Need advice about which tool to choose?Ask the StackShare community!
Google App Engine vs Kubernetes: What are the differences?
Introduction
This Markdown code provides a comparison between Google App Engine (GAE) and Kubernetes, highlighting their key differences.
Deployment and Scaling: In Google App Engine (GAE), the deployment and scaling of applications are handled automatically by Google, with the platform managing the underlying infrastructure and resources. On the other hand, Kubernetes allows more granular control over deployment and scaling, giving users the flexibility to define and manage these aspects themselves.
Container Orchestration: While both GAE and Kubernetes support running applications in containers, their approaches to container orchestration differ. GAE provides a fully managed environment where developers only need to focus on their application code, while Kubernetes offers a container orchestration platform that allows for more advanced operations and control, such as automatic scaling and managing container lifecycle.
Flexibility and Customization: Google App Engine abstracts away much of the infrastructure and configuration details, offering a more opinionated and simplified experience. Kubernetes, on the other hand, gives users full control and flexibility over their infrastructure and application stack, enabling customization and the use of various tools and technologies.
Multi-Cloud and Hybrid Deployments: Kubernetes is designed to work across multiple cloud providers and even on-premises environments, allowing for hybrid and multi-cloud deployments. Google App Engine, on the other hand, is tightly integrated with Google Cloud Platform and is optimized for running applications specifically on Google's infrastructure.
Resource Utilization and Efficiency: Kubernetes provides extensive features to optimize resource utilization, including resource allocation, scheduling, and auto-scaling based on metrics like CPU and memory usage. While Google App Engine also handles scaling automatically, Kubernetes offers more advanced resource management capabilities, enabling fine-tuning and optimization for specific application requirements.
Monitoring and Debugging: Kubernetes offers a range of built-in monitoring and debugging features, including logging, health checks, and metric collection. Google App Engine also provides monitoring capabilities, but Kubernetes exposes more advanced functionalities and integrations with various monitoring tools and frameworks.
In summary, Google App Engine is a fully managed platform that simplifies deployment and scalability, granting less control but ease of use, while Kubernetes offers more flexibility and control over deployment, scaling, and infrastructure, enabling sophisticated container orchestration and multi-cloud support.
Our whole DevOps stack consists of the following tools:
- GitHub (incl. GitHub Pages/Markdown for Documentation, GettingStarted and HowTo's) for collaborative review and code management tool
- Respectively Git as revision control system
- SourceTree as Git GUI
- Visual Studio Code as IDE
- CircleCI for continuous integration (automatize development process)
- Prettier / TSLint / ESLint as code linter
- SonarQube as quality gate
- Docker as container management (incl. Docker Compose for multi-container application management)
- VirtualBox for operating system simulation tests
- Kubernetes as cluster management for docker containers
- Heroku for deploying in test environments
- nginx as web server (preferably used as facade server in production environment)
- SSLMate (using OpenSSL) for certificate management
- Amazon EC2 (incl. Amazon S3) for deploying in stage (production-like) and production environments
- PostgreSQL as preferred database system
- Redis as preferred in-memory database/store (great for caching)
The main reason we have chosen Kubernetes over Docker Swarm is related to the following artifacts:
- Key features: Easy and flexible installation, Clear dashboard, Great scaling operations, Monitoring is an integral part, Great load balancing concepts, Monitors the condition and ensures compensation in the event of failure.
- Applications: An application can be deployed using a combination of pods, deployments, and services (or micro-services).
- Functionality: Kubernetes as a complex installation and setup process, but it not as limited as Docker Swarm.
- Monitoring: It supports multiple versions of logging and monitoring when the services are deployed within the cluster (Elasticsearch/Kibana (ELK), Heapster/Grafana, Sysdig cloud integration).
- Scalability: All-in-one framework for distributed systems.
- Other Benefits: Kubernetes is backed by the Cloud Native Computing Foundation (CNCF), huge community among container orchestration tools, it is an open source and modular tool that works with any OS.
Pros of Google App Engine
- Easy to deploy145
- Auto scaling106
- Good free plan80
- Easy management62
- Scalability56
- Low cost35
- Comprehensive set of features32
- All services in one place28
- Simple scaling22
- Quick and reliable cloud servers19
- Granular Billing6
- Easy to develop and unit test5
- Monitoring gives comprehensive set of key indicators5
- Really easy to quickly bring up a full stack3
- Create APIs quickly with cloud endpoints3
- No Ops2
- Mostly up2
Pros of Kubernetes
- Leading docker container management solution166
- Simple and powerful129
- Open source107
- Backed by google76
- The right abstractions58
- Scale services25
- Replication controller20
- Permission managment11
- Supports autoscaling9
- Simple8
- Cheap8
- Self-healing6
- Open, powerful, stable5
- Reliable5
- No cloud platform lock-in5
- Promotes modern/good infrascture practice5
- Scalable4
- Quick cloud setup4
- Custom and extensibility3
- Captain of Container Ship3
- Cloud Agnostic3
- Backed by Red Hat3
- Runs on azure3
- A self healing environment with rich metadata3
- Everything of CaaS2
- Gke2
- Golang2
- Easy setup2
- Expandable2
- Sfg2
Sign up to add or upvote prosMake informed product decisions
Cons of Google App Engine
Cons of Kubernetes
- Steep learning curve16
- Poor workflow for development15
- Orchestrates only infrastructure8
- High resource requirements for on-prem clusters4
- Too heavy for simple systems2
- Additional vendor lock-in (Docker)1
- More moving parts to secure1
- Additional Technology Overhead1