On August 1, VAST Data, self-styled data platform for the AI era, revealed how
it was bringing its vision to life with the launch of a ‘transformative’ data computing platform that it believes will accelerate AI’s ability to find solutions to humankind’s greatest challenges in healthcare, climate science, agriculture and other areas.
Designed for the deep learning era, the VAST Data Platform builds on and expands the company’s existing Disaggregated Shared-Everything (DASE) distributed systems architecture to enable companies that have built large-scale AI clusters to automate the discovery of new ideas and solutions by interacting with data, including text, images, sound, video and instrument data, in a more intelligent way.
Key advances include the creation of one global namespace, the VAST DataSpace, spanning public cloud, private cloud, edge and devices (and all geographies); the ability to run SQL queries on file and object metadata and cross-correlate between different data sources; and the ability automatically to run functions on unstructured data such as data preparation, data cleaning, data anonymisation, data inference and data training.
VAST expects these new capabilities to ease the transition from generative AI and Large Language Models (LLMs), which it says are limited to performing routine tasks or reciting information that is already known, to AI-assisted discovery when machines can recreate the process of discovery by capturing, synthesising and learning from data. You can find out more about its vision at https://buildbeyond.ai/special-event.
VAST also has applications for businesses with less ambitious goals as Technology Reseller discovered when we caught up with the company’s recently appointed Vice President of Channels Greg Machon.
Although Machon had been at VAST for less than three months when we spoke, he said he had been following the company since 2019 when it was in stealth mode and ready to announce itself to the world.
“I was fascinated with the idea that VAST was going to create one massive pool or volume where people could store their unstructured data, whether it was NFS (Network File System) or SMB (Server Message Block) or S3 (Amazon Simple Server Storage), and dramatically simplify things and drive down costs for companies that would normally store their data on a combination of flash and/or hard disk drives.
“I was fascinated by how you could create this highly performant platform on all-flash storage class memory with the economics (or better) of a high density disk environment or at least a hybrid environment. At the time, I said it sounded almost too good to be true.”
Four years later and Machon reels off a list of the benefits of VAST’s platform, including a massive acceleration in performance and a 25% saving in compute and power costs due to the use of all-flash storage-class memory, plus cost savings of 66% from new storage algorithms, including VAST’s own patented similarity-based data reduction technology.
“What’s interesting from a data reduction standpoint is that we are seeing environments that have already been deduplicated and compressed and on our platform we are still able to get anywhere from 1.5X to 4X data reduction savings. We take a lot of data and compress that to the smallest amount so a customer doesn’t need to pay for more software licensing or more hardware to be able to store that data.”
Machon adds that having a simplified, unified and higher performing platform enables companies to do interesting things with unstructured data using training and inference models and language learning models (LLMs). Hence VAST’s claim, even before its most recent announcement, to be the data platform for the AI era.
“No longer do customers have to have silos for their tape environment or their archive environment, for their low-cost hard disk drives or for their upper tier performance or flash media. They can put all that into one big volume and allow these models and algorithms and AI machines to look at and gain insights from that entire pool of data far more quickly.”
This message clearly resonates with customers, as VAST has been growing at a 2X to 3X CAGR and has been profitable for nearly 12 quarters in a row.
Not for everyone
Even so, Machon points out that VAST Data’s universal storage platform is not right for everybody.
“The customers we go after are typically storing and managing petabyte(s) of data in the unstructured data space. Often, those organisations and enterprises are doing something rather interesting with that data, creating a lot of insights and monetising it more and more. Our story around having a universal storage pool is very relevant to them because they want an easy way to be able to analyse all their data, not just some of their data, as quickly as possible.”
VAST’s target market includes high performance computing (HPC), AI and analytics environments, as well as enterprise backup and recovery, in key verticals such as life sciences, animation & VFX, media & broadcast, financial services (specifically quantitative trading) and the public sector.
