The combination of a vital need for IT experts among businesses and a digital skills gap is making lifelong learning increasingly critical. Beyond professional development, learning new skills offers additional rewards from building peer connections to boosting your creativity. That’s why in 2020 Krishna Deepak Nallamilli and I launched KIMO.ai to reimagine how people approach learning, especially in developing markets. Our team is building the artificial intelligence needed to generate individual learning paths through a wide range of quality digital learning content.

Google Cloud and its Startup Program have been instrumental in connecting our team with the tools, people, processes, and best practices to grow our business. 

Existing learning platforms lack engagement

Outside of traditional education settings, massive open online learning courses (MOOCs)—often modeled after university courses—can provide a flexible and affordable way to upskill or reskill. But the vast majority of people who participate in MOOC programs fail to complete courses. Based on our research, the challenge with existing learning platforms is a lack of engagement, primarily caused by limited direction on which skills to learn, whether AI, fintech, blockchain, or other in-demand disciplines.   

We’ve also received feedback that many corporate learning management systems–developed as online training systems to upskill employees–tend to be poorly designed and time-consuming to use. 

Overall, a significant challenge with most existing learning platforms is that they’re generic. For example, suppose you’re interested in learning about AI. In that case, you need AI-related coursework that applies to your industry and the job you want because AI in medicine is vastly different from AI in financial services. Today’s online learning options typically take a one-size-fits-all approach and fail to capture the nuances of what learners really need to get ahead. 

Building a future-proof learning platform 

The commitment to highly personalized, accessible learning inspired KIMO.ai, a platform that we believe is the future of education. Depending on your goals, current skills, location, and other factors, our AI-based platform will identify which coursework (and where to find those classes) to build the skills you need. The more personalized, relevant learning recommendations even take into account people’s preferences for podcasts, MOOCs, books, articles, videos, courses, publications, and more.  

In a mix of cooperation and competition we call “coopetition,” KIMO.ai will regularly recommend courses from other established online learning systems if, based on our automated assessment, it’s the best option for a learner. There’s also the option to access free content only. 

Google cultural alignment fosters trust

Our platform started with one developer exploring NLP models and Google APIs. As we’ve grown our team and launched our beta to 110,000 users in developing markets, we discovered there is a lot of interest in our platform, and we believe we can make a significant impact. In feedback forums, we also learned that we need to focus our efforts on the mobile experience to improve engagement since 99% of the beta testers use mobile devices. 

Beyond our team’s high level of trust in Google Cloud solutions, our team also appreciates the cultural alignment with Google. We value Google’s developer-centric approach and rely on tools like Dataflow for batch data processing and Cloud TPU to reliably run machine learning models with AI services on Google Cloud. We also build all of our deployments on Google Kubernetes Engine (GKE), which makes it easy to manage all our containerized workloads 

On the front end, Google App Engine makes it easy to deploy apps and experiment, and it integrates seamlessly with Firebase for authentication and more. BigQuery is our serverless data warehouse that efficiently scales to support the millions of articles, videos, and other learning resources we need to analyze to provide the targeted coursework recommendations our learners require.

As we grow our business having a network of trusted advisors is also extremely valuable. By working closely with DoIT International, the 2020 Google Cloud Global Reseller Partner of the Year, our team has access to their cloud, Kubernetes, and machine learning expertise. DoIT has already helped us quickly resolve IT issues and create analytics dashboards that give us insights to continually enhance our services. 

Building for a growing industry

The dynamic edtech market is growing rapidly and estimated to become an $11B industry by 2025. We’re proud to be part of the next wave of personalized education that has the potential to empower people in developing markets and beyond to grow their skills with coursework tailored to their exact needs and how they like to learn. This year, we will deliver our platform to at least 400,000 more people. We’re excited to see how they use it and where it takes them. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.

We’ve been saying it for years, the benefits and potential of the cloud abound. 

And yet, more than 80% of respondents in a survey of 753 business leaders point to managing cloud spend as their top organizational challenge, and these same respondents estimate that nearly 1/3 of their cloud spend is inefficient or wasted (Flexera, 2023). Many organizations are new to optimizing cloud costs and ensuring resources are used efficiently.

