Personal blog of Yzmir Ramirez

Artificial intelligence technology holds a huge amount of promise for enterprises — as a tool to process and understand their data more efficiently; as a way to leapfrog into new kinds of services and products; and as a critical stepping stone into whatever the future might hold for their businesses. But the problem for many enterprises is that they are not tech businesses at their core, so bringing on and using AI will typically involve a lot of heavy lifting. Today, one of the startups building AI services is announcing a big round of funding to help bridge that gap.

SambaNova — a startup building AI hardware and integrated systems that run on it that only officially came out of three years in stealth last December — is announcing a huge round of funding today to take its business out into the world. The company has closed on $676 million in financing, a Series D that co-founder and CEO Rodrigo Liang has confirmed values the company at $5.1 billion.

The round is being led by SoftBank, which is making the investment via Vision Fund 2. Temasek and the government of Singapore Investment Corp. (GIC), both new investors, are also participating, along with previous backers BlackRock, Intel Capital, GV (formerly Google Ventures), Walden International and WRVI, among other unnamed investors. (Sidenote: BlackRock and Temasek separately kicked off an investment partnership yesterday, although it’s not clear if this falls into that remit.)

Co-founded by two Stanford professors, Kunle Olukotun and Chris Ré, and Liang, who had been an engineering executive at Oracle, SambaNova has been around since 2017 and has raised more than $1 billion to date — both to build out its AI-focused hardware, which it calls DataScale, and to build out the system that runs on it. (The “Samba” in the name is a reference to Liang’s Brazilian heritage, he said, but also the Latino music and dance that speaks of constant movement and shifting, not unlike the journey AI data regularly needs to take that makes it too complicated and too intensive to run on more traditional systems.)

SambaNova on one level competes for enterprise business against companies like Nvidia, Cerebras Systems and Graphcore — another startup in the space which earlier this year also raised a significant round. However, SambaNova has also taken a slightly different approach to the AI challenge.

In December, the startup launched Dataflow-as-a-Service as an on-demand, subscription-based way for enterprises to tap into SambaNova’s AI system, with the focus just on the applications that run on it, without needing to focus on maintaining those systems themselves. It’s the latter that SambaNova will be focusing on selling and delivering with this latest tranche of funding, Liang said.

SambaNova’s opportunity, Liang believes, lies in selling software-based AI systems to enterprises that are keen to adopt more AI into their business, but might lack the talent and other resources to do so if it requires running and maintaining large systems.

“The market right now has a lot of interest in AI. They are finding they have to transition to this way of competing, and it’s no longer acceptable not to be considering it,” said Liang in an interview.

The problem, he said, is that most AI companies “want to talk chips,” yet many would-be customers will lack the teams and appetite to essentially become technology companies to run those services. “Rather than you coming in and thinking about how to hire scientists and hire and then deploy an AI service, you can now subscribe, and bring in that technology overnight. We’re very proud that our technology is pushing the envelope on cases in the industry.”

To be clear, a company will still need data scientists, just not the same number, and specifically not the same number dedicating their time to maintaining systems, updating code and other more incremental work that comes managing an end-to-end process.

SambaNova has not disclosed many customers so far in the work that it has done — the two reference names it provided to me are both research labs, the Argonne National Laboratory and the Lawrence Livermore National Laboratory — but Liang noted some typical use cases.

One was in imaging, such as in the healthcare industry, where the company’s technology is being used to help train systems based on high-resolution imagery, along with other healthcare-related work. The coincidentally-named Corona supercomputer at the Livermore Lab (it was named after the 2014 lunar eclipse, not the dark cloud of a pandemic that we’re currently living through) is using SambaNova’s technology to help run calculations related to some COVID-19 therapeutic and antiviral compound research, Marshall Choy, the company’s VP of product, told me.

Another set of applications involves building systems around custom language models, for example in specific industries like finance, to process data quicker. And a third is in recommendation algorithms, something that appears in most digital services and frankly could always do to work a little better than it does today. I’m guessing that in the coming months it will release more information about where and who is using its technology.

Liang also would not comment on whether Google and Intel were specifically tapping SambaNova as a partner in their own AI services, but he didn’t rule out the prospect of partnering to go to market. Indeed, both have strong enterprise businesses that span well beyond technology companies, and so working with a third party that is helping to make even their own AI cores more accessible could be an interesting prospect, and SambaNova’s DataScale (and the Dataflow-as-a-Service system) both work using input from frameworks like PyTorch and TensorFlow, so there is a level of integration already there.

