Bring AI to
Every Industrial Data Stream
Built on a high performance time series database, industrial ontology, and industrial agent runtime, TDengine brings AI capabilities to industrial scenarios, providing a full-stack solution for industrial data, from collection, storage, and real-time analysis to visualization, event management, and root cause analysis. A modern alternative to data historian.
Trusted by over 1,000 industrial companies worldwide
Why TDengine
Modernize your industrial data historian
Get everything you rely on from traditional data historians such as the AVEVA PI System, from data collection and asset modeling to visualization, events, calculations, reporting, security, and high availability, plus advanced analytics and AI-powered insights in one integrated platform.
Extreme performance for massive data streams
A purpose-built time-series architecture handles high-volume, high-cardinality sensor data with millisecond-scale ingestion, queries, and real-time analysis—delivering more than 10× the performance of general-purpose databases while reducing storage costs by up to 90%.
AI-Driven decisions, from days to minutes
Industrial ontology gives AI the context to analyze operating behavior, identify anomalies, uncover insights, and investigate potential causes, bringing expert-level analytical capabilities directly to frontline teams.
The AI-native Industrial Data Platform
Bring industrial data together with zero code, then store it efficiently
TDengine connects to OPC, MQTT, Kafka, and other industrial data sources. Built-in ETL handles cleaning and transformation before the data enters the platform.
Its storage engine delivers more than 10 times the write and query performance of general-purpose databases, while reducing storage costs to one tenth. Multi-level storage and hot/cold tiering help it support hundreds of millions of Data Collection Points reliably.
Give AI the context behind your data with industrial ontology
TDengine builds a unified business information layer between storage and applications. Asset hierarchies are organized in a tree structure, while network models describe horizontal relationships between devices.
Through data standardization and contextualization, scattered raw data becomes meaningful industrial assets. AI can then follow business context through the plant, instead of working only with database names and table names.
Turn live data streams into operational insight
TDengine supports a wide range of industrial monitoring scenarios, with multiple trigger types for different operating needs. Teams can build real-time monitoring logic through a visual interface, or describe the requirement in natural language and let AI suggest the configuration.
Each trigger creates a structured event with full context, connecting detection, analysis, and response. High-frequency industrial data becomes insight that engineers can review and AI can use for reasoning and action.
Put AI to work in real operations with industrial agent runtime
TDengine connects to major LLMs without tying users to a built-in model or a single vendor. Its knowledge base turns private enterprise documents into a searchable knowledge graph. Skills convert industrial know-how into standard workflows that AI agents can call.
Thirteen built-in AI assistants support the workflow from data queries and panel creation to root cause analysis and report generation. The security model is built around control, trust, auditability, and resilience, so AI strengthens operations without becoming a single point of dependence.
Process analytics helps engineers trace problems to their source
TDengine provides a no-code analysis workbench for multi-event comparison, scatter chart regression, clustering, trend forecasting, and similarity search.
Statistical calculations run inside the database, so engineers do not need to export data before analysis. They can investigate problems where they appear, trace them back to likely causes, and turn operating experience into practical decisions.
Process analyticsProvide an open platform that keeps pace with fast-moving AI technology
TDengine uses a loosely coupled, modular architecture. Each component can be deployed, scaled, or replaced independently. Through the Model Context Protocol (MCP), more than 50 system capabilities can be exposed to external AI agents.
Automation teams can use the CLI, while developers can work with a REST API described by the OpenAPI Specification. JDBC, ODBC, Kafka, MQTT, iframe embedding, and Excel Add-In support keep semantic data available across tools and workflows. TDengine can run in the public cloud, on premises, or in edge-cloud deployments without locking users into one vendor stack.
Turn tacit know-how into reusable assets with knowledge management
TDengine turns tacit enterprise knowledge into five manageable forms. Industrial Ontology gives data business meaning. Documents become a searchable knowledge graph. Calculation expressions turn expert judgment into monitoring logic that keeps running. Analysis models run directly in the database. Skills package complex tasks as standard workflows for AI agents.
Five-tier security policies protect this knowledge across the process, and the knowledge remains under the user’s control. What used to live in individual memory becomes a reusable enterprise asset.
