Value of Establishing a Baseline for Metrics

As organizations continue to leverage cloud investments to drive their business growth and top line revenue, business, finance, and technology executives need to become increasingly connected in their efforts to deliver strong business outcomes.  More than ever before, executives need to quantify the value of their investments in business and technology capabilities.  As such, business and IT leaders need a set of value metrics that cover both operational and strategic outcomes, as well as risks and opportunities.  Nevertheless, operational IT metrics are often disconnected from business outcomes, and executives need to establish the connection between technology and business outcomes to facilitate a meaningful dialogue between IT and business leaders.

Like many aspects of IT operations, metrics and KPIs are commonly a journey.  Organizations typically start this journey with unit metrics focusing on cloud costs and eventually progress toward a set of clearly defined business value metrics.

As we define the set of metrics across the five key building blocks of Cloud FinOps, which include Accountability & Enablement, Measurement & Realization, Cost Optimization, Planning & Forecasting, and Tools & Accelerators, we ensure that these metrics are easily measurable and commonly attainable across the organizations that are on the journey of digital transformation. 

Accountability and Enablement Metric

The Accountability and Enablement pillar is foundational to building a culture of cost and value awareness and charts the course for both the process and cultural transformation journey in cloud FinOps.  The primary goal is to help drive financial accountability and accelerate business value realization by streamlining IT financial processes and enabling frictionless cloud governance.  Enablement empowers IT, finance, and business teams with training to better understand cloud resources and strategies to efficiently deploy and manage them.  Driving accountability and enablement starts with a charter and core governance policies, and then guides the transformation of processes that link finance, IT, and business owners. 

We recommend adopting Cloud Enablement % as the standard metric for the accountability and enablement pillar, measured by the # of business leaders trained and certified / total # of business leaders in the organization.

This is an important metric as many organizations fail to adopt Cloud FinOps because of lack of awareness and training. This cloud enablement metric will help business leaders better understand the value of cloud and how it can be an enabler to drive sustainable business outcomes. 

The cloud enablement metric can easily be implemented through a set goal based on the number of identified business leaders across the organization. With that said, it is important to utilize the Pareto principle of 80/20 rule here and identifying the key business leaders who are extensively consuming services on the cloud should be the primary focus. Google Cloud recently published a new Cloud Digital Leader certification that is aimed for business leaders and executives. By obtaining the Cloud Digital Leader certification, it ensures the individual is well-versed in basic cloud concepts and can demonstrate a broad application of cloud computing knowledge in a variety of applications and how Google Cloud services can help achieve desired business goals. In addition, the FinOps Foundation also provides training and certification to practitioners in a large variety of cloud, finance and technology roles to validate their FinOps knowledge and enhance their professional credibility.

Ultimately, we see that a target goal of over 70% of business leaders achieving the Cloud Digital Leader certification can significantly drive alignment and adoption of Cloud FinOps across the organization and leverage cloud technologies as an enabler to create sustainable business outcomes.

Measurement and Realization Metric 

Foundational to any good process is accurate data and effective metrics, which starts with the notion of cloud costs visibility and traceability. This is driven by proper resource hierarchy and project structure standards and supported by a labeling and tagging data architecture behind your organization’s use of cloud resources.  While many common tags include IT-driven designators such as application, environment, and project, it is important to design a direct connection to your P&L into your labeling and tagging architecture, by including cost centers or the chart of accounts as tags.  Furthermore, automation of tagging ensures that all taggable resources are deployed with consistent and accurate labels and feed FinOps metrics with reliable data.

Establishing consistent and detailed tagging is essential to attributing cloud resources not only to specific products and projects, but also to detailed cost centers aligned with lines of business and associated P&Ls.  In order to establish a full chargeback of typical cloud services, customers will need to attribute costs associated with 3 types of cloud resources.  The first and most straightforward will be attributing taggable resources (compute instances, databases, and storage buckets) that are aligned to a specific P&L, such as where a given application is solely consumed by one line of business.  

The second situation is where taggable resources are shared across multiple lines of business.  Many customers will resort to using traditional P&L allocation models, such as using business revenue or headcount of the associated business units to divy up the costs.  In order to more accurately allocate shared application costs, leading-edge customers use elements in their cloud microservices architecture, such as API calls, to specifically measure the relative consumption of shared applications.  