“We’re quite comfortable in collaborating with others in this space,” Liang said. “We think the market will be large and will start segmenting. The opportunity for us is in being able to take hold of some of the hardest problems in a much simpler way on their behalf. That is a very valuable proposition.”

The promise of creating a more accessible AI for businesses is one that has eluded quite a few companies to date, so the prospect of finally cracking that nut is one that appeals to investors.

“SambaNova has created a leading systems architecture that is flexible, efficient and scalable. This provides a holistic software and hardware solution for customers and alleviates the additional complexity driven by single technology component solutions,” said Deep Nishar, senior managing partner at SoftBank Investment Advisers, in a statement. “We are excited to partner with Rodrigo and the SambaNova team to support their mission of bringing advanced AI solutions to organizations globally.”

Google open-sourced its TensorFlow machine learning framework back in 2015 and it quickly became one of the most popular platforms of its kind. Enterprises that wanted to use it, however, had to either work with third parties or do it themselves. To help these companies — and capture some of this lucrative market itself — Google is launching TensorFlow Enterprise, which includes hands-on, enterprise-grade support and optimized managed services on Google Cloud.

One of the most important features of TensorFlow Enterprise is that it will offer long-term support. For some versions of the framework, Google will offer patches for up to three years. For what looks to be an additional fee, Google will also offer to companies that are building AI models engineering assistance from its Google Cloud and TensorFlow teams.

All of this, of course, is deeply integrated with Google’s own cloud services. “Because Google created and open-sourced TensorFlow, Google Cloud is uniquely positioned to offer support and insights directly from the TensorFlow team itself,” the company writes in today’s announcement. “Combined with our deep expertise in AI and machine learning, this makes TensorFlow Enterprise the best way to run TensorFlow.”

Google also includes Deep Learning VMs and Deep Learning Containers to make getting started with TensorFlow easier, and the company has optimized the enterprise version for Nvidia GPUs and Google’s own Cloud TPUs.

Today’s launch is yet another example of Google Cloud’s focus on enterprises, a move the company accelerated when it hired Thomas Kurian to run the Cloud businesses. After years of mostly ignoring the enterprise, the company is now clearly looking at what enterprises are struggling with and how it can adapt its products for them.

Peltarion, a Swedish startup founded by former execs from companies like Spotify, Skype, King, TrueCaller and Google, today announced that it has raised a $20 million Series A funding round led by Euclidean Capital, the family office for hedge fund billionaire James Simons. Previous investors FAM and EQT Ventures also participated, and this round brings the company’s total funding to $35 million.

There is obviously no dearth of AI platforms these days. Peltarion focus on what it calls “operational AI.” The service offers an end-to-end platform that lets you do everything from pre-processing your data to building models and putting them into production. All of this runs in the cloud and developers get access to a graphical user interface for building and testing their models. All of this, the company stresses, ensures that Peltarion’s users don’t have to deal with any of the low-level hardware or software and can instead focus on building their models.

“The speed at which AI systems can be built and deployed on the operational platform is orders of magnitude faster compared to the industry standard tools such as TensorFlow and require far fewer people and decreases the level of technical expertise needed,” Luka Crnkovic-Friis, of Peltarion’s CEO and co-founder, tells me. “All this results in more organizations being able to operationalize AI and focusing on solving problems and creating change.”

In a world where businesses have a plethora of choices, though, why use Peltarion over more established players? “Almost all of our clients are worried about lock-in to any single cloud provider,” Crnkovic-Friis said. “They tend to be fine using storage and compute as they are relatively similar across all the providers and moving to another cloud provider is possible. Equally, they are very wary of the higher-level services that AWS, GCP, Azure, and others provide as it means a complete lock-in.”

Peltarion, of course, argues that its platform doesn’t lock in its users and that other platforms take far more AI expertise to produce commercially viable AI services. The company rightly notes that, outside of the tech giants, most companies still struggle with how to use AI at scale. “They are stuck on the starting blocks, held back by two primary barriers to progress: immature patchwork technology and skills shortage,” said Crnkovic-Friis.

The company will use the new funding to expand its development team and its teams working with its community and partners. It’ll also use the new funding for growth initiatives in the U.S. and other markets.

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