azure 和 aws比较_AWS，Google Cloud和Azure的并行比较

azure 和 aws比较

Learn more about AWS with our Getting Started with AWS screencast.

通过我们的AWS 入门截屏视频，了解有关AWS的更多信息。

Three main players of business cloud services have an array of products covering all you can possibly need for your online operations. But there are differences not only in pricing but also in how they name and group their services, so let’s compare one next to another and find out what they offer.

商业云服务的三个主要参与者提供了一系列产品，涵盖了您在线运营所需的全部内容。 但是，不仅在价格上存在差异，而且在服务的命名和分组方式上也存在差异，因此，让我们将彼此之间进行比较，找出它们所提供的服务。

We’ll focus on services provided by Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure. We won’t cover all of them, or get into much detail about the infrastructure of cloud computing. However, you will be exposed to many of the products you can use, and hopefully get familiar with some cloud concepts.

我们将重点介绍Amazon Web Services (AWS)， Google Cloud Platform (GCP)和Microsoft Azure提供的服务 。 我们不会涵盖所有内容，也不会详细介绍云计算的基础架构。 但是，您将接触到许多可以使用的产品，并希望熟悉一些云概念。

Trending posts on SitePoint today:

今天在SitePoint上的热门帖子：

• Is Using SVG Images Good for Your Website’s Performance?

  使用SVG图片是否对您网站的性能有利？

• Five Techniques to Lazy Load Images for Website Performance

  延迟加载图像以提高网站性能的五种技术

• Five CSS Performance Tools to Speed up Your Website

  五个CSS性能工具可加速您的网站

• How to Choose, Register and Make the Most of Your Domain Name

  如何选择，注册和充分利用您的域名

• Deploy & Manage Multiple WordPress Sites with ServerPilot

  使用ServerPilot部署和管理多个WordPress网站

为什么是云 (Why the Cloud)

Iconic companies from both the public and the private sector — such as Netflix, AirBNB, Spotify, Expedia, PBS, and many, many more — rely on cloud services for supporting their online operations. This allows them to better focus on doing what they’re known for, and let many of the technicalities be taken care of by an infrastructure that already exists and is constantly being upgraded. If they had to implement the physical infrastructure they actually need for their operations themselves, they would need an army of technicians, lots of extra budget and time, and many startups would never get past these technical challenges.

来自Netflix和AirBNB，Spotify，Expedia，PBS等公共和私营部门的标志性公司都依靠云服务来支持其在线运营。 这使他们可以更好地专注于做自己所熟知的事情，并使许多技术由已经存在并且正在不断升级的基础架构来照顾。 如果他们必须实施自己的实际所需的物理基础架构，那么他们将需要大量的技术人员，大量的额外预算和时间，而且许多初创公司将永远无法克服这些技术挑战。

为了每一个 (For everyone)

But this is not limited to big names. Today, we live in a world in which both a huge business, and two youngsters at home with virtually no initial capital, can access world-class infrastructure for storage, computing, management and more, to make the next massive online service, and pay as they go — literally — by the hour.

但这不仅限于大牌。 如今，我们生活在一个庞大的企业和两个几乎没有初始资金的在家中的年轻人可以访问世界一流的基础架构来进行存储，计算，管理等功能的世界，以进行下一个大规模的在线服务并付费当他们去的时候-从字面上看-按小时计算。

灵活的(有时是复杂的)定价 (Flexible (and sometimes intricate) pricing)

What you pay will vary a lot depending and how much processing power you demand, how many instances (that is, how many virtual servers) you deploy, and where you deploy them (more on this on the “Locations” section). There will also be significant discounts for bulk usage. In any case, you’ll have these advantages most of the time:

您所支付的费用会有所不同，具体取决于您需要多少处理能力，部署了多少实例(即，多少台虚拟服务器)以及部署它们的位置(有关更多信息，请参见“位置”部分)。 批量使用也将有很大的折扣。 无论如何，大多数时候您将拥有以下优势：

• no upfront costs
  没有前期费用
• no termination fees
  无终止费
• pay only for what you use
  只为您使用的东西付费
• per minute billing
  每分钟计费

For precise details, you’ll need to read the pricing fine print of AWS, GCP and Azure.

