

Persistent block storage for use with EC2 instances/VMs, offering various performance tiers (SSD, HDD).

Low-cost archival storage for long-term data retention with varying retrieval times and costs.

Managed file storage (NFS for EFS, SMB/NFS for Azure Files) that can be shared across multiple resources.

Physical devices to transfer large amounts of data to and from the cloud, with options for edge compute capabilities.

Hybrid cloud storage solutions to integrate on-premises environments with cloud storage.

Isolated private network in the cloud, allowing definition of IP address ranges, subnets, route tables, and network gateways.

Scalable Domain Name System (DNS) web service, offering domain registration, DNS routing, and health checking.

Dedicated private network connection from on-premises data centers to the cloud, offering higher bandwidth and more consistent network performance.

Distributes incoming application traffic across multiple targets, such as EC2 instances, containers, and IP addresses.

Service for creating, publishing, maintaining, monitoring, and securing APIs at any scale.

Managed relational database services supporting various engines.

• Key Considerations:
  • AWS (RDS/Aurora): RDS supports MySQL, PostgreSQL, MariaDB, Oracle, SQL Server. Aurora is a MySQL and PostgreSQL-compatible relational database built for the cloud, offering higher performance and availability. Serverless options available.
  • Azure (SQL DB et al.): Azure SQL Database offers PaaS SQL Server with various deployment options (Single Database, Elastic Pool, Managed Instance). Azure Database for MySQL/PostgreSQL/MariaDB are managed open-source database services. Strong tooling and migration support for SQL Server.
• Common Use Cases: Transactional applications, web applications, enterprise applications, data persistence for applications.

Managed NoSQL database services.

• Key Considerations:
  • AWS (DynamoDB): Key-value and document database delivering single-digit millisecond performance at any scale. Offers global tables, on-demand capacity, and ACID transactions. Well-suited for high-traffic web apps, mobile apps, gaming.
  • Azure (Cosmos DB): Globally distributed, multi-model database service supporting various APIs (SQL/Core, MongoDB, Cassandra, Gremlin, Table). Offers tunable consistency levels, low latency, and turnkey global distribution. Often chosen for applications requiring multi-model support or global scale with specific consistency needs.
• Common Use Cases: User profiles, session management, real-time data, gaming leaderboards, IoT data, product catalogs.

Data warehousing services for large-scale analytical querying and business intelligence.

Broad monitoring and observability services for applications and infrastructure.

• Key Considerations:
  • AWS (CloudWatch): Collects metrics, logs, and traces. Features include Alarms, Dashboards, Logs Insights, ServiceLens, Synthetics, RUM. Integrates deeply with AWS services.
  • Azure (Monitor): Comprehensive solution for collecting, analyzing, and acting on telemetry. Includes Metrics, Logs (Log Analytics), Alerts, Application Insights, VM insights, Container insights. Strong integration with Azure services and on-premises via Azure Arc.
• Common Use Cases: Infrastructure monitoring, application performance monitoring, log aggregation and analysis, alerting on anomalies or thresholds.

Application Performance Management (APM) services for monitoring, detecting, and diagnosing issues in live applications, including distributed tracing.

Infrastructure as Code (IaC) services to define and provision cloud resources using templates.

Services for tracking resource configurations, ensuring compliance, and managing application settings.

Provides recommendations to optimize your cloud environment across cost, performance, security, reliability, and operational excellence.

Tools for analyzing, visualizing, and managing cloud costs and setting budgets.

Manage user access to services and resources, and define roles and permissions.

Manage cryptographic keys and secrets. CloudHSM/Key Vault Managed HSM offer dedicated hardware security modules.

Protects web applications from common web exploits that can affect availability, compromise security, or consume excessive resources.

Protection against Distributed Denial of Service (DDoS) attacks.

Suite of services for DevOps practices including version control, build automation, deployment automation, CI/CD pipelines, and cloud-based IDEs.

Serverless workflow orchestration to visually design, build, and automate business processes and integrate applications and data.

• Key Considerations:
  • AWS (Step Functions): Integrates with Lambda, ECS, SNS, SQS, and other AWS services. Offers Standard and Express workflows. Focuses on coordinating components of distributed applications and microservices. State management is a core feature.
  • Azure (Logic Apps): Extensive library of connectors to Azure services, Microsoft products (Office 365, Dynamics 365), and third-party SaaS applications. Visual designer with a strong emphasis on enterprise integration (EAI/B2B). Consumption and Standard (single-tenant) plans.
• Common Use Cases: Microservice orchestration, ETL processes, IT automation, B2B integration, data processing pipelines.

Messaging services for decoupling applications, including queues for point-to-point, topics for pub/sub, and event routing/streaming.

Services for building, training, and deploying ML models (SageMaker, Azure ML) and AI-powered applications using pre-trained models for vision, speech, language, etc., including Generative AI capabilities.

Services for connecting, managing, securing, and analyzing data from IoT devices at scale, with options for edge computing.

Comprehensive suite of services for data ingestion, ETL/ELT, data lakes, stream processing, big data processing (Spark, Hadoop), interactive querying, data warehousing (see Databases), and business intelligence.

Tools and services to plan, execute, and track migrations of servers, applications, databases, and data to the cloud. (Snow/Data Box families also play a role in data transfer).

Services for establishing secure landing zones, centralized security posture management, threat detection, vulnerability management, data security, and security analytics/incident response (SIEM/SOAR capabilities).