AWS vs Azure vs Google Cloud: 26 Services Compared

Marketplace

Easy-to-deploy and automatically configured third-party applications, including single virtual machine or multiple virtual machine solutions.

They are both digital catalog with thousands of software listings from independent software vendors that make it easy to find, test, buy, and deploy software that runs on their respective cloud platform.

AI and machine learning

A cloud service to train, deploy, automate, and manage machine learning models.

According to the StackShare community, Azure Machine Learning has a broader approval, being mentioned in 12 company stacks & 8 developers stacks; compared to Amazon Machine Learning, which is listed in 8 company stacks and 9 developer stacks.

AI and machine learning

Build and connect intelligent bots that interact with your users using text/SMS, Skype, Teams, Slack, Office 365 mail, Twitter, and other popular services.

One major advantage Google gets over Alexa is that Google Assistant is available to almost all Android devices.

AI and machine learning

Description:API capable of converting speech to text, understanding intent, and converting text back to speech for natural responsiveness.

api.ai provides us with such a platform which is easy to learn and comprehensive to develop conversation actions. It is a good example of the simplistic approach to solving complex man to machine communication problem using natural language processing in proximity to machine learning. Api.ai supports context based conversations now, which reduces the overhead of handling user context in session parameters. On the other hand in Lex this has to be handled in session. Also, api.ai can be used for both voice and text based conversations (assistant actions can be easily created using api.ai).

AI and machine learning

Description:Computer Vision: Extract information from images to categorize and process visual data.

Google Cloud Vision and Amazon Rekognition offer a broad spectrum of solutions, some of which are comparable in terms of functional details, quality, performance, and costs.

Big data and analytics: Data warehouse

Description:Cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP) to quickly run complex queries across petabytes of data.

Big data and analytics: Data warehouse

Description: Apache Spark-based analytics platform. Managed Hadoop service. Data orchestration, ETL, Analytics and visualization

Virtual servers

Description:Virtual servers allow users to deploy, manage, and maintain OS and server software. Instance types provide combinations of CPU/RAM. Users pay for what they use with the flexibility to change sizes. Batch: Run large-scale parallel and high-performance computing applications efficiently in the cloud.

Containers and container orchestrators

Description: A container is a standard unit of software that packages up code and all its dependencies so the application runs quickly and reliably from one computing environment to another. Container orchestration is all about managing the lifecycles of containers, especially in large, dynamic environments.

Google Container Engine, AWS Container Services, and Azure Container Instances can be used to run docker containers. Google offers a simple and very capable set of services that are easy to understand. However, with availability in only 5 regions it does not have the coverage of the other players.

Serverless

Description: Integrate systems and run backend processes in response to events or schedules without provisioning or managing servers.

Relational databases

Description: Managed relational database service where resiliency, scale, and maintenance are primarily handled by the platform.

All three providers boast impressive relational database offering. RDS supports an impressive range of managed relational stores while Azure SQL Database is probably the most advanced managed relational database available today. Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.

NoSQL, Document Databases

Description:A globally distributed, multi-model database that natively supports multiple data models: key-value, documents, graphs, and columnar.

DynamoDB and Cloud Datastore are based on the document store database model and are therefore similar in nature to open-source solutions MongoDB and CouchDB. In other words, each database is fundamentally a key-value store. With more workloads moving to the cloud the need for NoSQL databases will become ever more important, and again all providers have a good range of options to satisfy most performance/cost requirements. Of all the NoSQL products on offer it’s hard not to be impressed by DocumentDB; Azure also has the best out-of-the-box support for cross-region geo-replication across its database offerings.

Caching

Description:An in-memory–based, distributed caching service that provides a high-performance store typically used to offload non transactional work from a database.

ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn't— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it'll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys. ElastiCache supports Memcached and Redis. Memcached Cloud provides various data persistence options as well as remote backups for disaster recovery purposes. Redis offers persistence to disk, Memcache does not. This can be very helpful if you cache lots of data, since you remove the slowness around having a fully cold cache. Redis also offers several extra data structures that Memcache doesn't— Lists, Sets, Sorted Sets, etc. Memcache only has Key/Value pairs. Memcache is multi-threaded. Redis is single-threaded and event driven. Redis is very fast, but it'll never be multi-threaded. At hight scale, you can squeeze more connections and transactions out of Memcache. Memcache tends to be more memory efficient. This can make a big difference around the magnitude of 10s of millions or 100s of millions of keys.

Security, identity, and access

Description:Authentication and authorization: Allows users to securely control access to services and resources while offering data security and protection. Create and manage users and groups, and use permissions to allow and deny access to resources.

Object Storage and Content delivery

Description:Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.