有关准确的详细信息，您需要阅读AWS ， GCP和Azure的定价细则。

产品与解决方案 (Products vs Solutions)

We will use the terms “products” and “services” rather indistinctly; a solution, however, is a more specific concept that you’ll hear a lot about when dealing with cloud services. Simply put, a solution is a set of preconfigured products oriented to a very specific need, with plentiful documentation, use cases and testimonials that will guide you through the process of adopting the cloud infrastructure.

我们将模糊地使用术语“产品”和“服务”； 但是， 解决方案是一个更具体的概念，您在处理云服务时会听到很多。 简而言之，解决方案是针对特定需求的一组预配置产品，其中包含大量文档，用例和推荐，可指导您完成采用云基础架构的整个过程。

Some typical “canned” solutions are:

一些典型的“罐装”解决方案是：

• big data — AWS, GCP, Azure

  大数据 — AWS ， GCP ， Azure

• digital marketing — AWS, Azure

  数字营销 — AWS ， Azure

• ecommerce — AWS, GCP, Azure

  电子商务 -AWS ， GCP ， Azure

• gaming — AWS, GCP,

  游戏 -AWS ， GCP ，

• government — AWS (federal, sate and local)

  * — AWS( 联邦 ，州和地方 )

• Internet of Things (IoT) — AWS, GCP, Azure (predictive maintenance, remote monitoring)

  物联网(IoT) -AWS ， GCP ，Azure( 预测性维护 ， 远程监控 )

• private clouds

  私有云

• reseller hosting

  转销商托管

让我们比较一下！ (Let’s Compare!)

Amazon introduced “commoditized” cloud computing services through its first AWS service launched back in 2004, and ever since then they kept innovating and adding features, which somehow allowed them having the upper hand in the business by building the most extensive array of services and solutions for the cloud. They are also, in many regards, the most expensive.

亚马逊通过2004年推出的首个AWS服务推出了“商品化”云计算服务，从那时起，他们就不断创新和添加功能，从而通过构建最广泛的服务和解决方案系列使它们在业务中处于优势地位对于云。 在许多方面，它们也是最昂贵的。

Google, and later Microsoft, came into the game and are quickly coming up to par, bringing their own infrastructure and ideas, offering deals, and pulling the prices down.

Google和后来的Microsoft加入了游戏，并Swift达到标准，带来了自己的基础设施和想法，提供了交易，并降低了价格。

In the following video, representatives of each company discuss their cloud strategies:

在以下视频中，每个公司的代表讨论了他们的云战略：

计算 (Compute)

This is what computers are for, after all: to calculate, to process data — to compute. If you need faster processing for graphics rendering, data analysis or what have you, you can either buy more hardware, or you can go on the cloud.

毕竟，这就是计算机的用途：计算，处理数据和计算。 如果需要更快的处理来进行图形渲染，数据分析或您拥有什么，可以购买更多硬件，也可以上云。

Sure, if you buy the hardware you own it, it’s an asset, but you’re also paying for all of the idle time when the computers are not doing any actual processing, plus all of the maintenance that comes with it, which can go really high if you build a data center.

当然，如果您购买拥有的硬件，这是一项资产，但是您还需要为计算机没有进行任何实际处理的所有空闲时间以及可能附带的所有维护费用付出代价。如果您构建数据中心，那确实很高。

When you go on the cloud, on the other hand, you just pay for what you use and you can scale to thousands of processing nodes in a few minutes (and blow your credit card while at it, if you’re not careful).

另一方面，当您使用云时，只需支付使用的费用，即可在几分钟内扩展到数千个处理节点(如果不小心，还可以同时刷信用卡)。

Elastic Compute Cloud (EC2) is Amazon’s flagship for scalable computing on demand, competing with Google’s Compute Engine and Azure’s Virtual Machines and Virtual Machine Scale Sets. Amazon’s service is the most comprehensive, but as mentioned, the pricing for EC2 can get very intricate, and the same goes for Azure’s VMs pricing. Google’s offering is somewhat less flexible, but the pricing is a lot easier to follow (see pricing section).

