SurrealDB raises $23M, launches update to fuel agentic AI
Though funding isn't flowing as freely into data management as it once was, the startup is attracting interest by focusing on enabling customers to unify data for AI development.
The startup multimodel database vendor secured $23 million in venture capital funding, an extension of its Series A round that nearly doubles its total funding to $44 million. In addition, SurrealDB launched version 3.0 of its platform.
The $23 million brings SurrealDB's Series A round to $38 million, with Chalfen Ventures and Begin Capital joining previous investors FirstMark and Georgian in their investment in the database specialist. As part of the deal, Mike Chalfen, founder of Chalfen Ventures, joins SurrealDB as a director
Meanwhile, SurrealDB 3.0 includes new features such as a new control layer and improved vector storage and indexing capabilities, and is designed to help customers unify multiple data models -- relational, document, graph, time-series, vector, geospatial and key-value -- to fuel AI development.
Both the new funding and platform update are significant for SurrealDB users, with one providing the cash that will enable SurrealDB to grow its multimodel capabilities and the other demonstrating those capabilities, according to Kevin Petrie, an analyst at BARC U.S.
"This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI," he said. "They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions. This level of funding can help SurrealDB deepen its product capabilities and get more serious go-to-market activities."
Based in London, SurrealDB provides a platform that supports various data types so that users can integrate data to inform decisions based on more than just one type of data. Other vendors providing multimodel capabilities include Couchbase, Redis and the open-source PostgreSQL platform, while hyperscale cloud providers AWS, Google Cloud and Microsoft also offer multimodel databases.
Cash infusion
SurrealDB's latest funding comes at a time when venture capital investments in data management vendors are few and far between.
Throughout the 2010s and into the early 2020s, funding poured into the data and analytics market. In 2021 alone, vendors such as Aiven, Confluent, Databricks, Reltio, SnapLogic, ThoughtSpot and TigerGraph raised $100 million or more with Databricks' funding round reaching $1 billion. In early 2022, Aiven raised another $210 million and Sigma secured $300 million.
But then tech stock prices plummeted in mid-2022, and the funding for data and analytics vendors evaporated.
Since then, while vendors such as Aerospike and Sigma have attracted investments, few data and analytics vendors have raised capital. The common theme among the data and analytics vendors that continue to attract funding is their enablement of AI development.
SurrealDB aims to be a layer in the AI development process. It's that focus on enabling customers to develop agents and other AI applications that helped the vendor raise funding, according to Tobie Morgan Hitchcock, SurrealDB's co-founder and CEO.
"Raising now signals we're not just another database vendor, but increasingly an enabling layer for enterprise AI workflows," he said. "SurrealDB is being used as part of enterprise AI deployments, including agentic workflows that depend on fast, consistent data access. … Investors are leaning into infrastructure that makes AI systems production-grade, which is exactly what we are building."
Matt Aslett, an analyst at ISG Software Research, similarly noted that SurrealDB's ability to attract venture capital funding reflects its focus on helping enterprises build AI applications.
"While many VCs are chasing potential returns from investment in AI specialists, there is always investor interest in startups with the potential to make an impact in the lucrative database market, especially providers that are responding to the need for innovation to support AI initiatives," he said.
SurrealDB plans to use the added $23 million to accelerate product engineering and improve go-to-market efforts, according to Hitchcock.
"It lets us scale the team and the platform in parallel, shipping more capability, hardening reliability and security, and supporting larger deployments," he said. "In short, it accelerates our path from rapid adoption to durable, global scale."
Given that SurrealDB, which was founded in 2021, is a relatively new database vendor compared to peers such as ArangoDB and Redis, improving its platform and increasing its profile are wise areas of focus, according to Aslett.
"SurrealDB is in the early stages, and its new funding round will help the company accelerate the development of both its core database and its platform capabilities, as well as expanding investment in support and services resources as well as raising its profile in a crowded market," he said.
Platform update
While the new funding will be used, in part, to fuel future product development, SurrealDB 3.0 represents the vendor's current product development.
This funding announcement reflects the compelling pain that so many enterprises feel as they adopt AI. They struggle to integrate disparate data sources to provide agents with the business context they need to make trustworthy decisions and actions.
Kevin PetrieAnalyst, BARC U.S.
SurrealDB is designed to provide agents and other applications with unified data -- disparate data types integrated as one -- to give them proper context and memory so that they remember facts even as data volume and complexity increase. To provide that proper context and memory, SurrealDB positions context graphs in its database next to the data.
With that focus on enabling customers to build intelligence applications that feature contextual awareness and the memory to recall and learn from previous interactions, SurrealDB, though a startup competing against more established vendors for market share, has an opportunity to play the role of disruptor, according to Aslett.
"The evolving requirements for operational databases are to support the development of intelligent applications infused with contextually relevant recommendations, predictions and forecasting driven by machine learning, generative AI and agentic AI," he said. "These evolving requirements are providing opportunities for emerging database providers to disrupt established incumbents."
ISG predicts that within the next two years, around three-quarters of all enterprises will have adopted operational databases specifically designed to support the AI inferencing capabilities that intelligent applications require, Aslett added.
SurrealDB 3.0 builds on previous platform capabilities by adding the following:
Surrealism, a layer that enables developers and administrators to customize business logic -- the rules, processes and operations that determine how an enterprise uses SurrealDB -- including access controls and version controls.
Improved vector search and indexing to enable the discovery and use of unstructured data such as text and images.
Architectural changes that add stability and improve SurrealDB's performance such as separating data values from data expressions.
An improved developer experience, including a refined model that enables users to define custom API endpoints directly within the database, among other functions.
A combination of customer feedback and market observations provided SurrealDB with the impetus for developing the individual features that comprise its update, according to Hitchcock.
"The focus is on removing friction -- making the platform easier to adopt, operate, and scale -- while expanding the capabilities teams need in production," he said. "The goal is to keep the developer experience simple as use cases become more demanding."
Competitive standing
Looking ahead, SurrealDB is focused on three main initiatives, according to Hitchcock: maturing its platform to meet the needs of enterprises at scale, expanding its capabilities so customers don't have to integrate it with as many other tools to build AI applications and continuing to enhance the developer experience.
"As more customers deploy AI in production, we're investing in capabilities that make it easier to deploy and scale AI-powered applications and agentic workflows," Hitchcock said. "Our focus is broad. … It's all about simplification."
Despite still prioritizing some foundational capabilities such enabling enterprise-scale workloads and adding features that allow users to simplify their AI development stacks, SurrealDB is establishing itself as a viable alternative to other database vendors, according to Aslett.
All database providers are similarly adding capabilities that foster AI development, such as vector storage and indexing. But Aslett noted that SurrealDB's support for various data types and the quality if its vector storage and indexing capabilities stand out.
"SurrealDB is ahead of many established providers in terms of delivering differentiating capabilities, including enhanced vector search and indexing as well as native agent memory and context graphs, … an intuitive visual user interface and support for relational, document, graph, time-series, vector, search, geospatial and key-value data types in a single database."
Petrie similarly noted that SurrealDB is staking out a place for itself in a competitive market with the variety of its multimodel capabilities.
"I'm impressed with the range of data types that SurrealDB already supports as a Series A startup," he said. "This really simplifies your agentic AI architecture. The more you can consolidate diverse data and models onto a single platform, with real-time performance and memory, the better you can streamline your projects and reduce time to production."
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
