云服务提供了从完全托管到较少托管的一系列选项。管理较少的服务为开发人员提供了更多的控制。计算和应用引擎的区别也是一样的。下面的图片更详细地说明了这一点

我会用一种对我来说有意义的方式来解释:

Compute Engine: If you are do-it-yourself person or have an IT team and you just want to rent a computer on cloud that has specific OS (for example linux), you go for the Compute Engine. You have to do everything by yourself. App Engine: If you are (for example) a python programmer and you want to rent a pre-configured computer on cloud that has Linux with a running web-server and the latest python 3 with necessary modules and some plug-ins to integrate with other external services, you go for the App Engine. Serverless Container (Cloud Run): If you would like to deploy the exact image of your local setup environment (for example: python 3.7+flask+sklearn) but you do not want to deal with server, scaling, etc. You create a container on your local machine (through docker) and then deploy it to Google Run. Serverless Microservice (Cloud Functions): If you want to write bunch of APIs (functions) that do specific job, you go for google Cloud Functions. You just focus on those specific functions, the rest of the job (server, maintenance, scaling, etc.) is done for you in order to expose your functions as microservices.

随着深入，你会失去一些灵活性，但你不必担心不必要的技术方面。你也多花了一点，但你节省了时间和成本(IT部分):其他人(谷歌)正在为你做这件事。

如果你不想关心负载平衡、伸缩性等，将你的应用程序分割成一堆“无状态”的web服务是至关重要的，这些服务将任何持久化的内容写入单独的存储(数据库或blob存储)。然后你会发现云运行和云函数是多么棒。

就我个人而言，我发现谷歌Cloud Run是一个很棒的解决方案，在开发中绝对自由(只要是无状态的)，将其作为web服务公开，docker您的解决方案，与Cloud Run一起部署。让谷歌成为你的IT和DevOps，你不需要关心扩展和维护。

我已经尝试了所有其他的选择，每一个都适合不同的目的，但谷歌运行只是棒极了。对我来说，它是真正的无服务器，而不会失去开发的灵活性。

谷歌计算引擎(GCE)

托管在云中的虚拟机(vm)。在云技术出现之前，这些服务器通常被称为虚拟专用服务器(VPS)。您可以像使用物理服务器一样使用这些服务器，在其中安装和配置操作系统，安装应用程序，安装数据库，保持操作系统的最新状态，等等。这被称为基础设施即服务(IaaS)。

当您在数据中心的VM或服务器上运行现有应用程序，并希望轻松地将其迁移到GCP时，VM最有用。

谷歌应用引擎

App Engine托管并运行你的代码，而不需要你处理操作系统、网络，以及许多你必须在物理服务器或VM上管理的其他事情。可以把它看作一个运行时，它可以自动部署、版本化和扩展应用程序。这被称为平台即服务(PaaS)。

当你想要自动部署和自动扩展你的应用程序时，App Engine是最有用的。除非应用程序需要自定义操作系统配置，否则App Engine通常比手动配置和管理虚拟机更有优势。

谷歌计算引擎(GCE)是基础设施即服务(IaaS)，而谷歌应用程序引擎(GAE)是平台即服务(PaaS)。你可以查看下面的图表，以更好地理解差异(从这里更好地解释)-

Google Compute Engine GCE is an important service provided from Google Cloud Platform (GCP) since most of the GCP services use GCE instances (VMs) beneath the management layer (not sure which one don't). This includes App Engine, Cloud Functions, Kubernetes Engine (Earlier Container Engine), Cloud SQL, etc. GCE instances are the most customisable unit there and thus should only be used when your application can't run on any other GCP services. Most of the time people use GCE to transfer their On-Prem applications to GCP, since it requires minimal changes. Later, they can choose to use other GCP services for separate component of their apps.

谷歌应用引擎 GAE是GCP提供的第一个服务(早在谷歌进入云业务之前)。它从0自动扩展到无限实例(它在下面使用GCE)。它有标准环境和灵活环境两种口味。

标准环境非常快，当没有人使用你的应用程序时，可以缩小到0个实例，在几秒钟内扩大和缩小，并有专用的谷歌服务和库用于缓存，身份验证等。标准环境的警告是，它是非常限制性的，因为它运行在沙箱中。您必须仅针对特定的编程语言使用托管运行时。最近添加的是Node.js (8.x)和Python 3.x。旧的运行时可用于Go, PHP, Python 2.7, Java等。