弹性计算云(EC2)是亚马逊按需可扩展计算的旗舰， 可与Google的Compute Engine和Azure的虚拟机和虚拟机规模集竞争。 Amazon的服务是最全面的服务，但是如上所述， EC2的定价可能非常复杂，Azure的VM定价也是如此 。 Google的产品灵活性较差，但是定价更容易遵循 (请参阅定价部分)。

There’s also the option of renting computing processes for web and mobile apps, which can offer significant savings when used instead of EC2 or Compute Engine if your apps fit in the specs of this service (see AWS Elastic Beanstalk and Google App Engine for more details).

还可以选择租用Web和移动应用程序的计算流程，如果您的应用程序符合此服务的规格，则可以代替EC2或Compute Engine节省大量资金(有关更多详细信息，请参阅AWS Elastic Beanstalk和Google App Engine )。 。

If you want to deploy software containers with Docker, you should look at Amazon’s EC2 Container Service (ECS) and EC2 Container Registry (ECR); Google’s equivalent are Container Engine and Container Registry. Azure’s also on board with Docker with its Container Service, though at the moment they are not providing a facility for private Docker registries.

如果要使用Docker部署软件容器 ，则应查看Amazon的EC2容器服务(ECS)和EC2容器注册表(ECR) ； Google的等效项是Container Engine和Container Registry 。 Azure还通过其容器服务支持Docker，尽管目前它们还没有为私有Docker注册中心提供便利。

Azure, since it’s Microsoft’s, also allows you to deploy Windows client apps with its RemoteApp service.

Azure，因为它是Microsoft的，所以还允许您使用其RemoteApp服务来部署Windows客户端应用程序 。

存储 (Storage)

Alongside computing, storage is a key pillar to cloud services. In the cloud you can store with the same ease anything from a bunch of GBs to several PBs (1 petabyte = 1,024 terabytes = 1,048,576 gigabytes). Beware, though: implementing these solutions is not so trivial, as this is not a regular hosting for which you just need a user and password to upload files to an FTP. Instead, you’ll need to interact with APIs or third-party programs, and it may take some time before you’re ready to operate your storage entirely in the cloud.

除计算外，存储是云服务的关键Struts。 在云中，您可以轻松地存储从几GB到几PB的所有内容(1 PB = 1,024 TB = 1,048,576千兆字节)。 但是要当心：实施这些解决方案并不是那么简单，因为这不是常规的托管，您只需要用户和密码即可将文件上传到FTP。 相反，您需要与API或第三方程序进行交互，并且准备完全在云中操作存储可能需要一些时间。

To store objects (that is, pretty much anything), Amazon Simple Storage Service (S3) is the service that’s been running the longest, and as such it has extensive documentation, including free webinars, tons of sample code and libraries, articles and tutorials and very active discussion forums where Amazon developers provide very useful feedback on a regular basis. Of course, Google Cloud Storage and Microsoft Azure Storage provide a service that’s as reliable and robust, but the resources you’ll find don’t come even close that of Amazon’s. That being said, Google and Microsoft may have an edge on the price, so read the fine print.

为了存储对象 (几乎所有东西)，Amazon Simple Storage Service(S3)是运行时间最长的服务，因此它具有广泛的文档 ，包括免费的网络研讨会 ，大量示例代码和库 ， 文章和教程。以及非常活跃的讨论论坛 ，在这些论坛上 ，Amazon开发人员会定期提供非常有用的反馈。 当然，Google Cloud Storage和Microsoft Azure Storage提供了既可靠又健壮的服务，但是您发现的资源甚至无法与Amazon的资源相提并论。 话虽如此，谷歌和微软可能在价格上有优势，因此请阅读精美文字。

1. Durable Reduced Availability
   持久性降低可用性
2. Locally Redundant Storage
   本地冗余存储
3. Geographically Redundant Storage
   地理冗余存储
4. Read-Access Geographically Redundant Storage
   读取访问地理冗余存储

For archiving, also sometimes called “cold storage” (like when you store objects you don’t plan to access regularly for the most part), you’ll enjoy lower rates but also lower access speeds, which shouldn’t be much of a problem in most cases. The characteristics and prices are very similar among different providers, so most probably you’ll be conditioned by which API you have implemented on your back-end. For the specs and details, see Amazon Glacier, Cloud Storage Nearline by Google, and Azure Backup; and check also the archiving solutions these providers offer — Data Archive by AWS, and Backup and Archive by Azure.

