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What’s in today’s newsletter:

• Databricks raises $7B, valued at $134B 💰🧱

• Apple Acquires Graph Database Startup Kuzu🍎📈

• OpenAI runs ChatGPT efficiently on PostgreSQL database 🚀

Also, check out the weekly Deep Dive - A look into SingleStore

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DEEP DIVE

A detailed look into SingleStore

During the week, I run an inordinate amount of deep research reports for myself personally, for the job, and of course, for this very newsletter.

I just subscribed to Claude, and in its infinite wisdom, the report spat out by Opus 4.6 was aptly named “the database that wants to do everything”:

What a great and appropriate place to start.

TL;DR: SingleStore — From IPO Hopeful to AI Powerhouse

• The Big Shift: SingleStore has officially moved off the IPO track following a $500M growth buyout by Vector Capital. The move shifts the company from "venture-scale at all costs" to a disciplined, cash-flow-positive focus on the enterprise AI stack.

• The Financials: Currently at $123M+ ARR (23% YoY growth), SingleStore is being positioned as a "silver medalist" asset—high-quality tech that thrives under Private Equity’s long-term R&D runway.

• Technical Edge: Their proprietary "Universal Storage" engine remains the gold standard for HTAP (Hybrid Transactional/Analytical Processing). It eliminates the "ETL tax" by handling real-time transactions and deep analytics in a single system.

• The AI "Single-Shot": Unlike specialized vector databases (like Pinecone), SingleStore enables "Single-Shot Retrieval." This allows developers to join vector similarity scores with live operational data (metadata, permissions, inventory) in a single SQL query—a massive win for production-grade RAG.

• The Verdict: While it faces a "market squeeze" from Snowflake and the ubiquity of PostgreSQL, SingleStore remains the go-to for ultra-low latency use cases (e.g., real-time fraud detection) where traditional data warehouses are too slow and document stores are too limited.
  

Deep Dive: SingleStore — The Strategic Shift from IPO to Private Equity

While the database market often focuses on the largest players like Snowflake and Databricks, SingleStore (formerly MemSQL) continues to occupy a specific niche in real-time analytical and transactional processing. Recently, the company underwent a significant corporate restructuring, moving away from an IPO track toward a private equity buyout.

This analysis examines SingleStore's current financial standing, its "Universal Storage" architecture, and its strategic pivot toward the enterprise AI stack under new ownership.

Corporate Strategy: The Vector Capital Buyout

In late 2025, SingleStore entered into an agreement for a growth buyout led by private equity firm Vector Capital. This move marks a departure from the company's previous trajectory toward the public markets.

• Investment Thesis: Vector Capital’s CIO has described SingleStore as a "silver medalist" asset—a high-quality technology company that, while performing well, did not achieve the venture-scale returns initially targeted during the zero-interest-rate policy (ZIRP) era.

• Capital Structure: The transaction involved approximately $500 million in equity commitments. Major existing investors, including Google Ventures, Dell Technologies Capital, and IBM, retained stakes in the company.

• Financial Discipline: The company is currently operating near cash-flow break-even. As of Q2 FY2026 (ending mid-2025), SingleStore reported Annual Recurring Revenue (ARR) of over $123 million, representing 23% year-over-year growth.

Technical Architecture: Universal Storage and Hybrid Workloads

SingleStore’s core technical value proposition remains its proprietary "Universal Storage" engine, designed to handle Hybrid Transactional/Analytical Processing (HTAP) workloads within a single system.

1. Universal Storage Engine Historically, databases separated row-oriented storage (optimized for transactional writes/seeks) and column-oriented storage (optimized for analytical scans). SingleStore combines these:

• Write Path: Incoming data is written to an in-memory rowstore buffer for low latency.

• Read/Storage Path: Data is asynchronously flushed to a disk-based columnstore for compression and large-scale analytics.

• Seekable Columnstore: SingleStore implements hash indexes on columnar data, allowing the system to perform point lookups (seeking specific rows) on compressed columnar data with sub-millisecond latency.

2. Separation of Compute and Storage (Helios) SingleStore’s managed cloud service, Helios, utilizes "Workspaces" to decouple compute resources from storage.

• Architecture: The system allows for a Primary Workspace (Read/Write) and multiple Read-Only Workspaces attached to the same database.

• Workload Isolation: This architecture allows organizations to isolate heavy analytical queries or AI inference jobs on separate compute nodes without impacting the performance of the primary transactional workload.

3. SingleStore Kai (MongoDB Compatibility) To address the NoSQL market, SingleStore released "Kai," an API layer compatible with the MongoDB wire protocol. This feature allows developers to run queries on JSON data using SingleStore’s columnar engine, with the company claiming performance improvements of 100x–1,000x for analytical queries on JSON datasets compared to native document stores.

The AI Strategy: "Single-Shot Retrieval"

SingleStore is positioning itself as a foundational database for AI applications, specifically targeting Retrieval-Augmented Generation (RAG) workflows. Their strategy focuses on "single-shot retrieval," which combines vector search, keyword search, and SQL filtering in a single query execution.

• Vector Capabilities: The database supports native vector storage with indexing algorithms including HNSW (Hierarchical Navigable Small World) and IVF (Inverted File Index) to support Approximate Nearest Neighbor (ANN) search.

• Performance: Queries utilize SIMD (Single Instruction, Multiple Data) vector processing for hardware acceleration.

• Hybrid Search: Unlike specialized vector databases (e.g., Pinecone), SingleStore allows users to join vector similarity scores with standard SQL operational data (e.g., inventory levels or user permissions) in real-time.

Market Positioning and Competitive Landscape

The "Bear" View

• Market Squeeze: SingleStore faces competition from multiple directions. Snowflake and Databricks are increasingly adopting HTAP features (e.g., Snowflake Unistore), while PostgreSQL (via pgvector) serves as a "good enough" solution for many developers.

• Operational Complexity: While SingleStore reduces the need for separate ETL pipelines, managing a distributed HTAP system can be more complex than using fully serverless data warehouses like BigQuery.

• Cost Management: Running a persistent, high-performance database cluster can be more expensive for sporadic workloads compared to "scale-to-zero" architectures.

The "Bull" View

• Real-Time Requirements: SingleStore retains a performance advantage in ultra-low latency scenarios, such as "fraud detection on the swipe" (processing queries in under 50ms), where traditional data warehouses typically struggle.

• Infrastructure Economics: By consolidating operational and analytical workloads, SingleStore argues it can lower Total Cost of Ownership (TCO) by eliminating data duplication and reducing license costs for separate systems.

• Stable Backing: The Vector Capital buyout provides a runway for long-term R&D without the short-term pressures of quarterly public market reporting.

Conclusion

SingleStore has established itself as a viable option for enterprises hitting the performance limits of general-purpose databases or the latency floors of data warehouses. With its transition to private equity ownership, the company appears focused on operational efficiency and sustainable growth, targeting high-value use cases in real-time analytics and enterprise AI infrastructure.

Gladstone Benjamin