Google Compute Engine
Compute Engine (IaaS), a platform from Google that allows organizations to create and manage cloud-based virtual machines, is an infrastructure as a services (IaaS).
Computing infrastructure in predefined sizes or custom machine shapes to accelerate cloud transformation. General purpose machines (E2, N1,N2,N2D) offer a good compromise between price and performance. Compute optimized machines (C2) offer high-end performance vCPUs for compute-intensive workloads. Memory optimized (M2) systems offer the highest amount of memory and are ideal for in-memory database applications. Accelerator optimized machines (A2) are based on A100 GPUs, and are designed for high-demanding applications. Integrate Compute services with other Google Cloud Services, such as AI/ML or data analytics. Reservations can help you ensure that your applications will have the capacity needed as they scale. You can save money by running Compute using the sustained-use discount, and you can even save more when you use the committed-use discount.
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Google Cloud Platform
Google Cloud is an online service that lets you create everything from simple websites to complex apps for businesses of any size.
Customers who are new to the system will receive $300 in credits for testing, deploying, and running workloads. Customers can use up to 25+ products free of charge.
Use Google's core data analytics and machine learning. All enterprises can use it. It is secure and fully featured. Use big data to build better products and find answers faster. You can grow from prototypes to production and even to planet-scale without worrying about reliability, capacity or performance. Virtual machines with proven performance/price advantages, to a fully-managed app development platform. High performance, scalable, resilient object storage and databases. Google's private fibre network offers the latest software-defined networking solutions. Fully managed data warehousing and data exploration, Hadoop/Spark and messaging.
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LocalOps
LocalOps provides a contemporary cloud-agnostic internal developer platform designed for streamlined engineering teams utilizing AWS, Google Cloud, or Azure, particularly those who lack DevOps expertise or are hindered by slow release cycles due to DevOps constraints. Teams can achieve a developer experience similar to Vercel, Fly, or Heroku directly within their own cloud infrastructure.
By linking their AWS, GCP, or Azure accounts along with their GitHub repositories, teams can launch services in less than 30 minutes without the need to manually configure AWS resources, create Dockerfiles, set up CI/CD pipelines, or write Terraform scripts. They gain self-service access to AWS, enabling automatic deployments through Git push, and can monitor logs and metrics from the outset with a pre-configured open-source monitoring setup that includes Grafana, Prometheus, and Loki. Additionally, they can scale resources infinitely on their own cloud account at a significantly reduced cost, and any available cloud credits can be utilized to cover the expenses of cloud resources. Ultimately, teams can efficiently deploy, monitor, automate, and scale their applications seamlessly in their personal cloud environments.
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Google Cloud Deployment Manager
Easily create and oversee cloud resources utilizing straightforward templates. Google Cloud Deployment Manager enables you to outline all necessary resources for your application in a declarative format using YAML. Additionally, Python or Jinja2 templates can be employed to parameterize the configuration, facilitating the reuse of standard deployment methods like a load-balanced, auto-scaled instance group. By considering your configuration as code, you can achieve repeatable deployments effortlessly. Through the creation of configuration files that delineate the resources, the resource creation process can be replicated consistently and reliably. Unlike many tools that follow an imperative approach, which requires users to specify each step involved in resource creation and configuration, a declarative approach empowers users to define desired configurations and allows the system to determine the necessary steps. This shift in focus lets users concentrate on the collective resources that make up their application or service rather than managing each resource in isolation. Ultimately, this methodology streamlines the deployment process, enhancing efficiency and reliability.
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