对于存档 (有时也称为“冷存储”)(例如，当您存储大部分不打算定期访问的对象时)，您将享受较低的价格，但访问速度也较低，这不在大多数情况下都是问题。 不同提供商之间的特征和价格非常相似，因此很可能会受到后端实现的API的限制。 有关规格和详细信息，请参阅Amazon Glacier ，Google的Cloud Storage Nearline和Azure Backup 。 并检查这些提供商提供的归档解决方案 -AWS提供的数据归档 ，以及Azure提供的备份和归档 。

Aside from storing and archiving, they provide more specific options, such as Amazon CloudFront targeted for building a content delivery network (CDN), same as Google’s Cloud CDN and Azure’s Content Delivery Network. But if you have more exotic requirements, make sure you check their sites.

除了存储和存档外，它们还提供了更多特定的选项 ，例如，针对与Google的Cloud CDN和Azure的Content Delivery Network相同的用于构建内容交付网络(CDN)的Amazon CloudFront 。 但是，如果您有更多特殊要求，请确保检查其站点。

分析工具 (Analytics)

Now we’re getting serious, as we enter the powerful place in which we integrate and make full use of computing, storage and delivery, all at once. And the truth is that there are certain things you just can’t perform anywhere else but in the cloud (that is, except you can afford a huge investment in infrastructure). So let’s talk analytics.

现在，我们正变得严肃起来，因为我们进入了一个强大的地方，我们可以一次集成并充分利用计算，存储和交付功能。 事实是，某些事情您只能在云中执行，而其他事情则无法执行(也就是说，除了您可以承担基础设施方面的巨额投资外)。 因此，让我们谈谈分析。

The challenges of big data are dealing with incredibly large data sets (so big you can’t fit them in memory), making sense of them, using them to make predictions, and even helping modeling completely new situations like new products, services, treatments, ways of planning cities, and a large et cetera.

大数据的挑战是处理难以置信的大数据集(如此之大，您无法将其容纳在内存中)，理解它们，使用它们进行预测，甚至帮助建模新情况，例如新产品，服务，治疗，规划城市的方式以及其他方面 。

This requires very specific technologies and programming models, one of which is MapReduce, which was developed by Google, so maybe it isn’t surprising to see Google walking forward in the big data arena by offering an array of products — such as BigQuery (managed data warehouse for large-scale data analytics), Cloud Dataflow (real-time data processing), Cloud Dataproc (managed Spark and Hadoop), Cloud Datalab (large-scale data exploration, analysis, and visualization), Cloud Pub/Sub (messaging and streaming data), and Genomics (for processing up to petabytes of genomic data). Elastic MapReduce (EMR) and HDInsight are Amazon’s and Azure’s take on big data, respectively. For more, check the big data solutions they all offer — GCP, AWS, and Azure.

这需要非常具体的技术和编程模型，其中之一就是由Google开发的MapReduce ，因此，看到Google通过提供一系列产品(例如BigQuery (托管)用于大规模数据分析的数据仓库)， Cloud Dataflow (实时数据处理)， Cloud Dataproc (托管的Spark和Hadoop)， Cloud Datalab (大规模数据探索，分析和可视化)， Cloud Pub / Sub (消息传递)和流数据)和基因组学 (用于处理高达PB的基因组数据)。 Elastic MapReduce(EMR)和HDInsight分别是Amazon和Azure的大数据业务。 有关更多信息，请查看它们都提供的大数据解决方案 -GCP ， AWS和Azure 。

But you don’t need to fall into the category of big data to be able to make sense of data. Large amounts of structured and even unstructured data can help you identify business opportunities. This is known as business intelligence (BI), and the strategies here can be very diverse and will vary a lot depending on your field. So if your business has the data sets, there may be valuable insights waiting to be mined. In this case, Amazon alone is taking on this niche with QuickSight.

但是您不必落入大数据类别就可以理解数据。 大量的结构化甚至非结构化数据都可以帮助您识别商机。 这就是所谓的商业智能(BI) ，此处的策略可能非常多样化，并且会因您所在的领域而有很大差异。 因此，如果您的企业拥有数据集，那么可能会有有价值的见解在等待挖掘。 在这种情况下，仅Amazon一个人就可以通过QuickSight占领这一市场。

And for all of this, you’ll most likely need to use machine learning (ML), which is a branch of artificial intelligence (AI). Interestingly, Google has the upper hand on this one too, not only offering Cloud Machine Learning for general purpose ML, but also for leveraging products they had to build for their own apps and offering interfaces for accessing them — half of them in beta — oriented to very specific uses of ML, including APIs for Cloud Vision, Cloud Speech, Cloud Natural Language and Google Translate. The general purpose alternatives are Amazon Machine Learning and Azure Machine Learning.