The third type of cloud resources are those that cannot be tagged.  Common examples include support, networking costs, and third party Marketplace costs.  Here, traditional P&L allocation models as described above (using headcount or revenue) are commonly used.  Some customers will use the relative distribution of their taggable resource allocations to appropriate non-taggable costs to their business units, while some types of costs, such as networking, are allocated based on API calls.

To measure the effectiveness of the Measurement & Realization pillar of cloud FinOps across these three types of cloud resources, we recommend adopting Cloud Allocation % as the lead metric.  This metric is measured as the percentage of total cloud costs (taggable resources consumed by individual business units, taggable resources shared across multiple business units, and non-taggable resources) allocated to responsible business owners.

This metric can be used to support both Showback (cloud costs held in a central IT P&L but reported to business units) and Chargeback models (cloud costs fully charged to business unit P&Ls), and reflects the underlying effectiveness and accuracy of resource tagging and cost attribution to business units.  Cloud Allocation % can be implemented in two ways.  The basic implementation would qualify costs apportioned by any P&L metric (either by consumption or by traditional P&L allocation such as by revenue or headcount).  The more advanced implementation of this metric would only qualify those resources (both specific and shared) that use either tagging or API calls to measure consumption and attribute associated costs to business units.

Customers evolving from a Crawl to a Walk stage of implementation will seek to allocate 70% or more of their total cloud costs, while those moving to a Run state will achieve 90% or greater cost attribution based on direct consumption measures.

Cost Optimization Metric

Cloud cost optimization is not just about cutting costs—it’s about knowing where to spend your money to maximize the business value. It is an iterative and continuous process that provides a consistent methodology to visualize and manage cloud consumption in a most cost effective way.  Success in cost optimization can result not only in significant reductions of cloud spend, but sometimes also in improved application performance to manage higher traffic (user requests per seconds or transaction processed) within the same cost envelope. 

It is important for an organization to automate reports generated by ingesting billing usage and cost data as well as recommendations generated for optimizations. These optimizations reflect the potential savings (also known as unrealized savings) which allows the team to prioritize implementations to realize the cost savings. 

Typically potential savings contains adoption of:

• Pricing optimizations like Committed Use Discounts (resource-based and spend-based), BigQuery reservations, etc.
• Resource optimizations of wasteful resources (including aged snapshots, idle instances, and over-sized databases) that don’t provide any business value.

Capturing this metric is important as it allows the organization to keep a pulse on inefficiencies that exist in the organization and allows businesses to focus on achieving cost savings thereby capturing true value of running their workloads in the cloud.

The cost optimization metric can be implemented by integrating Recommendation Hub in your FinOps workflows. Recommendations Hub is part of Active Assist that contains a portfolio of intelligent tools and capabilities to help you optimize your workloads with minimal effort. It surfaces a summary of all recommendations across your projects along with potential cost savings ($) so you can prioritize your cost optimization effort. We have seen customers realize savings by taking action on recommendations generated by idle VM recommender, Committed Use Discount recommender, VM machine type recommender and many more.

Ultimately we see customers achieving realized savings of over 90% on total cloud service optimizable. We have seen customers reinvest these savings into creating differentiated products and offerings and improving their customer experience, thus accelerating business value realization from the cloud.

Planning and Forecasting Metric

Financial planning is a foundational capability within finance organizations that will directly influence each company’s capabilities of cloud computing forecast accuracy. Financial planning focuses on accurately forecasting financial metrics that are set on an annual basis to guide the company’s financial objectives. The annual plans are measured on a quarterly basis and adjusted based on performance throughout the year; the forecast performance is monitored on a monthly basis to help influence operational results.

Planning and forecasting cloud computing costs is typically the responsibility of the team responsible for cloud operations. Operational forecast planning is based on consumption workload plans, historical trajectory, seasonality and leading indicators. Transformational projects also create material risks to forecast accuracy. 

Establishing accurate financial forecasting in the cloud spend requires rethinking traditional approaches to asset depreciation run-outs and trend-based forecasting of maintenance and licensing costs. Using workload-specific forecasting models that leverage a combination of trend-based models for steady-state workloads, driver-based models for scaling applications, as well as monthly variance analysis can greatly improve the accuracy of dynamic cloud needs.

