Deephaven
Deephaven Data Labs builds a real-time query engine using proprietary "live dataframe" technology with incremental delta processing. It sells a free open-source Community edition and an Enterprise tier ($200K–$2M+/yr) primarily to capital markets customers.
- Company typePrivate
- Founded2017
- HeadquartersNew York, United States
- Headcount11–50
- GTM typeB2B
- OfferingSoftware
What Deephaven does
Deephaven Data Labs, LLC operates a real-time query engine built around a proprietary abstraction called "live dataframes" — column-oriented structured tables that update incrementally via delta propagation through a directed acyclic graph (DAG), rather than via micro-batching. The engine runs on the JVM with an embedded SQL Calcite interface, a bi-directional Python-Java bridge (jpy), and the Barrage wire protocol (an Apache Arrow Flight extension over gRPC), and exposes idiomatic client APIs in Python, Java, Go, R, C++, and JavaScript. The platform unifies real-time streaming and historical batch workloads in a single engine, with native connectors to Kafka, Redpanda, Iceberg, Parquet, S3, Arrow Flight SQL, JDBC/ODBC/ADBC, WebSockets, and several Python ML libraries (PyTorch, TensorFlow, Scikit-Learn, TensorBoard).
The product is delivered on an open-core model. The Community Core edition is free under the Deephaven Community License, with supporting Apache 2.0 building blocks (Barrage, jpy, Web Client UI, VS Code Extension, Deephaven Express, deephaven.ui) available on GitHub and Docker. The commercial Deephaven Enterprise tier is sold via direct field sales at $200K–$2M+ USD annually and adds multi-user ACLs, clustered analytics, SSO, auditing, Kubernetes/Podman/Helm deployment, Excel sync, dedicated support, and scaled fanout across thousands of cores.
Deephaven's customer base is concentrated in capital markets: top-3 bulge bracket banks, top-3 investment and commercial banks, top-3 hedge funds, top-5 crypto exchanges, top-5 stock exchanges, top-5 derivative players, and top-10 quant funds use the platform for front-office trading, risk, compliance, simulation, and operations monitoring. The company was founded in 2017 when CEO Pete Goddard and six principal engineers spun the technology out of Walleye Capital, where it had been developed since 2012. It is privately held and founder-controlled, with no disclosed external funding rounds, ~46 employees (44 engineers, 1 salesperson, plus a designer and technical writer), and offices in Plymouth, MN (HQ), New York, NY, and Colorado Springs, CO.
Deephaven firmographics
Firmographics- Name
- Deephaven
- Legal name
- Deephaven Data Labs, LLC
- Website
- https://deephaven.io
- Company type
- Private
- Founded year
- 2017
- Operating status
- Operating
- Headcount range
- 11–50 employees
- Short description
- Deephaven Data Labs builds a real-time query engine using proprietary "live dataframe" technology with incremental delta processing. It sells a free open-source Community edition and an Enterprise tier ($200K–$2M+/yr) primarily to capital markets customers.
- Ownership category
- akta.pro rank
Deephaven industry classification
Industry- Product category
- Real-Time Query Engine
- NAICS
- Software Publishers (513210), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (5182), Computing Infrastructure Providers, Data Processing, Web Hosting, and Related Services (518)
- SIC
- Services-Prepackaged Software (7372), Services-Computer Integrated Systems Design (7373)
- akta.pro primary industry
- Query Engines & SQL Analytics Layers for Warehouses/Lakes (HDAEABAI)
- akta.pro secondary industries
- Event Streaming & Pub/Sub Platforms (HDAEAIAD), Columnar / Analytical Databases (OLAP) (HDAEAAAG)
Keywords
Where Deephaven is headquartered
LocationHeadquarters
- HQ city
- New York
- HQ country
- United States
- HQ region
- North America
Offices3 records
Markets served
Deephaven business model
Business model- GTM type
- B2B
- Offering type
- Software
- Cost components
- Personnel, Technology or R&D, Infrastructure, Operations, Marketing or Sales
Revenue model
- Enterprise Subscription: Annual or multi-year subscription licenses for enterprise customers. Includes multi-user access, ACL permissions, clustered systems, SSO, auditing, dedicated support, pro services, and enterprise-only features like pivot tables and Excel sync. Pricing ranges from $200K to $2M+ annually depending on scale and requirements.
- Community Edition (Freemium): Free open-source version of the core query engine available on GitHub. Includes core engine, APIs for stream ingestion and batch, authorization/authentication hooks, app-mode for microservices, and query engine. No revenue directly from community but drives enterprise conversion pipeline.