As your organization digitally transforms you may be realizing what other organizations are realizing too: When it comes to business value, simply migrating to the cloud isn’t enough. Achieving the full benefits of cloud requires fundamental changes to both mindset and behaviors around existing financial-management practices. It requires changing the way your disparate teams work together. 

Enter the Cloud FinOps Building Blocks

Cloud FinOps is a framework, discipline, and cultural shift combining people, processes, and technology to drive financial awareness and accountability. FinOps practices align engineering, finance, technology and business leaders and teams under a primary objective: to maximize business value from the cloud. With Cloud FinOps practices, every business stakeholder is charged not only to take responsibility for their spending and costs, but also to optimize them. These practices enable businesses to manage consumption and make sound, data-informed cloud-spend decisions. Cloud FinOps is comprised of five building blocks:

1. Accountability and enablement
   Establishing governance and policies to manage cloud spend and realize business value.
2. Measurement and realization
   Driving financial accountability and value realization with a defined set of KPIs and success metrics.
3. Cost optimization
   Providing financial visibility and recommendations of IT resource usage to optimize cloud spend.
4. Planning and forecasting
   Modernizing budgeting, forecasting, and chargeback methods to allow for iterative, innovative and cost effective development practices.
5. Tools and accelerators
   Deploying and integrating a set of cloud cost tooling to effectively manage and track cloud spend. Learn more here. 

For a general overview of the Cloud FinOps framework and more on the five building blocks, check out these resources:

• Video | What is FinOps and 3 reasons why you should care about it.
  This 5-minute video provides an overview of FinOps, the 5 building blocks, and how they can benefit your organization. 
• Podcast | FinOps with Joe Daly
  In this podcast, Joe Daly of the FinOps Foundation shares about the key principles of FinOps, which he refers to as financial DevOps. Daly discusses how this framework is helping companies make better and more efficient financial decisions while taking advantage of the cloud.
• Blog | Decoding Cloud FinOps to accelerate digital transformation
  This blogpost discusses the critical role of FinOps in a successful digital transformation. It outlines key metrics to help measure and track business value and to increase visibility into the effects of digital transformation on top-line revenue. 
• Article | Cloud FinOps: The secret to unlocking the economic potential of public cloud
  This Forbes article profiles OpenX, the first major advertising exchange platform to migrate entirely to the cloud. It details the 5 key pillars of the Cloud FinOps framework, which OpenX leveraged in their digital transformation strategy. In just 9 months, they reduced their per-unit costs by more than 60%.  

Importantly, Cloud FinOps isn’t about saving money; it’s about making money. It’s about promoting a cost-conscious culture, financial accountability, and business agility in the cloud. Whatever stage of the cloud journey you’re at, cloud FinOps practices will help you get the most value out of Google Cloud. This framework can help to remove blockers, implement the building blocks, and empower your teams to make better business decisions. 

The Cloud FinOps Journey 

Implementing Cloud FinOps is neither a destination nor a box your organization will check then archive. Rather, Cloud FinOps is an ongoing journey and discipline. It’s inherently iterative. As such, growth and maturity across processes, capabilities, and domains requires action, repetition, and continuous learning. 

Across the five FinOps building blocks, we’ve identified 50 subprocesses to best understand organizations’ FinOps proficiency, capabilities, practice domains, and blind spots. We scale them from 1 to 5 and categorize them in one of three phases of maturity: Crawl, Walk, or Run. Organizations in the Crawl phase tend to focus on technical problem solving and cloud-cost visibility. Organizations in the Walk phase emphasize strategic improvements such as employing cost visibility dashboards to realize better business value. And organizations in the Run phase are focused primarily on transformational change and strategic innovation, factoring cost considerations into both processes and cloud architecture. 

Through this “crawl, walk, run” maturity model, we can evaluate proficiency, establish a benchmark, and recommend a targeted action plan for FinOps adoption. And whatever your level of maturity, your organization can take quick scalable action not only to foster improvement but also to evaluate outcomes and gain insights. 

The key here is that regardless of your organization’s Cloud FinOps maturity level, you can take small steps now toward continuous improvement. Here are some common focus areas and several more resources organized by maturity level that you can access.

Crawl phase 

Improve cloud-cost visibility. 

