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.
Intelligent Analytics, from Raw Data to Insights
TDengine’s built-in stream engine continuously monitors data to generate KPIs and trigger alerts, while its TDgpt-powered AI engine identifies behavioral deviations without manual rules. For deeper insights, process analytics tools like batch comparison, trend analysis, and correlation help engineers move from “what happened” to “why it happened,” all within a single platform.
AI-Powered Insights: Data That Speaks for Itself
TDengine weaves AI throughout the entire data platform. Zero-Query Intelligence automatically delivers dashboards, insights, and KPI recommendations based on your data and business context. When deeper investigation is needed, the AI assistant generates analyses on demand, while root cause analysis investigates alerts and TDgpt enables anomaly detection, forecasting, and data imputation directly in the database.
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 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.
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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
We’re excited to announce that TDengine IDMP now supports Spanish and Korean, available starting in version 1.0.15.2.
TDengine IDMP Now Supports Spanish and Korean
by TDengine Team
April 2, 2026
What industrial users need is a new kind of visualization—one that is asset-centric, event-aware, insight-driven, and tightly integrated with the data foundation.
Asset-Centric and Event-Centric Visualization: From Dashboards to Operational Understanding
by Jeff Tao
April 2, 2026
Organizations increasingly expect systems to generate insights—detect anomalies, predict future behavior, identify patterns, explain deviations and analyze the root cause.
Advanced Analytics in Industrial Systems: Beyond the Historian
by Jeff Tao
April 2, 2026
Assets define what exists. Events define what happens. Only when both are modeled together can we truly understand industrial operations—and only then can AI become genuinely useful.
Event-Centric + Asset-Centric: The Missing Link in Industrial Data
by Jeff Tao
March 30, 2026
A low OEE number by itself is not very useful. What matters is whether the loss is coming from uptime, speed, or quality, and whether the team can isolate the cause quickly enough to act.
How AI Helps Engineers Move from OEE Monitoring to Root-Cause Analysis
by Jim Fan
March 29, 2026
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