对于所有这些，您很可能需要使用机器学习(ML) ，它是人工智能(AI)的一个分支。 有趣的是，Google在这方面也占了上风，不仅提供用于通用ML的云机器学习 ，而且还利用他们必须为自己的应用构建的产品，并提供用于访问它们的界面(其中一半在beta中)针对ML的特定用途，包括用于Cloud Vision ， Cloud Speech ， Cloud Natural Language和Google Translate的 API。 通用替代方法是Amazon Machine Learning和Azure Machine Learning 。

地点 (Locations)

When deploying your services, you may want to choose a data center that’s close to your primary target of users. For example, if you’re doing real estate or retail hosting in the West Coast of the United States, you’ll want to deploy your services right there to minimize the latency and provide a better user experience (UX). Of course, you can still deploy from afar, but the UX will suffer.

部署服务时，您可能希望选择一个与主要用户目标相近的数据中心。 例如，如果您在美国西海岸进行房地产或零售托管，则需要在此处部署服务，以最大程度地减少延迟并提供更好的用户体验(UX)。 当然，您仍然可以从远处进行部署，但是UX会受到影响。

Amazon clearly has the most extensive coverage:

亚马逊显然拥有最广泛的覆盖范围：

AWS locations. Diagram by Amazon

AWS位置。 亚马逊图

Azure comes close, with fairly good support for Asia:

Azure即将结束，并为亚洲提供了良好的支持：

Azure locations. Diagram by Microsoft

Azure位置。 微软图解

Google has a solid coverage in the United States, but falls behind in Europe and, particularly, in Asia (only one spot in *), with no coverage at all in South America. None of them are able to deploy in Africa.

Google在美国的覆盖率很高，但在欧洲尤其是亚洲(*仅一个位置)落后，在南美却完全没有覆盖。 他们中没有一个能够在非洲部署。

Google Cloud locations

Google Cloud位置

But beware that deploying at different locations comes at different rates, with the United States and Europe — in that order — being normally the cheapest.

但是请注意，在不同位置进行部署的费用会有所不同，美国和欧洲(按此顺序)通常是最便宜的。

For more details:

更多细节：

• AWS Global Infrastructure.

  AWS全球基础设施 。

• Google Cloud Locations.

  Google Cloud位置 。

• Azure Regions.

  天青地区 。

其他产品和服务 (Other Products and Services)

As mentioned, we only got to cover some of the main cloud services, but before we finish, let’s have a quick review of some products you might want to keep an eye on.

如前所述，我们仅涵盖了一些主要的云服务，但是在结束之前，让我们快速回顾一下您可能需要关注的一些产品。

联网 (Networking)

• You can network in the cloud by doing domain name system (DNS) with Amazon’s Route 53, Google DNS or Azure DNS.

  您可以通过使用Amazon的Route 53 ， Google DNS或Azure DNS进行域名系统(DNS)来在云中联网。

• Or do load balancing with Amazon’s Elastic Load Balancing, Google Cloud Loud Balancing and Azure’s Load Balancer.

  或者做负载与亚马逊的平衡 弹性负载平衡 ， 谷歌云大声平衡和Azure的负载均衡 。

• And, of course, set your virtual private network (v*n) with Amazon’s Virtual Private Cloud VPC, Google’s Cloud Virtual Network and Azure’s v*n Gateway.

  而且，当然，可以使用Amazon的Virtual Private Cloud VPC ，Google的Cloud Virtual Network和Azure的v*n Gateway设置您的虚拟专用网络(v*n) 。

资料库 (Databases)

• You can implement SQL solutions with Amazon’s Relational Database Service (RDS) (supporting many DBMS), Google’s Cloud SQL (supporting only MySQL at the moment), and with Azure’s SQL Database, SQL Data Warehouse and SQL Server Stretch Database.