• Whitepaper | Drive Cloud FinOps at scale with Google Cloud Tagging
  Tags and labels can be useful and flexible tools to help your organization segment cloud spend and allocate costs. This whitepaper introduces Google Cloud Tags and best practices for implementing them. It differentiates tags, which offer reliable reporting and governance features, from labels, which can be prone to problems, including poor coverage and a lack of integrity in data labeling. 
• Whitepaper | Unlocking the value of Cloud FinOps with a new operating model
  This white paper unpacks the details of the FinOps operating model, including roles, organizational alignment, and driving culture change. It details how to establish strong financial governance and a cost-conscious culture. 
• Whitepaper | Cloud FinOps: Shared services cost allocation
  In this whitepaper, you’ll explore the elements of cost allocation as well as the complexities and challenges associated with shared-services cost allocation. While some of these concepts and models are interchangeable between legacy and cloud environments, this whitepaper focuses primarily on cloud computing and associated services.

Walk phase

Improve business-value realization. 

• Blog | 5 key metrics to measure Cloud FinOps impact in your organization in 2022 and beyond
  To drive business growth and topline revenue, business leaders must be able to connect cloud investments to business outcomes. As such, traditional IT metrics and KPIs must continue to evolve. In this blogpost, we’ll explore five key business-value metrics aligned to the five Cloud FinOps building blocks. 
• Whitepaper | Maximize business value with Cloud FinOps
  The cloud introduces new complexity and challenges to traditional IT financial management. As such, it requires strategic financial governance, processes, and partnership across the organization. This whitepaper explains how Cloud FinOps helps enterprises that have invested in cloud to drive financial accountability and accelerate business value.

Run phase 

Improve strategic cloud innovation. 

• Whitepaper | Unit costing: The next frontier in cloud
  In this whitepaper, you’ll explore the nature of and need for cloud unit costing, the standard by which FinOps practitioners obtain full business context for their cloud costs. It features examples from cloud-first organizations that have pioneered FinOps practices. Additionally, it examines several cloud forecasting and budgeting methods, ranging from least to most rigorous. 
• Blog | You get what you pay for: Principles for designing a chargeback process
  Chargeback, a crucial Cloud FinOps capability, is the process of mapping cloud consumption to internal users within an organization. It provides transparency, facilitates accountability,  enables recovery of cloud costs, and fosters a culture of fiscal responsibility. This blogpost will walk you through some best practices in designing an effective chargeback process in Google Cloud. 

Success with Cloud FinOps

As global markets continue to face challenges, there’s never been a better time to increase the return on your cloud investments. Adopting and implementing FinOps practices will help. For some real-world examples of how organizations across a range of FinOps maturity levels have collectively saved millions of dollars on their overall cloud spend, check out these customers’ stories. 

• Video | Next 2022: Top 10 ways to lower your costs on Google Cloud with General Mills
  In this video, which highlights ten leading cloud cost optimization practices, hear how General Mills, which is on pace to increase their cloud footprint by 60%, has approached the discipline of cost savings and accelerated their adoption of Cloud FinOps to drive waste out of their cloud usage. 
• Video | How Nuro optimized their costs on Google Cloud
  In this video, you’ll get an overview of the Google Cloud FinOps framework, a deep dive on cost-optimization best practices, and hear about how startup Nuro AI has adopted their own cost-savings discipline and Cloud FinOps practice. 
• Video | How OpenX reduce per unit costs by 60%
  In this video, you’ll learn how to establish a cost center of excellence within your cloud practice, explore several cost-optimization recommendations, and hear from OpenX about how they reduced their costs on Google Cloud. 
• Case Study | How Sky saved millions with Google Cloud
  In this case study, read how a few years into their cloud adoption journey, media and entertainment company, Sky Group discovered over $1.5 million in savings and optimized costs with BigQuery, Compute Engine, and Cloud Storage. 
• Case Study | Etsy: Doing more with less cost and infrastructure
  In this case study, read how after migrating their data center and ecommerce platform to the cloud, Etsy realized more than 50% savings in compute energy and leveraged committed use discounts (CUDs) to reduce their compute costs by 42%. 

It’s important to remember that FinOps success looks different for different organizations. It’s neither a one-time fix nor a destination reached by way of a single path. But for every organization, success requires small actions, refinement, and continuous improvement. As you leverage Google Cloud FinOps resources and tools, your organization can:

• Drive financial accountability and visibility.
• Optimize cloud usage and cost efficiency.
• Enable cross organizational trust and collaboration.
• Prevent cloud-spend sprawl.
• Break down departmental silos.
• Accelerate innovation. 