Capturing and measuring forecast accuracy enables companies to understand if they do what they plan. Companies get what they measure and so by measuring and discussing variances to forecast accuracy it enables better control of cloud spend allocations. 

Cloud computing forecast accuracy should be included as a topic that Finance and Cloud operations teams discuss at least monthly. The cloud operations team should monitor forecast trajectory during the month and evaluate adjustments when they identify unexpected shifts.

An effective forecast accuracy is one that avoids surprises to company executives and investors. Cloud computing often has more variability and seasonality than depreciation of capex from on prem environments. Coordinating project and sprint agile management can help avoid surprises. If a development change creates an unexpected jump in spending then change management processes should be reviewed to avoid future surprises.

Tools and Accelerators Metric

Employing proper tools and accelerators are important to fully benefiting from FinOps practices. In earlier stages, companies may have limited their ability to report detailed analysis of cloud spend. As practices mature and improve, labeling and tagging of resources proves valuable to understanding costs for specific projects/teams and for building unit cost metrics. 

These capabilities can become even more powerful through automated monitoring of resources that offers insights on spend, value, compliance and recommendations.  

Therefore the recommended measure of Tools & Accelerators maturity is to evaluate the # of automated recommendations that have been implemented as a % of total list of automated recommendations generated that results in cost savings

This is an important metric because as the organization onboards newer workloads to the Cloud environment, lack of robust actionable recommendations and monitoring can lead to increased cloud waste. This has been a key component prohibiting organizations from realizing the total value of their cloud investment.

Customers starting on their tool maturity journey can leverage Google’s out of the box recommendations Hub to get started. The Recommendation Hub is a place in the Google Cloud Console where you can view, prioritize, and apply these recommendations.  Some examples include VM right sizing recommendations, BQ slot optimizations, Committed use Discount etc, Idle resource recommendations. This can further be integrated into any existing enterprise tooling using the recommendations API. As organizations mature, they can leverage Cloud Monitoring to create advanced recommendations based on custom business logic.

Ultimately, we see that a target goal of over 50% of automated recommendations implemented as the tooling for surfacing recommendations matures and this will ensure that the organization can minimize and eliminate cloud waste to maximize value from cloud investment. 

Bringing this together with a Cloud FinOps Dashboard

As technology and business goals continue to evolve over time, it is essential to establish a process where the Cloud FinOps metrics are continuously reviewed whenever the goals change. Furthermore, it is important to note that not all organizations need to achieve the “Run” state of the identified metrics target. The metrics are means to achieve the business outcomes based on the organization’s priorities. By collaborating with cross-functional teams to quantify and measure the impact of the Cloud FinOps metrics, executive leaders can quickly obtain buy-in, highlight common-shared goals, and move fast. 

At Google Cloud, we have developed solutions to help our customers build a Cloud FinOps Dashboard to capture these metrics to drive a culture of change and equip the transformation and business leaders with the tools to share and track the results of the key metrics. Successful adoption of the Cloud FinOps metrics enable organizations to focus on the business outcomes and the dashboard provides a meaningful feedback loop to report on the impact and drive visibility across the organization.

So, where are you now in your FinOps journey, and how do you move beyond the challenges ahead? Google can help you start the conversation and accelerate your path to maximizing business value with the cloud. 

No matter where you are on the cloud transformation journey, through an interactive session with Google, we can bring executives across the organization together to work toward a shared vision and a plan to accelerate and realize business value in the cloud. If you are interested in more information, please contact us.

^{Special thanks to Daniel Pettibone, Amitai Rottem, Bruce Warner, Jon Naseath, and Nihar Jhawar for co-authoring and contributing to this blog post and the members of the FinOps Foundation including J.R. Storment, Vas Markanastasakis, Anders Hagman, John McLoughlin, Mike Bradbury, and Rich Hoyer for providing their domain expertise and continuous support to this important cloud FinOps topic.}

Cloud offers a proven way to accelerate end-to-end chip design flows. In a previous blog, we demonstrated the inherent elasticity of the cloud, showcasing how front-end simulation workloads can scale with access to more compute resources. Another benefit of the cloud is access to a powerful, modern and global infrastructure. On-prem environments do a fantastic job of meeting sustained demand but Electronic Design Automation (EDA) tooling upgrades happen much more frequently (every six to nine months) than typical on-prem data center infrastructure upgrades (every three to five years). 

