Granica Launches Industry-first AI Efficiency Platform to Cut Cloud Data Costs up to 80% and Boost ROI from AI Initiatives
NEA and Bain Capital Ventures $45 Million-funded Startup Launches from Stealth to Help Petabyte-scale Amazon S3 and Google Cloud Storage Customers to Eliminate Redundant and Low-value Data and Increase Data Privacy
-Granica emerged from stealth today and introduced the industry’s first AI efficiency platform, bringing novel, fundamental research in data-centric AI to the commercial market as an enterprise solution. The company’s cloud-native solution operationalizes a next-generation approach to AI efficiency via data reduction and data privacy, enabling AI teams using Amazon S3 and Google Cloud Storage (GCS) to derive maximum value from their ever-growing volumes of training data. Granica makes it feasible for more AI data to be cost-effectively captured, stored and used to power enterprise AI implementations and thereby improve model performance and business outcomes. Consumed as an API, Granica physically reduces the size and cost of petabyte-scale AI training data in cloud object stores by up to 80% using novel compression and deduplication algorithms. Granica also preserves privacy of sensitive information in object data, enabling its safe use in AI and analytics while improving data security posture. At launch, Granica is focused on efficiency for data, with plans to drive efficiency across the end-to-end AI pipeline. For the first time, C-level executives in data intensive industries can drive significant profitability and innovation gains by pursuing AI efficiency, complementing their existing efficiency initiatives.
The company’s revolutionary outcome-based pricing model only charges users a small percentage of the savings generated per month—and only if savings are generated—so that deployment of the solution delivers upside only. Co-founded by CEO Rahul Ponnala and CTO Tarang Vaish, seasoned data experts and former engineers at Pure Storage and Cohesity, Granica’s total funding to date is $45 million from leading venture capital firms New Enterprise Associates (NEA), Bain Capital Ventures (BCV) and others, with participation from several industry luminaries including former Tesla CFO Deepak Ahuja; Eventbrite Chairman and co-founder Kevin Hartz; and Frederic Kerrest, Executive Vice Chairman and co-founder, Okta.
Pete Sonsini, investor at NEA, observed, “The sheer volume of data required to properly train AI models makes responsible and performant AI out of reach for many organizations. Granica democratizes access to AI while keeping data secure — to make AI more accessible, affordable and safe to use. Granica’s fusion of novel research and systems engineering places the company in a strong position to lead the new wave of data-centric AI.”
Enterprises are Struggling to Achieve Significant ROI on AI Due to Out-of-control Cloud Spend
McKinsey reports that AI adoption has more than doubled since 2017, however Boston Consulting Group determined that a mere 10% of organizations achieve significant financial return on AI.
AI is only as effective as the dataset it is trained on. To power AI models capable of rapidly generating new business insights and efficiencies, enterprises are obligated to store massive volumes of data in cloud object stores. However, for most companies it is too costly to do so at the desired scale, leading to significant volumes of important data being archived or deleted, constraining model performance and thus AI outcomes.
“Our mission is to enable enterprise AI teams to maximize the value of their data and keep much more, if not all, of their AI data ‘hot.’ This is the key to unlocking the transformative potential of artificial intelligence and machine learning,” said Rahul Ponnala, co-founder and CEO of Granica. “Data fuels the AI engines that are quickly becoming essential to modern commerce, science and everyday life. Just look at the sudden explosion in generative AI tools to get a sense of the future reach of this technology.”
Granica Fuses Novel Fundamental Research and Engineering to Close the AI Efficiency Gap for Data-forward Enterprises
The inefficiency in AI is driven largely by the rapid proliferation of training data with low information value. Such data often contains significant redundancy and sensitive information, including personally identifiable information (PII). This information inefficiency increases costs, risks and the time it takes for teams to move data through the AI pipeline.
“There is a huge efficiency gap for AI workloads, especially for training data in cloud object stores like Amazon S3 and Google Cloud Storage. Making data more efficient requires an entirely new layer in the AI stack consumed as an API and directly integrated with AI applications,” said Andrea Montanari, chief scientist at Granica. “At its core, what we’re doing at Granica is fusing fundamental data-centric AI research with large-scale systems engineering expertise to build a platform that drives AI information density and efficiency at cloud scale.”
“Our research team – led by our chief scientist Andrea Montanari, a pioneering expert in information theory and a professor at Stanford University – works in unison with our world class engineering team, pushing the boundaries of data-centric AI to deliver first-to-market solutions that make AI affordable, accessible and safe to use,” Ponnala remarked. “Building a technology company that solves hard problems requires empowering brilliant people as much as commercializing cutting edge research. We take pride in our people-centric approach to our organization.”
Granica Opens New Pathways for Enterprises to Pursue AI Innovation Leveraging the Cloud
Through a cloud service provider-native infrastructure, the Granica AI Efficiency Platform integrates inline with cloud applications, making data as well as downstream pipelines and models more efficient, more performant and privacy preserving. Granica Crunch, the company’s data reduction service, eliminates redundant and low-value data, cutting costs and speeding up downstream processes for hot AI data. Granica Screen, the company’s data privacy service, enables organizations to safely leverage sensitive data for AI and business use cases while improving their data security posture and reducing breach risk. As Granica continues on its mission to build the de facto standard for AI efficiency, data trust and collaboration, additional services and products will be brought to market to provide companies with end-to-end AI efficiency and new ways to maximize ROI on AI.
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Benefits of Granica’s AI Efficiency Services:
- Granica Crunch is the data reduction service for enterprise AI. It provides Byte-granular Data Reduction containing novel compression and deduplication algorithms which losslessly reduce the physical size of enterprise AI training data such as sensor, image and text files, reducing costs to store and transfer objects in Amazon S3 and GCS by up to 80%. Furthermore, it reduces write costs by up to 90% by intelligently batching write requests and optimizing other storage operations.
- Granica Screen is the data privacy service for enterprise AI. It provides Byte-precise Detection for both high recall and high precision identification and protection of sensitive data, including PII contained in structured, semistructured and unstructured text data. Granica Screen enables enterprises to improve their data security posture and prevent breaches, safely use their data for AI and analytics use cases, and simplify regulatory compliance. It is built to enable privacy-enhanced computing. Granica Screen is available via an Early Access Program.
Everyone Wins with Granica’s Radically New, Customer-oriented Business Model
Granica’s revolutionary business model is focused on delivered outcomes as opposed to mere consumption. The platform is free to deploy with no upfront costs. After deployment, Granica measures how Crunch reduces storage costs relative to the Amazon S3 and GCS baseline. The cloud costs incurred in the user’s environment is then covered by the generated baseline savings, resulting in a savings outcome for the user. Granica customers simply pay Granica a small portion of the savings outcome.
Granica’s pricing model is new to the AI industry and provides the ultimate customer-centric solution. Benefits include:
- Pay only for value received
- No financial risk to try or to expand usage
- No need to find or allocate budget
- No need for complicated total cost of ownership (TCO) modeling
- Easy to forecast future savings for reinvestment into AI data, people and tooling
Ponnala explains, “Instead of charging users based on consumption, we measure and charge for the outcomes Granica’s efficiency services deliver within the customer’s environment. Companies no longer have to run up against an ‘innovation wall’ due to sky-high cloud compute and cloud storage costs. The amount saved from using Granica puts dollars right back into organizations’ AI-based innovations—or directly to their bottom line. Simply put, Granica’s incentives to drive AI efficiency are aligned with our customers. The more value we can deliver from every byte of data, the more our customers improve their ROI on AI, and the more we earn. It’s a total win-win.”