Pricing tiers
| Model | Billing | Price |
|---|---|---|
| Freemium | Others | Community Edition - Free open-source version with core features |
| Subscription | Annual | Enterprise Edition - Full-featured enterprise platform for $200K - 2M+ annually |
Go-to-market motion2 records
Distribution channels3 records
Marketing channels7 records
Deephaven product offering
Product offeringCore offering
Deephaven provides a high-performance real-time query engine that processes live dataframes—column-oriented structured tables that update incrementally based on data changes (deltas) rather than micro-batching. The platform unifies streaming and batch data, exposes gRPC-based APIs in Python, Java, Go, R, C++, and JavaScript, and ships with an embedded SQL interface via Apache Calcite. It is distributed under an open-core model with a free Community edition (Apache 2.0) and a paid Enterprise edition priced at $200K–$2M+ USD/year, targeting data-intensive financial and analytics workloads.
Product overview
Deephaven is a real-time query engine company offering a platform-plus-modules architecture. The core product is the Deephaven query engine for processing live dataframes with millisecond response times. The offering is split into two tiers: Deephaven Community Core (free, open-source) provides the core engine with APIs, authentication hooks, and open-source building blocks; Deephaven Enterprise (paid, $200K-2M+/yr) adds multi-user support, high-availability clustering, and enterprise integrations. Supporting products include language-specific client APIs (Python, Java, Go, R, C++, JavaScript, C#), UI frameworks (deephaven.ui, Deephaven Express), communication protocols (Barrage), development tools (VS Code Extension, Web Client UI), and the Python-Java bridge (jpy). The platform processes structured data streams for finance, analytics, and real-time applications.
Differentiator
Problem solved
Functional benefit
Brands
- Deephaven Core: Open-source, Apache 2.0 licensed query engine — the core engine and base platform for Deephaven's real-time data technology.
- Deephaven Community Core
- Deephaven Enterprise
Products and services
- Deephaven Community Core Free, Apache 2.0-licensed open-source edition of the Deephaven real-time query engine. Includes the core engine, APIs for stream ingestion and batch processing, authorization and authentication hooks, App-mode for microservices, query engine for pipelines, and open-source building blocks (Barrage, jpy, Web Client UI, VS Code Extension, Deephaven Express, deephaven.ui). Designed for developers, quants, and smaller companies to evaluate and adopt Deephaven.
- Deephaven Enterprise Commercial, subscription-based edition of Deephaven for enterprise customers, priced at $200K–$2M+ USD/year. Adds multi-user access, ACL permissions, pivot tables, clustered analytics, autonomous applications, auditing, SSO, UIs at scale, dedicated support, pro services, scalability across 1000s of cores and TBs of streams, centralized ingestion of external feeds, real-time persistence of live dataframes, fanout at scale, Kubernetes/Podman/Helm deployment, and an Excel sync plugin.
- deephaven.ui Open-source Python library for building browser-based data applications optimized for live data. Similar in style to Streamlit, Reflex, or Dash but designed to render and interact with real-time, incremental data updates in the browser.
- Deephaven Express Open-source real-time wrapper for Plotly Express that accepts live dataframes as the data source and provides auto-updating versions of popular Plotly visualizations, with control over figure layout and formatting.
- MCP Integration Model Context Protocol (MCP) integration enabling AI development workflows with Deephaven, allowing large language models and AI agents to interact with live dataframes and real-time data processing.
Quantifiable outcome
- Millisecond response times for complex queries even under high throughput
- +2 more outcomes
Companies that use Deephaven
Customer profileNamed customers10 records
Segments3 records
Ideal customer profiles3 records
Deephaven technology and API
TechnologyTechnology focussed Yes
API detail
- Has API
- Yes
- API docs
- API detail
Core technology
AI maturity
App detail
Integration29 records
AI capability3 records
Feature6 records
Deephaven partnerships and signals
Strategic signalScale indicators4 records
Recent moves7 records
Expansion highlights6 records
Deephaven competitors and assessment
Company assessmentEmerging players
- QuestDB: QuestDB is an open-source time-series database optimized for high-throughput ingestion and SQL analytics on financial and IoT data. QuestDB competes at the lower end of the time-series analytics market with a more SQL-centric and developer-friendly approach, overlapping with Deephaven's community edition use cases.
- StarRocks: StarRocks is a high-performance real-time OLAP database focused on multi-dimensional analytics and real-time dashboards at scale. It overlaps with Deephaven in real-time analytics but is positioned more as a drop-in MPP OLAP replacement than a streaming-first query engine.
Broad incumbents
- Snowflake: Snowflake is a dominant cloud data platform that has been adding real-time and streaming data capabilities. As a broad incumbent with massive enterprise footprint and substantial R&D budget, Snowflake represents the strategic risk that Deephaven's customers could consolidate real-time analytics workloads into an existing Snowflake contract.