What this means is that your EDA tool can provide much better performance if given access to the right infrastructure. This is especially useful in certain phases of the design process.

Take for example, a physical verification workload. Physical verification is typically the last step in the chip design process. In simplified terms, the process consists of verifying design rule checks (or DRCs) against the process design kit (PDK) provided by the foundry. It ensures that the layout produced from the physical synthesis process is ready for handoff to a foundry (in-house or otherwise) for manufacturing. Physical verification workloads tend to require machines with large memories (1TB+) for advanced nodes. Having access to such compute resources enables more physical verification to run in parallel, increasing your confidence in the design that is being taped out (i.e., sent to manufacturing).

At the other end of the spectrum are functional verification workloads. Unlike the physical verification process described above, functional verification is normally performed in the early stages of design and typically requires machines with much less memory. Furthermore, functional verification (dynamic verification in particular) accounts for the most time (translating directly to the availability of compute) in the design cycle. Verifying faster, an ambition for most design teams, is often tied to availability of right-sized compute resources. 

The intermittent and varied infrastructure requirements for verification (both functional and physical) can be a problem for organizations with on-prem data centers. On-prem data centers are optimized for maximizing utilization—this does not directly address access to right-sized compute to deliver the best tool performance. Even if the IT and Computer Aided Design (CAD) departments choose to provision additional suitable hardware, the process of provisioning, acquiring and setting up new hardware on-prem typically takes months for even the most modern organizations. A “hybrid” flow that enables use of on-prem clusters most of the time, but provides seamless access to cloud resources as needed would be ideal.

Hybrid chip design in action

You can improve a typical verification workflow simply by utilizing a hybrid environment that provides instantaneous access to better compute. To illustrate, we chose a front-end simulation workflow, and designed an environment that replicates on-prem and cloud clusters. We also took a few more liberties to simplify the environment (described below). The simplified setup is provided in a GitHub repository for you to try out.

In any hybrid chip design flow, there are a few key considerations:

1. Connectivity between on-prem infrastructure and the cloud: Establishing connectivity to the cloud is one of the most foundational aspects of the flow. Over the years, this has also become a very well-understood field, and secure, high availability connectivity is a reality in most setups. 
   
   In our tutorial, we represent both on-prem and cloud clusters as two different networks in the cloud where all traffic is allowed to pass between these networks. While this is not a real-world network configuration, it is sufficient to demonstrate the basic connectivity model.
   
2. Connection to license server: Most chip design flows utilize tools from EDA vendors. Such tools are typically licensed, and you need a license server with valid licenses to operate the tool. License servers may remain on-prem in the hybrid flow, so long as latency to the license server is acceptable. You can also install license servers in the cloud on a Compute Engine VM (particularly sole-tenant nodes) for lower latency. Check with your EDA vendors to understand if you can rehost your license services in the cloud.
   
   In our tutorial, we use an open source tool (Icarus Verilog Simulator) and therefore, do not need a license server.
   
3. Identifying data sources and syncing data: There are three important aspects in running EDA jobs: the EDA tools themselves, the infrastructure where the tools run, and the data sources for the tool run. Tools don’t change much, and can be installed on cloud infrastructure. Data sources, on the other hand, are primarily created on-prem and updated regularly. These could be SystemVerilog files that describe the design, the testbenches or the layout files. It is important to sync data between on-prem and cloud to maintain parity. Furthermore, in production environments, it’s also important to maintain a high-performance syncing mechanism.
   
   In our tutorial, we create a file system hierarchy in the cloud that is similar to one you’d find on-prem. We transfer the latest input files before invoking the tool.
   
4. Workload scheduler configuration and job submission transparency: Most environments that leverage batch jobs use job schedulers to access a compute farm. An ideal environment finds the balance between cost and performance, and builds parameters in the system to enable predictive (and prescriptive) wrappers to job schedulers (see picture below).
   
   In our tutorial, we use the open-source SLURM job scheduler and an auto-scaling cluster. For simplicity, the tutorial does not include a job submission agent.

Other cloud-native batch processing environments such as Kubernetes can also provide further options for workload management.