“We’ve got vertical practices around those five areas, but that doesn’t mean we’re not relevant to people that are doing seismic studies for the oil and gas industry, for example. We have several customers in that space and more in the pipeline. In addition, use cases like enterprise backup and recovery and AI and machine learning can be in any vertical.”
Routes to market
In addressing these markets, VAST has a 100% channel approach, supported by its own sales and marketing activities and various technology alliances including an OEM partnership with HPE.
“We believe that as a manufacturer our responsibility is to create the market and then bring our partners in to wrap more intelligence and services around our offering and create new revenue streams for themselves,” explains Machon.
“We have a direct sales team and a marketing team that create demand for our product. Once we uncover opportunities, we pass them to our channel partners. More and more often our channel partners are creating that opportunity themselves. Today, about 35% of our deals are sourced by our channel partners.”
Other routes to market include Technology Alliance partnerships with vendors such as Nvidia, Vertica, Starburst, Veeam, Commvault and Veritas, including joint go to market initiatives as well as technology integrations; and partnerships with global Systems Integrators that
work with very large organisations around the world.
In April, VAST Data announced its first OEM agreement, with Hewlett Packard Enterprise (HPE) which has incorporated VAST software into the HPE GreenLake for File Storage service, giving HPE the ability to create opportunities, quote and sell through its partners or direct.
“That OEM model is already showing very promising signs both in deals that have been closed in pretty much every theatre where VAST operates and in a tonne of pipeline coming from the HPE enterprise and public sector teams,” says Machon.
One outcome of this relationship is the expansion of VAST’s addressable market to include organisations with smaller volumes of unstructured data.
“HPE has shown us that there is a market below where VAST has traditionally focused. It varies by region, but some sales teams typically would not be interested in anything below a petabyte; others took it down to 300 or 400 terabytes. HPE with its Alletra MP GreenLake for File offering has shown us there’s a market from 150 terabytes upwards, taking us into the lower end of the enterprise or the upper end of the commercial side, whereas in the past we stopped at 300, 400 or 500 terabytes.”
Margin opportunity
Machon points out that even without this greater market reach, VAST Data is an attractive proposition for channel partners.
“Our average deal size just for software is $1.2 million and there’s usually an equivalent amount in hardware, creating an average deal size of $2.5 million in terms of the revenue that VAST will see. Projects, on average, take less than six months, and what’s even more interesting is that our partners usually try us out for one use case and then come back and typically re-buy within another six months. On average that re-buy is usually two to three times the original deal size.”
Machon plans to build on this revenue and margin opportunity by helping partners develop services that complement VAST software and hardware.
“It’s important to note that we’re not a services company. We sell great software and we put it on a choice of hardware, whether it’s HPE’s or our own white box solution, but we’re not a services company. Instead, we want to encourage our partners to build out services offerings around these large projects, because at the end of the day, if somebody is looking to move 10 petabytes or 50 petabytes from multiple legacy storage platforms to us that’s not just a storage project, that is a digital transformation project.
“Also, in many cases, partners will be working with Nvidia and us to build
out training and inference and language models and those come with an incredible amount of services. We want to make sure our partners are able to participate in some of those opportunities.”
Machon adds that larger solutions providers, such as CDW, Softcat and Computercenter, have invested heavily in developing rich services offerings, and in any partnership with VAST will want to extend services to customers. This autumn, to help them do this, Machon is creating a Partner Advisory Board to advise on how VAST can broaden its services offering.
Another way in which VAST is planning to extend its reach is by working more closely with the channel partners of vendor partners like Nvidia, Commvault, Rubrik and Veritas that already have relationships with the organisations it wants to reach.
Ultimately Machon’s guiding principle when it comes to channel expansion is value not volume.
“We don’t want to saturate the market with a large number of partners. I’d rather do as much business as possible with as few partners as possible. If we can serve the London market, as an example, with five partners that help us address those five verticals plus that horizontal layer that we talked about, and they can do that well and profitably I’d be happy.”
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