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Why Google Cloud Platform services deserve a second look

Google Cloud services have evolved significantly over the past year, with an eye toward the enterprise. But there's still room for improvement in 2017.

The popularity of Amazon Web Services' public cloud makes it easy to overlook other large, competitive infrastructure as a service options, such as Google Cloud Platform. Most people are familiar with Google's cloud offerings through its online productivity software, Google Apps, which has been rebranded as G-Suite. However, its Google Cloud Platform services make it a serious cloud service competitor due to its infrastructure as a service option, known as Google Compute Engine, and platform as a service option, known as Google App Engine.

But while the cloud provider made a series of steps in 2016 aimed at broadening its enterprise appeal, there is still work to be done in terms of integrating Google Cloud Platform services with on-premises legacy workloads.

Let's examine the progress Google has made so far this year, including recent updates to its cloud platform, as well as the items that remain on cloud admins' wish list for 2017.

Google Cloud Platform services and features

Google Cloud Platform operates from redundant data centers in five regions, with several others set to open by 2017. The technology builds on the same infrastructure and data centers used for Google's consumer services, such as search, Gmail, Maps and YouTube. Because of this, few companies match Google's scale at building, optimizing and managing hyperscale infrastructure.

Like Amazon Web Services (AWS), Google Cloud Platform has connected, but geographically distributed, infrastructure deployed in regions and availability ; the former is a group of data centers in close proximity to enable automatic, site-level redundancy, while are widely separated regions that are isolated and independent. Google Cloud Platform reduces latency and improves performance through the synchronization of data between regions.

Google Cloud Platform services fall into four main categories:

  • Compute: Google Compute Engine for infrastructure as a service (IaaS); Google App Engine for platform as a service; and Google Container Engine, a set of Docker container images with cluster management and automation using Kubernetes.
  • Storage: Google Cloud Storage
  • Networking: Google Cloud DNS and Interconnect
  • Databases: Google Cloud SQL, Google Cloud Datastore and Google Cloud Bigtable

Key differentiators in pricing

Compared to some other IaaS offerings, Google Cloud Platform services have greater granularity in billing and provide a simple model for usage-based discounts.

Key features of Google's pricing structure include:

  • Per-minute granularity: Usage for compute instances is calculated by the minute, with a 10-minute minimum, whereas AWS, by comparison, rounds to the nearest hour.
  • Automatic discounts for sustained use: When an instance is used for more than 25% of a month, Google automatically applies a 20% discount for each incremental minute. Usage exceeding 50% and 75% a month gets escalating discounts, so the net discount for a month of full-time use is 30%.
  • Custom-sized machine types: Like all IaaS offerings, Google Cloud Platform has standard t-shirt sizes and rates for compute instances. It also allows users to define custom sizes with proportionate pricing for workloads that don't conveniently fit into one of the predefined types. Custom instances can range from 1 to 32 virtual CPUs with up to 6.5 GB of RAM per vCPU.

In addition, Google has reduced usage rates to reflect the declining costs of infrastructure, which together with its pricing model can result in savings. A commissioned paper from analyst firm Enterprise Strategy Group found savings over AWS of 15% to 50% for a mature enterprise application deployment.

Google's higher-level services

Like all IaaS providers, Google layers higher-level services on top of its basic infrastructure services. These include:

  • App notification: Google Cloud Pub/Sub
  • Identity management and security: Google Cloud IAM, Google Cloud Resource Manager and Google Cloud Security Scanner
  • Big data analytics: Google Cloud Dataflow, Google Cloud Dataproc, Google Datalab and Google BigQuery
  • Machine learning: model-driven algorithms using TensorFlow, image analysis, speech recognition and natural language processing and translation
  • Management and automation: Stackdriver, Trace, Google Cloud Deployment Manager, Google Cloud Shell, Google Cloud Console and various service and billing APIs

Room for improvement

Under the leadership of former VMware CEO Diane Greene, Google Cloud Platform is beefing up monitoring, logging, automation, identity management and networking features to attract enterprise customers. Additionally, Google Cloud Platform is focusing on application containerization by making a technology that Google itself has long used to streamline deployments and improve infrastructure efficiency available to public cloud users.

Although customers can run Linux and Windows applications in a virtual machine, it isn't easy to integrate Google Cloud Platform services with legacy, on-premises virtualization management platforms, such as VMware or Microsoft System Center. This makes Google Cloud Platform a poor choice for cloud laggards and organizations looking for a place to offload legacy virtual infrastructure and applications.

Google Cloud Platform, instead, is ideal for cloud-native applications, particularly those using big data analytics or machine learning. Its container and automation features also make it a good platform for organizations that have adopted DevOps, continuous integration and delivery processes, and microservice-based application architectures.

Next Steps

Learn more about Google cloud services

Explore Google Cloud Platform options

Find out of Google Compute Engine fits your budget

Share Google VPC networks across projects

Dig Deeper on Google and other public cloud providers