Our on-prem network is called ‘onprem’ and the cloud cluster is called ‘burst’. Characteristics of the on-prem and burst clusters are specified below:

Once set up, we ran the OpenPiton regression for single and two-tile configurations. You can see the results below:

Regressions run on “burst” clusters were on average 30% faster than on “onprem”, delivering faster verification sign-off and physical verification turnaround times. You can find details about the commands we used in the repository. 

Hybrid solutions for faster time to market

Of course, on-prem data centers will continue to play a pivotal role in chip design. However, things have changed. Cloud-based, high performance compute has proved itself to be a viable and proven technology for extending on-prem data centers during the chip design process. Companies that successfully leverage hybrid chip design flows will be able to better address the fluctuating needs of their engineering teams. To learn more about silicon design on Google Cloud, read our whitepaper “Using Google Cloud to accelerate your chip design process”.

Even before the COVID-19 pandemic struck, the consumer packaged goods (CPG) industry was facing disruption. Consumers have come to expect personalized and seamless experiences at every point in their relationship with a brand. Additionally, consumers are expecting CPG brands to meet rising standards for sustainability, social responsibility, and transparency. Business models are shifting as well. Direct-to-consumer and subscription models have been gaining ground on traditional business models. Add in the CPG industry’s ever-present pressure for wider profit margins and the effects of the global pandemic, and you get a perfect storm of disruption. 

Leading CPG companies are responding to these changes by capitalizing on the potential of emerging technologies and leveraging the power of the cloud to create digital enterprises. In doing so, they can unlock value through reduced operational costs, faster innovation, improved marketing ROI, and greater transparency and sustainability—among other benefits. For businesses that run on SAP, accessing these benefits requires creating a digital enterprise with SAP at its heart. 

What CPG can expect from SAP on Google Cloud

SAP drives core business processes across most enterprise functions in CPG companies, and modernizing these operations is step one in unlocking next-level data and analytics capabilities. Creating a digital enterprise with SAP at the core requires establishing a digital foundation on a cloud platform capable of supporting and optimizing SAP workloads well into the future. From there, CPG companies can leverage the combination of SAP data and additional data signals to support high-value use cases utilizing the advanced analytics capabilities of the cloud

For CPG companies, running a successful digital enterprise in this climate depends on the power of the cloud because of the unmatched agility, security, scale, and flexibility offered by cloud technologies. More and more, consumer brands are turning to Google Cloud to host their applications—including core enterprise applications such as SAP—to drive business agility and maximize the value of data through smart analytics and machine learning. Google Cloud establishes a digital foundation for SAP customers by simplifying SAP deployments and offering  a suite of applications that integrates with and enhances SAP functionality. A Forrester study on the total economic value of Google Cloud for SAP customers found an average payback of less than six months and a total ROI of over 160%. By turning to Google Cloud to run their SAP systems, companies are able to: 

• Maximize insights 
  CPG enterprise data is often fragmented across disparate systems. Google’s analytics tools including BigQuery and Looker allow businesses to connect customer, operational and business data at scale by unifying data from SAP systems with other Google data signals such as Ads, Maps, Shopping or Google Marketing Platform. This precious data is fully democratized, allowing for complex queries to be completed rapidly so companies can uncover and analyze insights and create an end-to-end view of the consumer and the business.
• Create an intelligent organization
  Google’s AI and machine learning capabilities allow businesses to create built-in intelligence. Instead of reacting to trends, they can accurately predict them. For marketing teams, this could be the ability to evaluate promotions and effectiveness of marketing spend. For forecasting, product quantities and restock timing can be better planned. Supply chain optimization can include external data sources to closely monitor inventory and eliminate stock outs. 
• Future-proof your business
  Running SAP systems on Google Cloud creates an agile, secure and highly available environment that scales quickly as a business grows and as the CPG market evolves. A recent study conducted by IDC showed that SAP on Google Cloud deployments resulted in a 46% lower three-year cost of operations with 83% less frequent unplanned downtime and 56% more efficient IT teams. This frees IT resources to drive innovation and customer centricity. 
• Deliver on sustainability 
  Around the globe, consumers are becoming more and more demanding regarding sustainability. The impact of climate change and the abundance of plastic waste is only fueling this trend. Consumers are leaning into social signalling, and CPG companies are taking note. Sustainable IT is step #1, significantly advanced by  moving applications to Google Cloud, the cleanest cloud in the industry. We’ve neutralized all of our carbon emissions since our founding in 1998 and matched 100% of our electricity consumption with renewable energy purchases since 2017. Google Cloud allows SAP enterprises to further drive sustainability compliance and business objectives with AI and ML tools that can drive down waste and provide real-time decision making power to support proactive green initiatives. 

