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.}

After a seamless migration to Google Cloud Platform with CloudCover and Google Cloud Professional Services, Hike has reduced its costs by 20% and processed analytics queries 20 times faster than with its previous cloud provider. The business is also using AI and machine learning to enhance the experience provided by a new sticker-based messaging app, Hike Sticker Chat.

India is a market of opportunity for businesses that provide messaging apps to consumers. With more than 1.3 billion people, the country is the second most populous in the world. However, global messaging app providers face a robust market challenge from Hike, a home-grown internet and technology startup. Launched in 2012, Hike provides innovative products such as Hike Messenger and more recently the AI- and machine-learning-enabled Hike Sticker Chat, a service that enables young people in the country to express themselves through digital stickers.

The business says it understands the people of India and communication like no one else, while its mission is to reduce individuals’ dependency on the keyboard. To do this, Hike is building one of the largest repositories of AI and machine-learning-enabled stickers for Hike Sticker Chat. This messaging platform is, according to Hike, the only product of its type that enables conversations through stickers covering more than 40 languages and local dialects.

Google Cloud Results

• Processes analytics queries 20X faster than previously
• Doubles compute throughput
• Uses Google Cloud Machine Learning Engine managed, distributed capabilities to train complex models on TensorFlow that provide delightful local sticker recommendations through Hike Sticker Chat

Founded by Kavin Bharti Mittal, the Delhi-based venture is backed by SoftBank, Tencent, Tiger Global, Foxconn, and Bharti. To date, Hike has raised $261 million in funding. In August 2016, Hike raised its Series D round of funding, led by Tencent and Foxconn, at a valuation of $1.4 billion. The business is one of the fastest Indian startups to achieve Unicorn status, doing so in less than four years.

Hike started operations on a multinational cloud service. However, as user numbers and usage grew, the business began exploring options to improve performance and stability, reduce costs, and cut administration loads. In particular, Hike wanted to reduce latency between cloud data centers.

Focus on product development

“We aimed to move away from a technology stack with single points of failure to a horizontally scaled, highly reliable, distributed systems and managed services environment that enabled us to focus on product development rather than operations,” says Aditya Gupta, Director, Engineering, Hike.

Hike then began exploring the opportunities presented by Google Cloud Platform. The business held a number of executive-level meetings with Google to understand the capabilities, roadmap, and track record of the cloud service. It then decided to proceed with a proof of concept with Google Cloud Premier Partner CloudCover.

The proof of concept revealed that when Cloud Load Balancing was operating, latency between the Google Cloud data center in Taiwan and Delhi, India, was less than the latency between the incumbent cloud provider’s data center and Delhi. Further, compute throughput was up to two times greater on Compute Engine than on the equivalent service, while Hike could complete more then 1 million connections on Compute Engine – up from 500,000 connections on the incumbent service.

Migrate to GCP

The success of the exercise prompted Hike to migrate its messaging app to Google Cloud Platform. “We chose Google Cloud Platform because of its very broad set of services and features,” explains Gupta. “In addition, Google’s innovation mindset and the richness of the partnership would allow us to be onboarded quickly to machine learning services such as Cloud Machine Learning Engine.”

The business called on Google Cloud Professional Services (Technical Account Management) to help ensure a seamless lift-and-shift migration over two months. Google Cloud Professional Services initially undertook a technical infrastructure kickoff to establish a foundation for architecture requirements such as identity and access management and security.

Google Cloud Professional Services team delivers smooth migration

Google Cloud Professional Services worked closely with Hike to map out and deliver the Google Cloud Platform architecture that would deliver the greatest value to the business. The Professional Services team also worked with Hike to resolve product and support queries quickly; provided project background for product and support teams; and organized project meetings and early adopter program access.

In addition, Professional Services team members worked on site at least once a week, coordinated external support during critical migration periods, and coordinated teams in five countries for a single, 17-hour migration marathon. Over 60 days, the business migrated 7,000 processor cores, running virtual machine instances used for messaging infrastructure and analytics, to Google Cloud Platform.

Throughout the exercise, Google Cloud Professional Services worked with CloudCover to educate the customers’ technology teams to achieve proficiency with Google Cloud Platform. The teams soon built up skills and knowledge of best practices and began applying them to the Google Cloud Platform environment.

The Hike Google Cloud Platform architecture comprises virtual machine instances running in Compute Engine; Cloud Storage for unified object storage; networking; a BigQuery analytics data warehouse; Cloud Dataflow to transform and enrich data; Cloud Load Balancing to distribute workloads to maximize efficiency; and Cloud Dataproc to run Hadoop clusters.

Hike is also stepping up its AI & machine learning capabilities. It uses Google Cloud Machine Learning Engine managed, distributed computing capabilities to train complex models on TensorFlow. This powers key use cases such as delightful local sticker recommendations on Hike Sticker Chat. Hike is also investing heavily on AI and machine learning research.

Hike has achieved a range of benefits from its Google Cloud Platform deployment. As well as reduced latency, improved compute throughput, and increased connection handling, Google Cloud Platform managed services have enabled the business to reduce the time and effort required to administer core infrastructure, with the saved resources allocated to improving its messaging product.

“Managed services are beginning to reduce our operational overheads,” says Gupta. “For example, managed instance groups and Cloud Load Balancing are reducing our instance count and costs, thereby reducing involvement from DevOps and developer teams.”

Google Cloud Platform 20% cheaper

Gupta and his team have calculated that running for three years on Google Cloud Platform will cost, including the cost of migration, 20 percent less than on its previous platform. BigQuery is processing queries 20 times faster than a similar service offered by the previous provider, while storing 125TB of data and streaming 1.5TB of data daily. Furthermore, Hike’s analytics pipeline costs 80 percent less than in its previous environment.

“Google Cloud Platform has played an important role in enabling us to continue to innovate and realize our mission of reducing dependency on the keyboard,” says Gupta.

AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”