Source: All providers have good object storage options and so storage alone is unlikely to be a deciding factor when choosing a cloud provider. The exception perhaps is for hybrid scenarios, in this case Azure and AWS clearly win. AWS and Google’s support for automatic versioning is a great feature that is currently missing from Azure; however Microsoft’s fully managed Data Lake Store offers an additional option that will appeal to organisations who are looking to run large scale analytical workloads. If you are prepared to wait 4 hours for your data and you have considerable amounts of the stuff then AWS Glacier storage might be a good option. If you use the common programming patterns for atomic updates and consistency, such as etags and the if-match family of headers, then you should be aware that AWS does not support them, though Google and Azure do. Azure also supports blob leasing, which can be used to provide a distributed lock.

Internet of things (IoT)

Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.

Web Applications

Description:Managed hosting platform providing easy to use services for deploying and scaling web applications and services. API Gateway is a a turnkey solution for publishing APIs to external and internal consumers. Cloudfront is a global content delivery network that delivers audio, video, applications, images, and other files.

Encryption

Description:Helps you protect and safeguard your data and meet your organizational security and compliance commitments.

AWS KMS, is an ideal solution for organizations that want to manage encryption keys in conjunction with other AWS services. In contrast to AWS CloudHSM, AWS KMS provides a complete set of tools to manage encryption keys, develop applications and integrate with other AWS services. Google and Azure offer 4096 RSA. AWS and Google offer 256 bit AES. With AWs, you can bring your own key

Internet of things (IoT)

Description:A cloud gateway for managing bidirectional communication with billions of IoT devices, securely and at scale. Deploy cloud intelligence directly on IoT devices to run in on-premises scenarios.

Object Storage and Content delivery

Description: Object storage service, for use cases including cloud applications, content distribution, backup, archiving, disaster recovery, and big data analytics.

Backend process logic

Description: Cloud technology to build distributed applications using out-of-the-box connectors to reduce integration challenges. Connect apps, data and devices on-premises or in the cloud.

AWS Step Functions makes it easy to coordinate the components of distributed applications and microservices using visual workflows. Building applications from individual components that each perform a discrete function lets you scale and change applications quickly. AWS Step Functions belongs to \"Cloud Task Management\" category of the tech stack, while Google Cloud Dataflow can be primarily classified under \"Real-time Data Processing\". According to the StackShare community, Google Cloud Dataflow has a broader approval, being mentioned in 32 company stacks & 8 developers stacks; compared to AWS Step Functions, which is listed in 19 company stacks and 7 developer stacks.

Enterprise application services

Description:Fully integrated Cloud service providing communications, email, document management in the cloud and available on a wide variety of devices.

G suite document processing applications like Google Docs are far behind Office 365 popular Word and Excel software, but G Suite User interface is intuite, simple and easy to navigate. Office 365 is too clunky. Get 20% off G-Suite Business Plan with Promo Code: PCQ49CJYK7EATNC

Networking

Description: Provides an isolated, private environment in the cloud. Users have control over their virtual networking environment, including selection of their own IP address range, creation of subnets, and configuration of route tables and network gateways.

Subnets group related resources, however, unlike AWS and Azure, Google do not constrain the private IP address ranges of subnets to the address space of the parent network. Like Azure, Google has a built in internet gateway that can be specified from routing rules.

Management

Description: A unified management console that simplifies building, deploying, and operating your cloud resources.

AWS Console categorizes its Infrastructure as a Service offerings into Compute, Storage and Content Delivery Network (CDN), Database, and Networking to help businesses and individuals grow. Azure excels in the Hybrid Cloud space allowing companies to integrate onsite servers with cloud offerings. Google has a strong offering in containers, since Google developed the Kubernetes standard that AWS and Azure now offer. GCP specializes in high compute offerings like Big Data, analytics and machine learning. It also offers considerable scale and load balancing – Google knows data centers and fast response time.

DevOps and application monitoring

Description: Comprehensive solution for collecting, analyzing, and acting on telemetry from your cloud and on-premises environments; Cloud services for collaborating on code development; Collection of tools for building, debugging, deploying, diagnosing, and managing multiplatform scalable apps and services; Fully managed build service that supports continuous integration and deployment.

CodeCommit eliminates the need to operate your own source control system or worry about scaling its infrastructure. Azure DevOps provides unlimited private Git hosting, cloud build for continuous integration, agile planning, and release management for continuous delivery to the cloud and on-premises. Includes broad IDE support.

Messaging Services

Description: Cloud messaging service between applications and services

The implementation of FCM can be a little bit tricky for novices. Amazon SNS and Firebase Cloud Messaging can be primarily classified as 'Mobile Push Messaging' tools.