Rémy Cointreau is in high spirits after deploying SAP in the Google Cloud

Rémy Cointreau, a family-owned international maker of fine spirits, has products that can take up to one-hundred years to produce. But this long production cycle presents some unique challenges in today’s hyper-competitive premium beverage brands market. Since 1724, the company has been consumed with putting its customers first. In 2020, the company realized it was failing to capitalize on the benefits that the cloud can provide and began searching for a business partner that could help with this transformation.  

Rémy Cointreau made the move to Google Cloud for many reasons. First, the company could connect its SAP backbone to key SaaS applications like Salesforce. This enabled the creation of a 360-view of data among its ecommerce platform, SAP, and Salesforce to deliver sophisticated customer experiences that reflect the heart of the brand. The Rémy Cointreau team quickly realized they now had the ability to be more agile in their finance, manufacturing, and supply chain functions with easy access to valuable SAP system data that drives decision-making. Sebastien Huet, the company’s CTO, explains: “Now that we’re fully deployed on Google Cloud Platform, anything is possible. We can pull data in from multiple sources via integration and analyze it in a matter of days. We don’t need a three-month project to see value.”  

In today’s on-demand, omnichannel world, it’s not enough for CPG brands to understand their consumers. For companies like Rémy Cointreau, it is mission-critical that they anticipate consumer preferences and deliver personalized experiences. The winners will be the companies that can reduce time to insights by treating all their data as strategic assets, breaking down data silos to enable real-time business intelligence. With SAP on Google Cloud, CPGs are transforming consumer relationships and business outcomes.

Are you ready to change how your CPG brand operates? Check out this video and read the Google Cloud for SAP CPG customer white paper and ebook.  Learn more about how your peers are leveraging SAP on Google Cloud to evolve their businesses.

Small and medium-sized businesses and enterprises are the backbone of the US economy generating more than 44% of GDP¹. Yet these organizations are still underserved when it comes to online financial tools—and dealing with payments is no exception. Complaints include hidden fees, limited capabilities, long-term lease agreements, and poor customer service.

These are the issues that we wanted to solve when we launched Helcim in 2020 in Calgary (Alberta, Canada). We provide a payment service that offers low rates through our interchange plus pricing model, no monthly fee for the core payments offering, and numerous payment options, as well as simple, affordable hardware such as card readers. 

Our digital-first approach makes it easy for owners of small and medium-sized businesses to get started. Online sign-up means no paperwork and near instant access to Helcim’s software and all-in-one platform experience. Merchants can choose from a range of payment solutions from the Helcim app including in-person payments, SMS payment requests, online invoices with pay now buttons, and more. 

To achieve our goals, we built most of the business systems and processes in-house including our technology stack, financial partnerships, marketing, and everything in between. This is something that few startups would dare to do, but it enabled us to build a payments platform offering the rich capabilities and performance SMBs really need.

Taking control with the cloud

Before migrating our infrastructure to Google Cloud, it was hosted at two colocation data centers in Calgary. This model served us well, but as we grew, most of our hardware needed to be replaced to maintain service security and performance.

A successful round of Series A funding also impacted our trajectory. Giving us the fuel we needed to scale and innovate faster. When we considered the choice between making a large capital investment in our existing environment, or to transition to the cloud, the decision was clear: The cloud was the way to go.

We looked at other big names in cloud hosting and tested another platform. But Google Cloud is by far the best environment for us. It’s much easier for a lean technology team to manage, as we embark on our first cloud strategy. It also offers all the tools and advanced machine learning capabilities we need.

Google Cloud also comes with the backing of Alphabet, a business that in the past five years has spent more on research and development than any other organization in the S&P 500². The ability to engage the Google Workspace account team for guidance to improve our everyday processes was another bonus. 