Flexible Environment更加开放，因为它允许您在使用docker容器时使用自定义运行时。因此，如果您的运行时在提供的运行时中不可用，您总是可以为执行环境创建自己的dockerfile。需要注意的是，它要求至少有一个实例在运行，即使没有人在使用你的应用，再加上放大和缩小需要几分钟。

不要将GAE flexible与Kubernetes Engine混淆，因为后者使用了实际的Kubernetes，并提供了更多的自定义和特性。当您需要无状态容器并且应用程序仅依赖HTTP或HTTPS协议时，GAE Flex非常有用。对于其他协议，Kubernetes Engine (GKE)或GCE是您唯一的选择。看看我的另一个答案，你会有更好的解释。

In addition to the App Engine vs Compute Engine notes above the list here also includes a comparison with Google Kubernete Engine and some notes based on experience with a wide range of apps from small to very large. For more points see the Google Cloud Platform documentation high level description of features in App Engine Standard and Flex on the page Choosing an App Engine Environment. For another comparison of deployment of App Engine and Kubernetes see the post by Daz Wilkin App Engine Flex or Kubernetes Engine.

应用引擎标准

Pros

Very economical for low traffic apps in terms of direct costs and also the cost of maintaining the app. Auto scaling is fast. Autoscaling in App Engine is based on lightweight instance classes F1-F4. Version management and traffic splitting are fast and convenient. These features are built into App Engine (both Standard and Flex) natively. Minimal management, developers need focus only on their app. Developers do not need to worry about managing VMs in a reliable, as in GCE, or learning about clusters, as with GKE. Access to Datastore is fast. When App Engine was first released, the runtime was co-located with Datastore. Later Datastore was split out as the standalone product Cloud Datastore but the co-location of App Engine Standard serving with Datastore remains. Access to Memcache is supported. The App Engine sandbox is very secure. Compared with development on GCE or other virtual machines, where you need to do your own diligence to prevent the virtual machine from being taken over at the operating system level, the App Engine Standard sandbox is relatively secure by default.

Cons

实例通常比其他环境更受约束 小。虽然这对快速自动缩放很有好处，但许多应用程序都可以 受益于更大的实例，例如GCE实例大小可达96 内核。 网络没有与GCE集成 不能把应用引擎后面的谷歌云负载均衡器。局限于 支持的运行时:Python 2.7, Java 7和8,Go 1.6-1.9和PHP 5.5. 在Java中，有一些对servlet的支持，但不支持完整的J2EE标准。

App Engine Flex

Pros

可以使用自定义运行时吗 本机集成GCE网络 版本和流量管理方便，与标准相同 较大的实例大小可能更适合大型复杂应用程序，特别是可能使用大量内存的Java应用程序

Cons

网络集成不完善——没有与内部负载均衡器或共享虚拟私有云集成 对托管Memcache的访问通常不可用

谷歌Kubernetes引擎

Pros

Native integration with containers allows custom runtimes and greater control over cluster configuration. Embodies many best practices working with virtual machines, such as immutable runtime environments and easy ability to roll back to previous versions Provides a consistent and repeatable deployment framework Based on open standards, notably Kubernetes, for portability between clouds and on-premises. Version management can accomplished with Docker containers and the Google Container Registry

Cons

Traffic splitting and management is do-it-yourself, possibly leveraging Istio and Envoy Some management overhead Some time to ramp up on Kubernetes concepts, such as pods, deployments, services, ingress, and namespaces Need to expose some public IPs unless using Private Clusters, now in beta, eliminate that need but you still need to provide access to locations where kubectl commands will be run from. Monitoring integration not perfect While L3 internal load balancing is supported natively on Kubernetes Engine, L7 internal load balancing is do-it-yourself, possibly leveraging Envoy

计算引擎

Pros

Easy to ramp up - no need to ramp up on Kubernetes or App Engine, just reuse whatever you know from previous experience. This is probably the main reason for using Compute Engine directly. Complete control - you can leverage many Compute Engine features directly and install the latest of all your favorite stuff to stay on the bleeding edge. No need for public IPs. Some legacy software may be too hard to lock down if anything is exposed on public IPs. You can leverage the Container-Optimized OS for running Docker containers

Cons

Mostly do-it-yourself, which can be challenging to do adequately for reliability and security, although you can reuse solutions from various places, including the Cloud Launcher. More management overhead. There are many management tools for Compute Engine but they will not necessarily understand how you have deployed your application, like the App Engine and Kubernetes Engine monitoring tools do Autoscaling is based on GCE instances, which can be slower than App Engine Tendency is to install software on snowflake GCE instances, which can be some effort to maintain

App Engine是一个虚拟服务器。 计算引擎——它就像一个完整的服务器。