Getting started with Cloud FinOps

At Google, we have a team of experts in leading FinOps practices dedicated to helping you create an actionable plan to optimize cloud spend and drive cost efficiency. We’ve created numerous resources to help you get started from any stage in the FinOps journey. 

Whitepaper | Maximize Business Value with Cloud FinOps
This whitepaper outlines steps to help your organization implement FinOps. It details required teams and processes as well as the optimal behaviors, approaches, and outcomes to help maximize your investment on Google Cloud. 

With Google Cloud FinOps, your organization can also accelerate business value in the cloud. To find out more, join us on the Google Cloud Twitter channel twice a month for open Twitter Spaces discussions or reach out to your Google Cloud Sales Representative for a 1:1 discussion.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.

While the promise of artificial intelligence transforming the manufacturing industry is not new, long-ongoing experimentation hasn’t yet led to widespread business benefits. Manufacturers remain in “pilot purgatory,” as Gartner reports that only 21% of companies in the industry have active AI initiatives in production. 

However, new research from Google Cloud reveals that the COVID-19 pandemic may have spurred a significant increase in the use of AI and other digital enablers among manufacturers. According to our data—which polled more than 1,000 senior manufacturing executives across seven countries—76% have turned to digital enablers and disruptive technologies due to the pandemic such as data and analytics, cloud, and artificial intelligence (AI). And 66% of manufacturers who use AI in their day-to-day operations report that their reliance on AI is increasing.

The top three sub-sectors deploying AI to assist in day-to-day operations are automotive/OEMs (76%), automotive suppliers (68%), and heavy machinery (67%).

In fact, Bryan Goodman, Director of Artificial Intelligence and Cloud, Ford Global Data & Insight and Analytics shares, “Our new relationship with Google will supercharge our efforts to democratize AI across our business, from the plant floor to vehicles to dealerships. We used to count the number of AI and machine learning projects at Ford. Now it’s so commonplace that it’s like asking how many people are using math. This includes an AI ecosystem that is fueled by data, and that powers a ‘digital network flywheel.’”

Moving from edge cases to mainstream business needs

Why are manufacturers now turning to AI in increasing numbers? Our research shows that companies who currently use AI in day-to-day operations are looking for assistance with business continuity (38%), helping make employees more efficient (38%), and to be helpful for employees overall (34%). It’s clear that AI/ML technology can augment manufacturing employees’ efforts, whether by providing prescriptive analytics like real-time guidance and training, flagging safety hazards, or detecting potential defects on the assembly line.

In terms of specific AI use cases called out by the research, two main areas emerged: quality control and supply chain optimization. In the quality control category, 39% of surveyed manufacturers who use AI in their day-to-day operations use it for quality inspection and 35% for product and/or production line quality checks. At Google Cloud, we often speak with manufacturers about AI for visual inspection of finished products. Using AI vision, production line workers can spend less time on repetitive product inspections and can instead focus on more complex tasks, such as root cause analysis. 

In the supply chain optimization category, manufacturers said they tapped AI for supply chain management (36%), risk management (36%), and inventory management (34%).

In our day-to-day work, we’re seeing many manufacturers rethink their supply chains and operating models to better accommodate for the increased volatility that has been brought about by the pandemic and support the secular trend of consumers asking for increasingly individualized products. We’ll share more on deglobalization in the third installment of our manufacturing insights series.

AI use differs by geography, but not for the reasons you may think

The extent to which AI is already being used today varies quite strongly between geographies, according to our research. While 80% and 79% of manufacturers in Italy and Germany respectively report using AI in day-to-day operations, that percentage plummets in the United States (64%), Japan (50%) and Korea (39%).

It’s tempting to state this disparity is due to an “AI talent gap.” Although the most common barrier, just a quarter (23%) of manufacturers surveyed believe they don’t have the talent to properly leverage AI. Cost, too, does not appear to be a roadblock (21% of those surveyed). Rather, from our observations, the missing link appears to be having the right technology platform and tools to manage a production-grade AI pipeline. This is obviously the focus of our efforts and others in the space, as we believe the cloud can truly help the industry make a step change.