A variety of investment and training programs from Google Cloud also influenced our decision. We participated in Google for Startups Accelerator Canada which gave us access to Google Cloud experts across all our technology domains. This helped accelerate our infrastructure migration while ensuring we optimized every service from day one. We were also eligible for $100,000 USD of Google Cloud credits, covering our Google Cloud costs which helped us get everything up and running cost-effectively. The partnership with the wider Google team has been extremely impactful as we stand everything up for our business.

But ultimately, it’s the sheer depth and breadth of the Google Cloud environment that makes the difference. Here are the Google Cloud tools we currently have at Helcim.

BigQuery

We chose BigQuery because it is easy to aggregate new data and apply the right access controls. It also delivers outstanding performance running our analytics workloads.

Vertex AI

Vertex AI makes the deployment of new models exponentially faster, and with Google Cloud, we can do most of the work through containers instead of proprietary tooling.

GKE

GKE has enabled us to migrate to a fully managed cloud service and avoid a lift-and-shift exercise that would have prevented us from getting the full benefits of a containerized infrastructure. Running a new GKE environment also enabled us to significantly reduce platform latency by 20%-50%. We can now deploy projects in hours instead of days. 

Cloud Storage

We updated our centralized file system to use Cloud Storage, which is faster and more scalable.

Cloud SQL

All of our software today is built on top of MySQL, so CloudSQL was the natural choice for cloud database management.

Cloud Run

Cloud Run is more flexible than other serverless tools and was an easy way to reduce the burden of managing some of our services.

Boosting performance across the business

By migrating to Google Cloud, we transformed our application performance. We were able to decouple the infrastructure between systems and use modern server hardware for our compute and database instances. With very little change to our code, we saw a 50%+ increase in the speed of our entire platform.

Giving developers more control of the technology running their systems via containers means that they can enhance systems through more frequent language updates and by deploying new technologies to optimize workloads.

We can more closely monitor our systems to diagnose issues and fix their root cause faster. Being able to quickly add resources gives us further options if we experience platform latency or increased traffic.

Maintaining momentum with machine learning

Leveraging Vertex AI has significantly reduced the time it takes the team to build and deploy new machine learning models. Greater agility in our data stack has also freed up time for exploratory work in the data team. For instance, by creating low fidelity proxy data for human behavior in the application process, we created a new model that will reduce the number of manually reviewed batches by more than 10%.

We’ve also been able to improve our deployment process and the time to rollback. As a result, breaking changes in production have been reduced from more than five minutes to less than 30 seconds.

Thanks to BigQuery, we can make better use of data to support key business decisions. Previously it was hard to aggregate data from different sources and while maintaining our strict requirements for customer data confidentiality. We also needed specialist SQL knowledge to consume it. By investing in a more modern data stack, the availability of trusted data across the organization has increased exponentially. 

We’ve also overcome the constraints imposed by static hardware environments especially when maintaining a high-availability configuration between two locations. With Google Cloud, we’re no longer constrained by such a rigid arrangement and the deployment velocity of new infrastructure tooling has been reduced from months to days.

Security is another area where Google Cloud excels. From hackers and fraudsters to bots and web attacks, it protects our users, applications, and data, while facilitating compliance with local and regional authorities. We can also integrate more easily with our security partners ensuring that we can empower our team to stay ahead of cyber criminals and other external threats.

Building the payments platform for the future, today

When we look to the future, Google Cloud opens the door to dozens of opportunities to widen our appeal to SMBs while remaining competitive. Its advanced infrastructure for cloud computing, data analytics and ML supports our roadmap to profitability and will help us attract future rounds of funding.

Above all it provides a foundation for growth. We grew 400% in 2021 and raised more capital in the spring of 2022 to grow even faster. In 2022 we were also listed as one of the top payments processors by industry publications such as Nerdwallet and Merchant Maverick. With Google Cloud, we can build on this success, continue to innovate, and help our SMB customers take their payments and e-commerce strategies to the next level. 

If you want to learn more about how Google Cloud can help your startup, visit our 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. 

To learn more about Google for Startups Accelerators and to apply to a program in your region, visit the website here. 

^{1. Small Businesses Generate 44 Percent Of U.S. Economic Activity
2. Alphabet: Big Value In Big Tech}