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.
Complete Historian Capabilities TDengine vs. PI SystemExtreme 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%.
10×+ Performance Up to 90% Lower Storage CostAI-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.
Days → Minutes Expert-Level AnalyticsThe 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 ManaegementTDengine Overall Architecture
TDengine consists of two integrated components:
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. For teams weighing traditional data historians or IIoT platforms, TDengine is a modern alternative: it costs less and it covers more.
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
The TDengine Free Tier gives organizations of any size full functionality and performance—from time-series data management and industrial modeling to advanced analytics and AI, with no trial deadline SAN JOSE, Calif. — July 28, 2026 — TDengine, the AI-powered data platform for industrial applications, today announced the global availability of the TDengine Free Tier, making the complete
TDengine Makes Its Complete AI-Native Industrial Data Platform Free Forever for Up to 5,000 Tags
by TDengine Team
July 28, 2026
A chemical plant engineer without a formal software development background used an AI Agent and the TDengine Historian 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-Grade App on TDengine Historian in Three Days
by Arun Arulraj
July 10, 2026
A few years ago, when we first started introducing TDengine to industrial customers, we started hearing a pattern. More teams were looking for alternatives to PI System. The reasons varied: cost, performance, openness, cloud strategy, integration flexibility, or simply wanting more control over operational data. But the direction was clear. Many industrial companies were starting
Why a TSDB + Grafana Is Not Enough to Replace PI System
by Jim Fan
July 8, 2026
From a Single Question to a Root Cause Investigation For years, industrial AI has been associated with large projects. Build an AI agent. Connect it to your historian. Integrate alarms. Map your asset hierarchy. Connect dashboards. Configure analytics. Only then can you start asking questions. One thing I’ve enjoyed about the TDengine Historian is that none of
Built-In AI in the TDengine Historian: From Question to Root Cause
by Arun Arulraj
July 8, 2026
Key takeaways Industrial data analysis in Excel with the TDengine Historian Excel Add-In If you’ve used PI DataLink, you already understand TDengine EAI PI DataLink has been part of industrial Excel workflows for decades. For many process engineers, operations analysts, and maintenance teams, it is simply the way historian data gets into a spreadsheet: open
TDengine Historian Excel Add-In: Analyze Industrial Data in Excel
by Jim Fan
July 7, 2026
In shale gas production operations, automated data collection was already in place across gas wells, stations, compressors, and LNG/CNG facilities. But earlier SIS, BPCS, SCADA, and third-party vendor systems had been built independently, making it difficult to create a unified data view. That fragmentation limited the command center’s ability to support production situational awareness, emergency
TDengine TSDB in Shale Gas: From Data Silos to Unified Operations
by TDengine Team
July 2, 2026
In a typical factory scenario, 30 compressors report three Tags: temperature, pressure, and vibration. Over three months, that adds up to tens of millions of data points. The process director wants to know which operating range the compressors spend the most time in. Most monitoring panels rely on Line Charts and Bar Charts. They can
How TDengine Historian Heatmaps Reveal Equipment Operating Patterns
by Arun Arulraj
June 29, 2026
After many conversations with PI users globally, one pattern keeps coming up: the issue is not whether historian data exists, but how easily teams can use it somewhere else. Over the past year, my team and I have had many conversations with PI System users around the world. Utilities. Renewable energy operators. Manufacturers. Energy companies.
PI Works. Moving the Data is the Hard Part
by Jim Fan
June 27, 2026
For three decades, the Line Chart has dominated industrial equipment monitoring. Temperature, pressure, vibration, current. Whenever time-series data needs visualization, the default is a curve. But take motor winding temperature as an example. What operators actually care about is not the average. They want to know whether the temperature is climbing steadily or spiking suddenly,
How Candlestick Charts Reveal Industrial Data Fluctuations
by Arun Arulraj
June 26, 2026