Looking ahead: The Golden Age of AI for manufacturing

The key to widespread adoption of AI lies in its ease of deployment and use. As AI becomes more pervasive in solving real-world problems for manufacturers, we see the industry moving away from “pilot purgatory” to the “golden age of AI.” The manufacturing industry is no stranger to innovation, from the days of mass production, to lean manufacturing, six sigma and, more recently, enterprise resource planning. AI promises to bring even more innovation to the forefront. 

To learn more about these findings and more, download our infographic here and our full report here. 

^{Research methodology
The survey was conducted online by The Harris Poll on behalf of Google Cloud, from October 15 – November 4, 2020, among 1,154 senior manufacturing executives in France (n=150), Germany (n=200), Italy (n=154), Japan (n=150), South Korea (n=150), the UK (n=150), and the U.S. (n=200) who are employed full-time at a company with more than 500 employees, and who work in the manufacturing industry with a title of director level or higher. The data in each country were weighted by number of employees to bring them into line with actual company size proportions in the population. A global post-weight was applied to ensure equal weight of each country in the global total.}

As the pace of digital transformation accelerates, our partnership with VMware continues to focus on helping customers successfully navigate their cloud journey and achieve their business objectives through seamless and rapid migration of business critical VMware workloads.

We announced the general availability of Google Cloud VMware Engine in May 2020. Since then we have worked closely with VMware to make it easier for customers to quickly migrate and run business-critical, VMware-based workloads on Google Cloud. Customers are already leveraging the service across a variety of use cases including application migration, datacenter exit, virtual desktop infrastructure, disaster recovery, and spinning up new capacity quickly to meet business needs.

For example, retailer Carrefour migrated its on-premises VMware workloads to Google Cloud without disruption to shoppers or employees while reducing operating costs by 40% and energy consumption by 45%. Once in the cloud, Carrefour was able to leverage its data and AI to deliver innovative customer experiences across online and in-store channels. Similarly, telecommunications provider Mitel migrated thousands of VMware instances across 30 data centers to Google Cloud in less than 90 days, quickly achieving increased stability, scale, and security.

Today, we announced the continued growth of the Google Cloud and VMware partnership with the addition of Google Cloud VMware Engine within VMware Cloud Universal. Google Cloud VMware Engine delivers a cloud-native VMware experience and enables you to rapidly migrate to the cloud without changes to your apps, policies, or tools. Once you migrate your VMware workloads to Google Cloud, you can accelerate digital transformation through seamless access to services such as BigQuery for real-time data analytics and cloud-native container-based architectures on Kubernetes.

With VMware Cloud Universal, you will be able to accelerate migrations of your workloads and applications to Google Cloud through purchase of Google Cloud VMware Engine from VMware and its partners, allowing you to flexibly purchase credits, and leverage existing spend and unused VMware Cloud Universal credits. The program will offer the following benefits:

• Financial flexibility by letting you redeem VMware Cloud Universal credits for Google Cloud VMware Engine
• Streamlined consumption by enabling you to burn down your Google Cloud commits while purchasing from VMware
• Use of existing VMware licensing investments through the VMware Cloud Universal program for Google Cloud VMware Engine

With Google Cloud VMware Engine, you can take advantage of Google Cloud’s highly performant, scalable infrastructure with fully redundant and dedicated 100 Gbps networking, providing 99.99% availability to meet the needs of the most demanding workloads at very low costs. By providing a consistent VMware environment natively in Google Cloud, you can quickly migrate your VMware workloads to Google Cloud without changes. Deep and unique networking integrations and capabilities such as multi-region and multi-VPC connectivity, further ease the migration of complex enterprise networking topologies to Google Cloud. With rapid provisioning of private clouds across 13 global regions, you can also take advantage of on-demand capacity to serve your infrastructure needs with a cloud-native VMware environment in Google Cloud.Once in the cloud, you can take advantage of other Google Cloud services such as BigQuery and Cloud Operations to gain data-driven insights and unify operations.

Getting started

With Google Cloud VMware Engine as part of VMware Cloud Universal, Google Cloud is a compelling cloud destination for your VMware workloads. You can learn more about how to get started with the service and get additional detail around use cases and pricing on our website.