AI-Native Industrial Data Foundation
Move beyond legacy historians with an open foundation for industrial time-series data, asset context, real-time analytics, and AI-driven operations.
Trusted by over 1,000 industrial companies worldwide
Why TDengine?
AI-Ready Data Foundation
TDengine connects to sources like OPC, MQTT, and Kafka with built-in ETL for cleaning and transforming data. Its tree hierarchy, reusable templates, and rich metadata bring contextualization and standardization to your data and help you prepare your business for AI-driven innovation.
10x Performance at 10% Cost
TDengine is built for time-series data, with a specialized storage engine that outperforms general-purpose databases in data ingestion, querying, and compression. Its efficient architecture with automated tiered storage and S3 support minimizes data footprint and significantly reduces your storage costs.
From Raw Data to Real-Time Insight
TDengine supports standard SQL and a rich set of time-series functions, with a built-in stream processing engine for millisecond-level analytics. AI agent TDgpt enables forecasting and anomaly detection in a single SQL statement, powered by machine learning and time-series foundation models.
Zero-Query Intelligence, Let Your Data Speak
TDengine understands your business context and automatically generates dashboards, reports, and real-time analyses without requiring manual setup or domain expertise. Our AI agent pushes relevant insights directly to you, making advanced data analytics accessible to everyone.
Open Ecosystem, Unlimited Connectivity
TDengine is built on an open-source core and integrates with a rich ecosystem of third-party BI, AI, and other products over open interfaces. It supports industrial protocols like MQTT and OPC, making it easy to unify data across systems and sites, and ensures you stay free to build and scale without vendor lock-in.
Open ecosystemTDengine: AI-Native Data Foundation
TDengine is composed of two products seamlessly integrated:
More than just a database, TDengine delivers everything a traditional historian provides and more: high-performance time-series storage, industrial data management, contextualization, analytics, events, visualization, and AI.
TDengine in Action
See how TDengine supports real operational scenarios from data collection to analysis across industrial environments.
Data Encryption
Role-based Access Controls
IP Whitelisting
Data Backup & Restoration
Disaster Recovery
24/7 Support
Security & Compliance
Learn MoreProudly Open Source
TDengine TSDB-OSS, a fully open-source time-series database that includes clustering capabilities, serves as the foundation for all our paid offerings. Along with our vibrant open-source community, TDengine TSDB-OSS continues to innovate in the field of time-series data management.
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Latest Updates
By comprehensively addressing performance bottlenecks in data ingestion, storage, and computation for massive time-series workloads, TDengine has made the redrying process more digitalized, transparent, and intelligent.
Powering a Next-Generation Digital Redrying Facility with TDengine
by TDengine Team
March 27, 2026
This project has validated TDengine’s suitability for handling massive time-series data in the tobacco industry, providing a reusable technical approach for digital transformation across the sector.
Building a Foundation for AI-Driven Manufacturing at Kunming Cigarette Factory
by TDengine Team
March 27, 2026
To fully realize the value of industrial data, events need to become a native part of the data foundation, not an optional layer.
Why Time-Series Data Alone Is Not Enough: Rethinking Industrial Event Analysis in the Age of AI
by Jeff Tao
March 26, 2026
To fully understand industrial operations, data models must move beyond structure and begin to represent behavior.
Asset-Centric Modeling: The Foundation of Industrial Data Context
by Jeff Tao
March 24, 2026
Leveraging TDengine, Dali Cigarette Factory has implemented comprehensive data collection, storage, and analysis for cigarette rolling and packaging equipment, covering more than 40,000 monitoring points.
From Wonderware to TDengine: Modernizing Data Infrastructure at Dali Cigarette Factory
by TDengine Team
March 20, 2026
Refinery performance does not live in one screen. Throughput, blend quality, and site economics move together, and operations teams need a way to see them together if they want to respond earlier and operate more consistently.
Seeing Throughput, Blend Quality, and Margin Together in Refinery Operations
by Jim Fan
March 19, 2026
For decades, the Data Archive has been the core component of industrial data historians. However, when viewed through the lens of industrial internet, IoT, and AI, the assumptions behind Data Archive no longer hold.
From Data Archive to TSDB: Why the Industrial Data Foundation Must Be Rebuilt
by Jeff Tao
March 19, 2026
Generic dashboards are great for flexible charting. But industrial teams need more than charts. They need context, repeatability, and a system that reflects how operations actually work.
Why Asset-Centric Visualization Is Better Than Grafana for Industrial Operations
by Jim Fan
March 19, 2026
Data historians solved one of the hardest problems in industrial computing: reliably storing massive volumes of operational data. But in the AI era, simply storing data is no longer enough.
From Data Historian to AI-Native Industrial Data Foundation
by Jeff Tao
March 18, 2026
