CAST.AI vs Kubernetes

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CAST.AI

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Kubernetes

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Decisions about CAST.AI and Kubernetes
Simon Reymann
Senior Fullstack Developer at QUANTUSflow Software GmbH · | 29 upvotes · 5M views

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.
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Pros of CAST.AI
Pros of Kubernetes
  • 3
    Eks cost optmization
  • 3
    Easy to start
  • 2
    Eks analyzer
  • 2
    One cluster for multicloud
  • 2
    Aws cost optimization
  • 2
    Outage prevention via multicloud
  • 162
    Leading docker container management solution
  • 126
    Simple and powerful
  • 104
    Open source
  • 75
    Backed by google
  • 56
    The right abstractions
  • 24
    Scale services
  • 19
    Replication controller
  • 10
    Permission managment
  • 7
    Simple
  • 7
    Cheap
  • 7
    Supports autoscaling
  • 4
    Reliable
  • 4
    Self-healing
  • 4
    No cloud platform lock-in
  • 3
    Quick cloud setup
  • 3
    Open, powerful, stable
  • 3
    Scalable
  • 3
    Promotes modern/good infrascture practice
  • 2
    Captain of Container Ship
  • 2
    A self healing environment with rich metadata
  • 2
    Cloud Agnostic
  • 2
    Runs on azure
  • 2
    Backed by Red Hat
  • 2
    Custom and extensibility
  • 1
    Golang
  • 1
    Expandable
  • 1
    Gke
  • 1
    Easy setup
  • 1
    Sfg
  • 1
    Everything of CaaS

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Cons of CAST.AI
Cons of Kubernetes
    Be the first to leave a con
    • 15
      Poor workflow for development
    • 15
      Steep learning curve
    • 8
      Orchestrates only infrastructure
    • 4
      High resource requirements for on-prem clusters
    • 2
      Too heavy for simple systems
    • 1
      Additional vendor lock-in (Docker)
    • 1
      More moving parts to secure
    • 1
      Additional Technology Overhead

    Sign up to add or upvote consMake informed product decisions

    What is CAST.AI?

    It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

    What is Kubernetes?

    Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

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    Jobs that mention CAST.AI and Kubernetes as a desired skillset
    CBRE
    United States of America Texas Richardson
    CBRE
    United States of America Texas Richardson
    Pinterest
    San Francisco, CA, US; Atlanta, GA, US; New York, NY, US
    CBRE
    United States of America Texas Dallas
    CBRE
    United States of America Texas Dallas
    What companies use CAST.AI?
    What companies use Kubernetes?
      No companies found
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      What tools integrate with CAST.AI?
      What tools integrate with Kubernetes?

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      Blog Posts

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      Rancher Labs

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      Rancher Labs

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      What are some alternatives to CAST.AI and Kubernetes?
      Docker Compose
      With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.
      Amazon CloudWatch
      It helps you gain system-wide visibility into resource utilization, application performance, and operational health. It retrieve your monitoring data, view graphs to help take automated action based on the state of your cloud environment.
      Spring Cloud
      It provides tools for developers to quickly build some of the common patterns in distributed systems.
      Helm
      Helm is the best way to find, share, and use software built for Kubernetes.
      Rancher
      Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.
      See all alternatives