- Confluent: Confluent, built around Apache Kafka, provides a streaming data platform with ksqlDB for stream processing queries. While Confluent is more focused on the streaming transport and ETL layer than analytical queries, its ksqlDB product overlaps with parts of Deephaven's real-time analytics value proposition.
- Databricks: Databricks offers a unified lakehouse platform with Delta Live Tables and structured streaming for real-time data processing. Databricks competes for enterprise data platform budgets, including the financial services segment where Deephaven sells, as a broad platform play rather than a real-time-specialized engine.
Direct peers
- ClickHouse: ClickHouse is an open-source columnar OLAP database optimized for real-time analytical queries on large datasets. Deephaven competes with ClickHouse in real-time analytics workloads, particularly where streaming ingestion and sub-second query latency are required for trading and monitoring applications.
- TimescaleDB: TimescaleDB is an open-source time-series database built on PostgreSQL, offering hypertables, continuous aggregates, and real-time analytics for time-series workloads. Deephaven and TimescaleDB compete for the same developer-led time-series analytics mindshare, though Deephaven emphasizes streaming/real-time updates more strongly.
- KX (kdb+): KX's kdb+ is the dominant time-series database for capital markets, used by most major banks, hedge funds, and exchanges for tick data and real-time analytics. Deephaven competes directly with kdb+ for the same high-performance, low-latency capital markets workloads, with a different language (Python-first vs q) and incremental-update model.
- Materialize: Materialize is a streaming SQL database built on incremental view maintenance, providing real-time updates to query results as underlying data changes. Materialize's incremental computation model directly mirrors Deephaven's live dataframes approach, making it a close architectural and product peer in real-time analytics.
- Apache Druid: Apache Druid is a high-performance real-time analytics database designed for sub-second queries on streaming and batch data, widely used in fintech and observability. Deephaven competes with Druid in real-time analytics pipelines, particularly for dashboards, monitoring, and trading analytics use cases.
Market position
Strengths4 records
Weaknesses4 records
Competitive moat5 records
Key risks5 records
Key highlights7 records
Customer concentration
Deephaven social profiles
Digital presenceDeephaven financial estimates
Financial estimateRevenue estimate
Valuation estimate
Deephaven leadership team
Management profileNumber of profiles
Profiles1 record
Deephaven funding detail
Funding detailFunding overview
Funding rounds
Investors
Funding detail is available on the Subscription and Enterprise plan.Contact sales →
Deephaven M&A and investment
M&A and investmentM&A
Investments
M&A and investment is available on the Subscription and Enterprise plan.Contact sales →
Frequently asked questions about Deephaven
What does Deephaven do?
Deephaven provides a high-performance real-time query engine that processes live dataframes—column-oriented structured tables that update incrementally based on data changes (deltas) rather than micro-batching. The platform unifies streaming and batch data, exposes gRPC-based APIs in Python, Java, Go, R, C++, and JavaScript, and ships with an embedded SQL interface via Apache Calcite. It is distributed under an open-core model with a free Community edition (Apache 2.0) and a paid Enterprise edition priced at $200K–$2M+ USD/year, targeting data-intensive financial and analytics workloads.
Is Deephaven a public or private company?
Deephaven is a private company. It is classified as founder individual operated bootstrapped and is currently operating.
When was Deephaven founded?
Deephaven was founded in 2017. It employs 11 to 50 people.
Where is Deephaven based?
Deephaven is headquartered in New York, United States, in the North America region.
How does Deephaven make money?
Two revenue lines are on record. Enterprise Subscription is the primary driver. The others are community Edition (Freemium).
Who are Deephaven's main competitors?
Emerging players on record are QuestDB and StarRocks. Broad incumbents are Snowflake, Confluent and Databricks. Direct peers are ClickHouse, TimescaleDB, KX (kdb+), Materialize and Apache Druid.
Does Deephaven have an API?
Yes. Deephaven provides gRPC-based APIs in Python, Java, Go, R, C++, JavaScript, and C#. Users can submit queries through server-side interfaces using idiomatic client APIs. The Barrage wire protocol, based on Apache Arrow Flight, enables efficient data exchange. The platform also offers MCP (Model Context Protocol) integration for AI development workflows. Developer documentation is at docs.deephaven.io.
What industry is Deephaven in?
Deephaven's product category is Real-Time Query Engine. Its primary akta.pro industry code is HDAEABAI, Query Engines & SQL Analytics Layers for Warehouses/Lakes, with a secondary code of HDAEAIAD, Event Streaming & Pub/Sub Platforms. Its NAICS code is 513210 and its SIC code is 7372.