TDengine
AI-powered time-series database for Industrial IoT
Founded
2017
Starting Price
Free
Category
Monitoring & Observability
Last updated March 14, 2026
About TDengine
TDengine is a high-performance time-series database built for Industrial IoT, connected vehicles, and large-scale sensor data workloads. It combines a time-series database engine with built-in stream processing, caching, data subscription, and an AI copilot for anomaly detection and forecasting. Available as open-source, self-hosted enterprise, and fully managed TDengine Cloud on AWS, Azure, and GCP.
Pros & Cons
Pros
- Exceptional write throughput and query speed — benchmarks show up to 426x faster queries than InfluxDB
- All-in-one platform eliminates separate message queue, cache, stream processor, and TSDB
- Up to 12x storage savings through columnar compression optimized for time-series data
- Native SQL support minimizes learning curve for relational database teams
- Industrial-grade connectors for OPC-UA, PI System, and MQTT out of the box
Key Features
High-Performance Time-Series Ingestion
Writes up to 16x faster than InfluxDB with automatic batch processing and thread scheduling
Built-in Stream Processing
SQL-based windowed aggregation and real-time computation with millisecond latency — no Kafka or Flink needed
Built-in Caching
Latest-value cache per table eliminates the need for an external Redis layer
Built-in Data Subscription
Kafka-like pub/sub mechanism for real-time data streaming to downstream consumers
AI Copilot
Built-in anomaly detection and forecasting models for industrial time-series data
Industrial Data Modeling
Hierarchical asset modeling, tag management, and metadata support for OT/IT convergence
Native SQL Support
Standard SQL with time-series extensions for downsampling, interpolation, and gap-filling
Pricing
Open Source
Free
- Self-hosted (AGPL)
- Core TSDB engine
- SQL support
- Community support
Cloud Starter
\u0024975/month
Best For
Industrial IoT & Smart Manufacturing
Ingest and analyze sensor data from production lines, PLCs, and SCADA systems for predictive maintenance
Energy & Utilities Monitoring
Collect high-frequency readings from power meters and substations for anomaly detection and compliance
Connected Vehicles & Fleet Telematics
Store GPS, engine telemetry, and driver behavior data from large vehicle fleets
IT Infrastructure Monitoring
Replace InfluxDB/Prometheus stacks for DevOps metrics and high-cardinality observability data
