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AEROSPIKE SET TO GROW REAL TIME DATABASE FOR AI WITH $109M RAISE

Sean Michael Kerner@TechJournalist
April 4, 2024 2:00 AM
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Credit: Aerospike

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As demand for AI continues to grow, so too is interest and demand in database
technologies that help to support AI efforts.

One such vendor is Aerospike which is seeing new demand for its real time NoSQL
database platform. To support that demand and help the company expand, the
company today announced that it has raised $109 million in a new round of
funding, as it seeks to add new AI capabilities and expand its go-to-market
efforts. Aerospike got its start back in 2009 with a focus on advertising
technology applications. In recent years, Aerospike has been steadily adding new
features and growing as a multi-model database, providing support for the JSON
document model in 2022 and in 2023 adding graph database capabilities.

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Live from GTC 2024 - Interview with Supermicro


Aerospike is increasingly being used to enable AI and machine learning (ML)
applications and includes Adobe, AppsFlyer, Barclays, Flipkart and  PayPal among
its many users. While Aerospike is being used to support AI/ML today, it does
not yet have vector capabilities, which is something the company is building out
and plans on releasing this quarter.

“We were founded on the premise that real-time access to data is going to pretty
much be prevalent in every industry,” Subbu Iyer, CEO of Aerospike, told
VentureBeat. “One of the things we have seen is the need to really get data
access and drive insight in real time.”


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VECTOR ISN’T THE ONLY THING THAT’S NEEDED FOR AN AI DATABASE

Over the last year, there has been an explosion in the number of database
providers supporting vector capabilities. 

Google recently announced that all of its cloud databases will have vector
support. Graph database vendor Neo4j, which has a competitive offering to
Aerospike, announced its vector support in August 2023. While vectors are
critical to enabling different generative AI use cases, they are not the only
way that organizations are using databases to enable AI.

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“A lot of the use cases that we do today are in the realm of predictive AI,”
Iyer said.

Iyer said that Aerospike currently supports AI and ML use cases without native
vector support. The use cases typically include predictive AI, which is a form
of AI for making predictions that predates the modern deployments of gen AI
types of models. With the predictive model, Iyer said that organizations are
doing offline model training and then storing the results in Aerospike. Moving
forward Iyer expects that Aerospike will be used to support a continuous
learning process.

Aerospike is currently building out full vector capabilities as an enterprise
preview with early access customers and will reach general availability this
quarter.


THE INTERSECTION OF GRAPH AND VECTOR IS A POWERFUL AI COMBINATION

The combination of Aerospike’s existing multi-modal database capabilities,
including graph database, alongside the in-development vector support, has some
interesting possibilities.

“We see a lot of synergy between vectors and graphs going forward,” Iyer said.

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Iyer noted that organizations are leveraging vector embeddings to help bring
more context and intelligence to AI. Some of the vectors can also be graphs,
which are used to better understand the relationship across different entities.

“Our graph solution right now is primarily a knowledge graph and we are selling
into identity and fraud,” Iyer said. “Folks are leveraging vectors for
recommendation engines, for fraud and so on, so there’s a natural overlap that
is coming up between the vectors that are being used to feed context and also
graph as a part of that entire value chain.”

Beyond improving support for AI use cases, Aerospike is also planning on adding
more core database capabilities to its platform in the coming months. Iyer said
that among the planned improvements are enhanced multi-record transactions,
point-in-time recovery as well as improved observability and management
functionality.

“As our database is actually getting deployed into larger cluster sizes and
large data sets we want to make sure that it’s easier to manage those clusters,”
Iyer said.

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