Aerospike raises $114M to fuel database innovation for GenAI

Enabling AI

As generative AI has gained interest, so have vector databases.

For enterprises to benefit from generative AI, they need models to understand their business. Large language models (LLMs) such as ChatGPT and Google Gemini are trained on public data. They do not, however, have access to an organization’s proprietary data and therefore cannot answer questions about that organization unless fine-tuned using its data.

Vector search and storage are one means of discovering data that can be used to fine-tune LLMs to understand an enterprise’s unique needs. They are also a means of discovering data that can be used to develop small, domain-specific models built from scratch.

Likewise, graph technology can be used to train AI models. Both graph and vector databases enable similarity searches based on neural networks. Similarity searches, meanwhile, are what enable organizations to discover the data needed to train language models.

As a result, one of the ways Aerospike plans to use its new funding is to accelerate development of databases that support AI workloads, according to Subbu Iyer, Aerospike’s CEO. That acceleration, meanwhile, includes increased research and development as well as talent acquisition.

“[We will be] accelerating our AI strategy with new talent and vision,” Iyer said. “Growth and innovation are not just about creating the best products. They also require hiring world-class talent.”

Adding new support for vector search and storage as well as improving support for graph technology are wise ways for Aerospike to earmark some of its new funding, according to Matt Aslett, an analyst at ISG’s Ventana Research.

Numerous other database are prioritizing similar capabilities, which feed the retrieval-augmented generation (RAG) pipelines used to train AI models and applications.

For example, Pinecone is a vector database specialist while TigerGraph and Neo4j are graph database specialists. In addition, among many others, tech giants AWS and Google are adding vector search and storage to their databases while independent vendors such as Couchbase, MongoDB and SingleStore have all added capabilities designed to help customers build generative AI applications.

However, given that the explosion of interest in developing AI workloads is recent, Aerospike is addressing a relatively new demand by investing more heavily in capabilities that enable AI.

“Support for vector search and RAG is nascent across the database industry,” Aslett said. “We [therefore] anticipate further investment from Aerospike to combine the benefits of its graph and vector capabilities to serve the development and deployment of the next-generation of high-performance AI-infused operational applications.”

Vote of confidence

While $114 million in new funding will enable Aerospike to invest more heavily in product development, particularly in developing database capabilities that will enable customers to build generative AI applications, it also signals that the vendor is providing valued capabilities.

For years, funding flowed freely into data management and analytics vendors.

For example, Databricks raised $1 billion in early 2021 while Confluent raised $828 million by going public in June of that year. In addition, Reltio, Sigma Computing and ThoughtSpot all raised $100 million in venture capital funding 2021.

But near the start of 2022, funding — particularly from venture capitalists — evaporated. Since then, investments have been hard to come by with only a select few vendors able to raise substantial amounts of money.

Databricks is one vendor that has still been able to attract significant attention from investors.

But otherwise, many months often pass between any major funding rounds. Before Aerospike’s $114 million, the last data management or analytics vendor to attract as much or more in funding was Denodo in September 2023, which secured $336 million in equity funding. Before that, it was SingleStore raising $146 million in 2022.

In addition, vendors Pyramid Analytics, Qlik and SAS have all expressed interest in going public but have held off on IPOs given the current unfavorable climate for IPOs.

For Aerospike to raise $114 million is therefore significant, according to Aslett. In particular, it demonstrates that Aerospike has been able to stand out with database capabilities that support high-performance, low-latency applications that help customers control spending.

“Raising venture capital has been somewhat difficult in recent years given the overall economic environment, but funding is still available … for analytics and data software providers that have an attractive, differentiated value proposition,” Aslett said.

Despite how difficult it has been for data management and analytics vendors to attract funding over the past two years, purse strings may finally be loosening.

On the same day Aerospike raised $114 million in private equity financing to foster further database development, data transformation specialist Coalesce.io raised $50 million in venture capital funding. In addition, hyperscale data warehouse vendor Ocient raised $49.4 million in venture capital funding on March 11.

If there’s a common thread among the vendors attracting financing, it’s AI, Catanzano noted.

Ocient’s hyperscale data warehouse can handle AI workloads. Coalesce’s data transformation capabilities enable customers to prepare data for AI. Aerospike’s graph and vector capabilities help users discover data to train AI.

“[The vendors attracting funding] are all adding AI enablement capabilities to chase that market of AI workloads,” Catanzano. “I think vendors look at the massive AI market projections and can easily show they can get a piece, but they need more money and are getting attention.”

More plans

Beyond adding capabilities that add and improve support for AI workloads, Iyer said Aerospike plans to use some of its new funding to expand go-to-market capabilities.

In addition, Coalesce’s roadmap includes continuing to improve the multimodal AI capabilities of its databases with performance and cost control both areas of focus, Iyer continued.

“Existing and future innovations will continue to help enterprises handle many complex data sets easily for even more accurate AI, and at a lower cost than traditional databases,” he said.

Aslett noted that adding new database capabilities such as support for vectors while also improving existing capabilities is appropriate for Aerospike. By doing so, it enables the vendor to broaden its potential customer base.

Aerospike started with real-time analysis as a focal point. Since its inception, it has added features that address SQL-based analytics, time-series forecasting, graph capabilities and vector search, among other capabilities.

“Aerospike is … continuing to expand its addressable market,” Aslett said.

Catanzano, meanwhile, said that Aerospike’s primary focus on adding more support for AI workloads is also appropriate. He noted that Aerospike likely isn’t yet attracting a significant number of AI workloads so the addition of support for vectors and improving graph capabilities could attract new users.

“This [funding] will keep them focused on AI for a while,” Catanzano said.

Eric Avidon is a senior news writer for TechTarget Editorial and a journalist with more than 25 years of experience. He covers analytics and data management.