  您可以使用Amazon的关系数据库服务(RDS) (支持许多DBMS)，Google的Cloud SQL (目前仅支持MySQL)以及Azure的SQL数据库 ， SQL数据仓库和SQL Server Stretch Database来实现SQL解决方案 。

• Also newer NoSQL solutions are available with Amazon’s DynamoDB, Google’s Bigtable and Cloud Datastore, and Azure’s DocumentDB and Table storage.

  Amazon的DynamoDB ，Google的Bigtable和Cloud Datastore以及Azure的DocumentDB和Table存储还提供了更新的NoSQL解决方案 。

• But who wants a DB when all you need is a cache? If that’s the case, then Amazon’s ElastiCache and Azure’s Redis Cache might do it.

  但是，当您只需要一个缓存时，谁想要数据库？ 如果是这样，那么Amazon的ElastiCache和Azure的Redis Cache可能会做到这一点。

• For more, check the solution that amazon offers — Cloud Databases with AWS.

  有关更多信息，请检查亚马逊提供的解决方案 - 带有AWS的云数据库 。

开发人员工具，管理，安全性，身份，灾难恢复…… (Developers tools, management, security, identity, disaster recovery …)

And there’s even more, but let’s call it a day for now!

甚至还有更多，但现在让我们称之为一天！

其他玩家 (Other Players)

We only got to cover big names here, but the cloud arena is very dynamic, and there are several providers offering solid infrastructure at very competitive prices. Many of them are focusing on the needs of developers rather than companies, and they may be well worth trying, especially if your scaling needs are between small and moderate.

我们只需要在这里介绍一些大牌公司，但是云计算领域非常活跃，有多家提供商以非常具有竞争力的价格提供了坚实的基础架构。 他们中的许多人专注于开发人员而不是公司的需求，并且它们很值得尝试，特别是如果您的扩展需求介于中小型之间。

Some alternatives:

一些替代方案：

• Brightbox. UK-based cloud hosting for teams that insist on 100% uptime.

  Brightbox 。 总部位于英国的云托管服务，用于坚持100％正常运行时间的团队。

• Codero. Managed, dedicated and cloud hosting.

  Codero 。 托管，专用和云托管。

• DigitalOcean. Cloud computing, designed for developers with competitive prices.

  DigitalOcean 。 云计算，专为具有竞争力的价格的开发人员而设计。

• Kyup. Scalable cloud hosting on Linux containers.

  y Linux容器上的可扩展云托管。

• Linode. High performance SSD Linux servers for many infrastructure needs.

  Linode 。 满足许多基础架构需求的高性能SSD Linux服务器。

• Packet. A base metal cloud build for developers.

  包 。 开发人员的基础金属云构建。

• Rackspace Cloud. Powered by OpenStack, an open source technology.

  机架空间云 。 由开源技术OpenStack提供支持。

• SoftLayer (IBM Cloud). Another option for businesses with widespread locations, backed by IBM.

  SoftLayer(IBM云) 。 在IBM的支持下，适用于分布广泛的企业。

• Vultr. High performance SSD cloud available in 14 cities worldwide, with competitive prices.

  Vultr 。 高性能SSD云在全球14个城市可用，价格具有竞争力。

接下来做什么 (What to Do Next)

Amazon, Google and Microsoft, and nearly all of the alternatives we discussed, offer starting deals and even free credit for new accounts, meaning that you can start experimenting in the cloud without taking your credit card and with no future obligations.

亚马逊，谷歌和微软以及我们讨论过的几乎所有替代方案都提供了启动交易，甚至为新帐户提供免费信贷，这意味着您可以在不使用信用卡且无需承担未来义务的情况下开始在云中进行实验。

And don’t get paralyzed! There may be plentiful options, but you can start by focusing on what your needs are. If you need a very specific solution, or certain locations, or if you’re just a humble developer who might be better off with a smaller company, start from there.

而且不要瘫痪 ！ 可能有很多选择，但您可以先着眼于自己的需求。 如果您需要一个非常特定的解决方案或某些位置，或者您只是一个谦虚的开发人员，而在一家较小的公司中可能会更好，那么从这里开始。

Learn more about AWS with our Getting Started with AWS screencast.

通过我们的AWS 入门截屏视频，了解有关AWS的更多信息。

翻译自: https://www.sitepoint.com/a-side-by-side-comparison-of-aws-google-cloud-and-azure/

azure 和 aws比较