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TDengine provides an AI-powered data historian, fundamentally a high-performance time-series database engineered for the demands of industrial data. This core product integrates an industrial data management layer and an AI copilot, enabling efficient processing and analysis of vast streams of time-stamped operational data. Its architecture is specifically designed to manage the scale and velocity inherent in IoT and industrial environments, offering capabilities tailored for data ingestion, storage, and querying.
The company was founded by Jeff Tao, who identified a critical need for purpose-built data infrastructure capable of handling the unique challenges posed by time-series data in the industrial sector. His insight centered on developing a system that could not only store massive datasets but also make them readily accessible and intelligent-ready for emerging analytical applications. This foundational understanding guided the development of a specialized database solution.
TDengine serves enterprises and organizations grappling with complex industrial data management, particularly those leveraging IoT devices and large-scale sensor deployments. The company's vision is to democratize industrial data systems, providing users with the freedom and tools to extract maximum value from their information. Ultimately, TDengine aims to make industrial data inherently AI-ready, fostering environments where intelligence can organically emerge from the collected data itself.
TDengine has raised $57.0M across 2 funding rounds.
TDengine has raised $57.0M in total across 2 funding rounds.
TDengine is an AI-powered data platform specializing in time-series databases for Industrial IoT (IIoT) and Industry 4.0 applications. It builds high-performance solutions like TDengine TSDB for ingesting, storing, analyzing, and distributing petabytes of real-time data from billions of sensors daily, alongside TDengine IDMP, an AI-native system for automated insights.[1][3][5] Serving sectors such as energy, manufacturing, renewable energy, automotive, and IT infrastructure, TDengine solves the challenges of managing massive sensor data for use cases like predictive maintenance, energy optimization, equipment monitoring, and smart cities—making big data accessible and affordable without heavy reliance on data experts.[1][2][4] Customers include Siemens, Mingyang, Gotion, and Nevados, with strong growth in digital transformation for traditional industries.[1][2]
Founded in 2017 and headquartered in Los Gatos, California, TDengine emerged from Taos Data to tackle the data overload in industrial IoT, where traditional databases falter under petabyte-scale time-series workloads.[2][5] CEO Jeff Tao, the founder, drove the vision for a purpose-built platform, evolving from open-source TDengine TSDB-OSS to enterprise-grade offerings like TDengine TSDB-Enterprise and cloud services on AWS, Azure, and GCP.[3][5][7] Early traction came from addressing Industry 4.0 pain points, such as real-time data from sensors in manufacturing and energy, leading to innovations like the 2025 launch of TDengine IDMP for AI-driven insights without manual queries.[4][7]
TDengine rides the IIoT and Industry 4.0 wave, where exploding sensor data (petabytes daily) demands real-time analytics amid digital transformation in legacy sectors like energy and manufacturing.[1][5] Timing is ideal as AI democratizes insights—TDengine IDMP breaks data silos, enabling ESG compliance, predictive maintenance, and smart infrastructure without big tech budgets.[3][4] Market forces like renewable energy growth, regulatory pressures (e.g., GMP), and edge computing favor its scalable, low-cost model over general databases.[2][4] It influences the ecosystem by empowering startups to multinationals with accessible AI tools, accelerating adoption in underserved industrial AI.[3]
TDengine is poised to dominate AI-native industrial data with IDMP's automated insights, expanding into cloud-hybrid deployments and custom AI integrations amid rising IIoT demands.[3][7] Trends like agentic AI, edge processing, and sustainability (e.g., carbon tracking) will propel growth, potentially capturing more Fortune 500s in energy and manufacturing.[1][4] Its influence may evolve from database specialist to full industrial AI platform, unchaining traditional industries much like cloud did for tech—unlocking data value at scale.[3] This positions TDengine as a quiet powerhouse in the next wave of industrial digital transformation.
TDengine has raised $57.0M across 2 funding rounds. Most recently, it raised $47.0M Series B in May 2021.
| Date | Round | Lead Investors | Other Investors | Status |
|---|---|---|---|---|
| May 1, 2021 | $47M Series B | Matrix Partners China | 5Y Capital, Granite Asia, GGV Capital, Hongshan Capital Group (Sequoia Capital China), Index Capital | Announced |
| Jan 1, 2020 | $10M Series A | — | 5Y Capital, Granite Asia | Announced |
TDengine has raised $57.0M in total across 2 funding rounds.
TDengine's investors include Matrix Partners China, 5Y Capital, Granite Asia, GGV Capital, Hongshan Capital Group (Sequoia Capital China), Index Capital.
