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TDengine

About TDengine

TDengine provides a full-stack software platform for industrial data covering collection, storage, real-time analysis, visualization, event management, and root cause analysis. The platform is built on a time series database, an industrial ontology, and an industrial agent runtime. It is positioned as a modern alternative to traditional data historians. TDengine connects to industrial data sources such as OPC, MQTT, and Kafka and includes built-in ETL for cleaning and transformation. The company describes itself as an industrial AI infrastructure company. TDengine can be deployed in the public cloud, on premises, or in edge-cloud deployments.

Products

  • TDengine TSDB: A time-series database with built-in data ingestion, storage, and query processing engines.
  • TDengine IDMP: An industrial data management platform that includes an industrial ontology, atomic applications, and the Industrial Agent Runtime.
  • TDengine TSDB-OSS: A fully open-source time-series database with clustering capabilities that serves as the foundation for the paid offerings.
  • TDengine Cloud: A fully managed cloud service delivered on AWS, Microsoft Azure, or Google Cloud Platform.

Used for

  • Performing root cause analysis using built-in AI assistants.
  • Migrating from a PI System data historian setup.

Who it is for

  • Used by small and medium-sized industrial enterprises, offered free of charge.
  • Used by automation teams through the CLI and by developers through a REST API.

From TDengine's own website, read 2026-10-24. How we build listings.

How to evaluate Data Warehouse & Lakehouse

This is how the CIOPages Research Team evaluates this category. It is not an assessment of TDengine. The category covers two kinds of product, so both frameworks are here. Most buyers need one of them.

Cloud Data Warehouse
25%
Query Performance & Concurrency
TPC-DS benchmarks, concurrent user support, query queuing, automatic scaling, sub-second response for dashboards
20%
Data Ingestion & Integration
Streaming ingestion (Kafka, Kinesis), batch loading, CDC support, native connectors, data sharing/marketplace
20%
Governance & Security
Column/row-level security, dynamic data masking, data lineage, access policies, audit logging, compliance certifications
15%
AI/ML Integration
Native ML runtimes (Python/Spark), feature store, vector search, LLM integration, model serving
10%
Cost Management
Compute/storage separation, auto-suspend, resource monitors, usage attribution, reserved capacity pricing
10%
Ecosystem & Tooling
BI tool compatibility, dbt/Airflow integration, Iceberg/Delta support, partner ecosystem, marketplace
Data Lakehouse Platforms
25%
Open Format & Catalog Interoperability
Native read AND write to Apache Iceberg and/or Delta Lake, Iceberg REST Catalog support, credential vending for external engines, cross-format bridges (Delta UniForm, Apache XTable), and whether grants and lineage travel with the table when another engine reads it
20%
Query Engine Performance & Concurrency
Vectorized execution (Photon, Arrow/Gandiva, native engines), caching and materialization (reflections, result cache, Warp Speed), high-concurrency BI behavior, autoscaling, and predictable performance on your own data — not vendor benchmarks
20%
Data Engineering, Streaming & AI/ML
Batch and streaming ingestion into open tables, incremental/CDC and upsert support, orchestration, ML lifecycle (feature store, training, model serving), notebook and Python/Spark depth, and native LLM/agentic and vector capabilities
15%
Governance, Security & Lineage
A unified catalog spanning tables, files, ML models and (increasingly) unstructured data; fine-grained RBAC/ABAC, row/column masking, data sharing, automated lineage, and consistent policy enforcement across every engine that touches the data
10%
Operational Simplicity & Table Maintenance
Automated compaction, clustering, snapshot expiry and orphan-file cleanup; serverless vs. cluster sizing; multi-cloud and hybrid/on-prem reach; admin and FinOps tooling; and how much table toil the team must own versus the platform absorbing it
10%
Cost Model & Consumption Control
Consumption unit (DBU, credit, capacity unit, bytes/slots), separation of storage and compute, idle-suspend and autoscaling guardrails, egress and cross-region exposure, workload isolation, and the FinOps tooling to attribute and cap spend

CIOPages put this listing together from public sources. It’s information, not an endorsement. How we build listings. Work here? Claim this listing or .

Quick Facts

tdengine.com Pricing page
CategoryData & Analytics
SubcategoryData Warehouse & Lakehouse

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