On Vector Capital acquisition of SingleStore. Q&A with Stephen Goodman
Q1. Vector Capital has described the SingleStore acquisition as your largest new platform investment in over 15 years. Given your extensive experience with database and data management investments including MarkLogic, Riverbed, and others, what specific market dynamics and technical capabilities convinced Vector that SingleStore represents this level of strategic opportunity in the enterprise AI data platform space?
That’s right—SingleStore is Vector’s largest new platform investment since 2007, as measured by enterprise value. In order to get comfortable making such a huge bet, we needed an extremely high degree of conviction that the investment will be successful. That conviction came from a few areas: one, our extensive prior experience in the database sector as you mentioned, in particular via MarkLogic which was a very successful investment for us; two, from a bullish and thoroughly researched perspective on SingleStore’s market opportunity, product capabilities, and competitive positioning; and three, confidence in CEO Raj Verma and his team who we are thrilled to partner with in this investment.
Our framework for evaluating database investments involves first identifying any unique technical capabilities that can’t be matched in another database or multi-vendor architecture, second identifying the customer segments who truly require those capabilities in their workloads, and third optimizing the go-to-market strategy to “land and expand” those customers efficiently. With respect to product capabilities, SingleStore is the only database in the world that offers the combination of real-time performance (specifically, millisecond latency between data ingestion and query response), for complex queries, at petabyte scale, with support for high user concurrency and ACID transactions. Which workloads truly require all of that? Any workload that needs to make a high volume of decisions or actions based on complex analysis of real-time information. Historically, that has meant applications like fraud detection, dynamic pricing, offer personalization, operational telemetry, logistics optimization, and operational dashboarding. Looking forward, we think an enormous number of enterprise AI applications will have these requirements and benefit from low latency analytics and transactions within one database. SingleStore is unquestionably the best-fit database for those apps, and we see that as an enormous opportunity.
Q2. With SingleStore positioning itself as ‘the leading data platform for enterprise AI,’ how do you see the database market fundamentally shifting as AI workloads become mainstream? What unique advantages does SingleStore’s real-time, AI-ready architecture provide compared to traditional data warehousing solutions, and how does this align with Vector’s investment thesis?
Generically speaking, AI is a rising tide that lifts many boats in the database market. Database companies’ revenue growth correlates with application development and data volume under management, so as customers build more AI applications and create more data, that drives more demand for databases.
However, which databases customers select to build those apps and store that data depends entirely on matching the needs of the workload with the capabilities of the database. Traditional data warehouses are great for classic business intelligence use cases, running reports on massive historical datasets. With respect to AI, these systems are also useful for the more batch process-style workloads, providing historical data for model training and feature engineering. But data warehouses are not suitable for building true applications. They aren’t transactional, and even with respect to analytics, they are slow—especially in scenarios with high and variable user concurrency, which is important in agentic or customer-facing software.
This is where SingleStore shines. To be precise, SingleStore is the leading data platform for enterprises to build real-time AI applications that are both analytical and operational. It’s less for the in-house data scientist to train AI models and more for the developer to build AI software. As to why SingleStore is uniquely positioned for this, it’s for a lot of technical reasons. The core value proposition of high performance analytics and transactions within one database comes from SingleStore’s 3-tier architecture, with an in-memory row store enabling real-time ingest and transactions, column store for high performance analytics, and object store for cost effective long-term retention. Then there are some additional capabilities that are beneficial for AI use cases, such as support for vectors, which SingleStore has had since 2017, and bi-directional integration with Apache Iceberg which launched last year.
Q3. The announcement mentions plans to expand the Company’s global reach’ under Vector’s ownership. Can you elaborate on the specific geographic markets and customer segments you’re prioritizing? How will Vector’s operational expertise and portfolio synergies accelerate SingleStore’s international growth beyond its current footprint?
SingleStore today has customers all around the world, so the comment was less about needing to expand in any specific geographies, and more about wanting to expand the company’s reach in general. Our investment in SingleStore is a growth investment, and our aspiration is to grow the business even faster than it is growing today. Some of that will come from focus on customer segments where SingleStore has already seen a lot of traction, such as technology, financial services, healthcare, retail, telecom, and industrials. Though we do see opportunity for SingleStore to enter some segments where it hasn’t focused historically, the public sector being one example.
As for Vector’s role, we are a very operationally-focused private equity firm, especially with respect to go-to-market. Vector likes to invest in companies that already have market-leading technology, but where we think go-to-market transformation can produce faster and more capital efficient growth. Vector’s Value Creation Team, comprised of former executives, operators, and management consultants, partners with company management to drive these transformations. Fortunately, we have a long track record of successful outcomes, including at MarkLogic where we significantly accelerated growth and efficiency in a relatively short period of time. Much of our value creation playbook from MarkLogic should be transferable to SingleStore. We have spent a lot of time with Raj and CRO Andy Wild on these topics and are extremely aligned in terms of how we envision the go-to-market strategy evolving.