Knowledge ManagementTDengine Overall Architecture
TDengine consists of two integrated components:
- TDengine TSDB: high-performance time-series database
- TDengine IDMP: AI-powered industrial data management platform
TDengine TSDB has built-in data ingestion, data storage, and query processing engines. TDengine IDMP includes an industrial ontology, atomic applications, and the Industrial Agent Runtime.
The two integrate into a single AI-native industrial data platform. It covers data collection, storage, analysis, visualization, and AI applications. Raw data goes in, operational decisions come out. A modern alternative for data historian.
TDengine Industrial Use Cases
Discover how TDengine is used in real operational scenarios
Explore More Industrial Use Cases
SOC 2® Type II certified
ISO 27001 certified
21 CFR Pat 11 Technical Controls
EU GMP Technical Controls
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
In the previous article, we discussed the evolution of industrial data infrastructure: from data historians, to industrial data platforms, and now to AI-native industrial data foundations. This evolution is not just about adding new capabilities—it is fundamentally about rethinking the underlying architecture. At the center of this shift lies a critical but often overlooked change:
From Data Archive to TSDB: Why the Industrial Data Foundation Must Be Rebuilt
by Jeff Tao
November 3, 2026
Visualize and compare multiple events by a simple click in TDengineOver the years, one concept in the PI System has always impressed me: Event Frames. For those who work with industrial operational data, the idea is simple but powerful. Instead of looking only at raw time-series signals, Event Frames convert continuous data streams into discrete
Event Frames: One of the Most Brilliant Ideas in Industrial Data — And Why They Matter Even More in the AI Era
by Jeff Tao
November 3, 2026
Moving industrial time-series workloads to the cloud should make your data infrastructure easier to manage. But that depends on whether your database gives you the same product everywhere or adds another layer of complexity. This is where TDengine and AVEVA PI System take different paths. One Unified Platform vs. Two Connected Systems As a modern,
Cloud Experience: TDengine vs. PI System
by Arun Arulraj
November 3, 2026
In the world of industrial data, ownership and portability define how much control you truly have. Once your time-series data is trapped in a proprietary ecosystem, every migration, integration, or upgrade becomes a challenge. Two Philosophies on Data Ownership TDengine is an AI-native industrial data platform built for openness and interoperability, combining time-series data management,
Vendor Lock-In and Data Freedom: TDengine vs. PI System
by Jeff Tao
November 2, 2026
Visualization Is Where Engineers Meet Data In industrial systems, visualization is where everything comes together. Data may be stored in historians or processed through analytics pipelines, but for engineers and operators, the primary interface to the system is the visualization layer. This is where they monitor equipment, investigate issues, and make decisions. If visualization is
Asset-Centric and Event-Centric Visualization: From Dashboards to Operational Understanding
by Jeff Tao
November 2, 2026
For organizations evaluating alternatives to AVEVA PI System, the decision goes beyond time-series data storage. Asset modeling, real-time analytics, integration, and migration effort all play an important role. This article compares PI System with TDengine, a modern alternative to traditional data historians, to help you evaluate the right solution for your industrial data infrastructure. About
Compare PI System with TDengine
by Jeff Tao
October 19, 2026
Across three production energy sites, TDengine uses about 2% of one CPU core per appliance—leaving compute available for real-time control and local AI. At DJIN EMS, we record 35 million rows of energy data every day with TDengine TSDB. By September 2026, our three operating sites had accumulated more than two billion rows in under
How DJIN EMS Captures 35 Million Rows a Day with TDengine at the Grid Edge
by Nick Hughes
October 14, 2026
Industrial data rarely lives in one place. A plant may rely on a historian for process data, a relational database for asset or production information, a time series database for newer applications, and other systems for maintenance, quality, or operational context. Over time, that creates a data landscape made up of different technologies, schemas, and
TDengine IDMP Opens the Industrial Data Layer
by TDengine Team
October 7, 2026
A chemical plant engineer without a formal software development background used an AI Agent and the TDengine IDMP SDK to build and deploy an industrial-grade web application for the full alarm lifecycle in three days. The case shows how TDengine can serve as an industrial data foundation in the AI era: TDengine uses AI, and
How a Factory Built an Industrial Alarm Management App with TDengine in Three Days
by Arun Arulraj
September 28, 2026
