Finding Equivalent Services Across AWS, Azure, and GCP


Navigating the cloud service offerings of Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP) can be challenging due to the sheer volume and variety of services available. This article aims to demystify this complexity by providing a detailed comparison of equivalent services across these three cloud giants, focusing on how these services function and their primary use cases.


Computing Services

EC2 (Elastic Compute)Virtual MachinesCompute Engine
LambdaFunctionsCloud Functions
Elastic BeanstalkApp ServiceApp Engine
AWS BatchBatchCloud Dataflow

Key Points

  • EC2, Virtual Machines, and Compute Engine offer scalable computing capacity in the cloud. They allow users to run and manage servers without the overhead of physical hardware management. Ideal for a wide range of computing needs, from hosting websites to running large-scale data processing.
  • Lambda, Functions, and Cloud Functions are serverless computing services, enabling the running of code in response to events without the need to manage servers. These are used for automating small tasks and integrating systems.
  • Elastic Beanstalk, App Service, and App Engine simplify the deployment and scaling of applications. They manage the infrastructure, allowing developers to focus on code.
  • AWS Batch, Azure Batch, and Cloud Dataflow are designed for large-scale batch computing and data processing. They automate the provisioning of resources and are ideal for big data applications.

Storage Services

S3 (Simple Storage Service)Blob StorageCloud Storage
EBS (Elastic Block Store)Managed DisksPersistent Disk
GlacierBlob Storage - Cool & ArchiveColdline & Archive Storage

Key Points

  • S3, Blob Storage, and Cloud Storage are object storage services, ideal for storing and retrieving any amount of data. They are commonly used for backup, disaster recovery, and serving static content for web applications.
  • EBS, Managed Disks, and Persistent Disk provide block-level storage volumes for persistent data storage. These are typically used with virtual machines when consistent performance and durability are required.
  • Glacier, Blob Storage - Cool & Archive, and Coldline & Archive Storage offer cost-effective solutions for long-term data archiving and backup. They are used when access to data is infrequent and retrieval times of several hours are acceptable.

Database Services

RDS (Relational Database Service)SQL DatabaseCloud SQL
DynamoDBCosmos DBFirestore (Datastore mode)
RedshiftSynapse Analytics (SQL DW)BigQuery

Key Points

  • RDS, SQL Database, and Cloud SQL support popular relational databases like MySQL, PostgreSQL, and SQL Server. These services are used for traditional database workloads where structured data is stored in tables.
  • DynamoDB, Cosmos DB, and Firestore are NoSQL database services, providing fast and flexible options for working with unstructured data. They are ideal for mobile, web, gaming, ad tech, IoT, and many other applications.
  • Redshift, Synapse Analytics, and BigQuery are fully managed data warehousing services for large-scale data analytics. They are used for running complex queries and analytics on large datasets.

Networking Services

VPC (Virtual Private Cloud)Virtual NetworkVPC (Virtual Private Cloud)
Route 53Azure DNSCloud DNS
API GatewayAPI ManagementAPI Gateway

Key Points

  • VPC and Virtual Network provide isolated network environments within the cloud. They enable users to launch resources into a virtual network tailored for their applications, ideal for managing access and security.
  • Route 53, Azure DNS, and Cloud DNS are scalable domain name system services, used for domain registration, DNS routing, and managing domain names for applications.
  • API Gateway across all three platforms offer a way to create, publish, manage, and secure APIs. They act as a front door for applications to access data, business logic, or functionality.

AI and Machine Learning Services

SageMakerAzure Machine LearningAI Platform
RekognitionComputer VisionVision AI
ComprehendText AnalyticsNatural Language API

Key Points

  • SageMaker, Azure Machine Learning, and AI Platform provide comprehensive environments for building, training, and deploying machine learning models. These services offer tools and integrated environments for data scientists and developers.
  • Rekognition, Computer Vision, and Vision AI specialize in image and video analysis, using machine learning to detect objects, faces, and textual content within images and videos.
  • Comprehend, Text Analytics, and Natural Language API are services for natural language processing, enabling applications to analyze and interpret human language.


Navigating through the plethora of services offered by AWS, Azure, and GCP can be overwhelming. This comparison provides a foundational understanding of the equivalent services across these platforms, aiding in making informed decisions based on specific requirements and use cases. As cloud technologies continue to evolve, staying updated with the latest offerings and features is crucial for leveraging the full potential of cloud computing.