Q4. The AI data platform space includes established players like Snowflake, Databricks, and emerging competitors. How do you assess SingleStore’s competitive differentiation, particularly in terms of real-time processing capabilities and vector database functionality? What strategic moves will be critical for maintaining and expanding market position?
It’s interesting that both Databricks and Snowflake—obviously, OLAP vendors—acquired OLTP databases this year. We think this is a testament to two important facts about enterprise AI, both of which are beneficial to SingleStore. First, that OLTP capabilities are essential to compete for agentic apps. Second, that there is real value in bringing OLAP and OLTP capabilities closer together. That second idea is not new, but it has gone in and out of fashion in the database world over time. We see agentic AI as a new factor which definitively tilts scales in favor of unified OLAP and OLTP systems adding real value above and beyond hybrid architectures.
This is where SingleStore is uniquely differentiated. Databricks and Snowflake acquiring OLTP databases does not automatically make them true unified systems. For any hybrid transactional and analytical workload, there will still be data integration or ETL happening under the hood. This introduces latency, creates multiple copies of data, and is just an inferior architecture for the types of AI workloads we discussed earlier: those that require real-time performance, for complex queries, at petabyte scale, with support for high user concurrency and ACID transactions. We think the most mission critical AI applications of the future will have these requirements, and there is no database other than SingleStore that can deliver them within one platform.
You also asked about “real-time,” which is the most important performance dimension to expand on. “Real-time” is one of those buzzwords like “AI” where everyone talks about it, but not many are specific about what it means. For SingleStore, we define it as the lowest latency for complex queries at scale, under high concurrency, with transactional integrity. Mission critical agentic applications will need all of this, and every other database that markets itself as real-time will fall down on at least one of those dimensions, if not more. ClickHouse, for example, struggles with complex multi-table queries, and the way data is moved in batches from buffer tables to the column store introduces latency between ingestion and query availability. But ClickHouse is great for simple, single-table queries at massive scale, where latency between data creation and query availability is not as important. Every other real-time database we studied in the market has a different answer for what it’s good at and where it falls down. SingleStore is the best at minimizing latency for complex queries at scale, under high concurrency, with transactional integrity. The vast majority of SingleStore customers we spoke with during diligence had chosen SingleStore for this reason, at least in part.
With respect to vectors, pretty much every database in the market has added support for vectors over the last few years. SingleStore has supported vectors since 2017, but at this point the capability is table stakes. Whether or not an application is a good fit for SingleStore has less to do with whether or not it needs vectors—we can do that, but so can most others—and more to do with whether it needs real-time analytics and transactions at scale.
Q5. Looking ahead to the SingleStore NOW Conference in October 2025 and beyond, what are your key priorities for the partnership between Vector Capital and CEO Raj Verma’s team? How do you envision SingleStore’s evolution over the next 3-5 years, both in terms of product innovation and market leadership in the enterprise AI infrastructure landscape?
We think the company has done an incredible job with respect to product innovation and technology development overall. While I would love to say that Vector is coming in with all these great product ideas, the truth is that we want SingleStore to keep doing what it’s doing with respect to its product development and innovation roadmap. In particular, we’re excited about Aura Analyst and AI & ML Functions, which are getting announced at SingleStore Now, and some key roadmap items in Q4 such as Embedded Aura Analyst, AI guided query tuning, and a new SSD based shared caching layer for even better price/performance.
Where we see the most incremental value in Vector’s partnership with SingleStore, and what we consider to be Vector’s highest near-term priorities, are the go-to-market transformation initiatives described previously. The database market is crowded and lots of vendors are shouting about AI without a clear and specific message of what they actually do. This makes it hard for developers to know which database is truly best-fit for their application, and hard for vendors to find customers who have needs well-suited to their databases. By helping SingleStore solve this problem, we think we can help the company accelerate its already impressive growth.
If we get all of this right, I think the simplest way to describe our vision is that in 3-5 years, SingleStore will be considered an essential component of any enterprise’s AI architecture. Specifically, the component that powers real-time AI agents that make complex decisions and act on them. We see this as a multi-billion dollar opportunity.
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Stephen Goodman is a Principal and joined Vector Capital in 2014.
Stephen currently sits on the Board of Directors of Planful and Riverbed. Stephen was also involved in Vector’s investments in MarkLogic, Synamedia, Saba, and Vispero, as well as the portfolio management of Gerber Scientific and Cambium Networks.
Prior to Vector, Stephen worked as an Associate Consultant at Bain & Company in Boston. While at Bain, he provided strategic guidance to clients in the Technology, Telecommunications, and Healthcare sectors, and worked on a number of M&A transactions as part of Bain’s Private Equity Group.
Stephen received his MBA with distinction from the Harvard Business School. He graduated from the Massachusetts Institute of Technology with a B.S. in Physics.