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Home/Categories/Databases & Storage/TDengine
icon of TDengine

TDengine

Open source alternative to InfluxDB Cloud, Timescale Cloud and Datadog

A high-performance, scalable time-series database designed for Industrial IoT, offering efficient data handling and AI integration.

24.9K starsCAGPL-3.0Active this month
Visit websiteGitHub repo
image of TDengine
Contents
  1. 01Who TDengine is for
  2. 02The problem it solves
  3. 03How it solves it
  4. 04Strengths and trade-offs
  5. 05TDengine vs alternatives
  6. 06Tech stack
  7. 07FAQ
  8. 08Similar open-source tools
TL;DR

TDengine is an AGPL-3.0 time-series database built for industrial IoT, telemetry, and high-volume sensor workloads. It replaces managed time-series stacks such as InfluxDB Cloud or Timescale Cloud when teams need local control, SQL access, and ingestion paths for operational data. It fits industrial teams more than general app developers looking for a simple relational database.AGPL-3.0 · C · 24.9K stars · Active this month

who it's for

Who TDengine is for#

Industrial teams storing sensor telemetry

TDengine fits plants, device fleets, and monitoring systems that ingest large volumes of time-stamped measurements.

Skip if:

Your data is mostly business transactions or document records; PostgreSQL or another general database is a better default.

Edge operators with data residency constraints

Self-managed deployments help keep operational metrics close to machines and local networks.

Skip if:

You prefer a fully managed SaaS time-series database and do not have local data-control requirements.

the problem
tech stack · detected from GitHub

What it's built on#

Languages
CC++Go
frequently asked

FAQ#

What is TDengine used for?
Is TDengine open source?
Is TDengine a PostgreSQL alternative?
also worth a look

Similar open-source tools#

QuestDB

QuestDB

Ingest high-frequency data and query it in real time with SQL

16.9KJavaApache-2.0

Repository

Stars
24.9K
Forks
5K
License
AGPL-3.0
Latest
ver-3.4.1.6
Last commit
21 days ago
Last verified
May 13, 2026
Repo
taosdata/tdengine ↗

Additional details

Language
C
Open issues
498
Contributors
308
First release
2019

Categories

Databases & StorageData & AnalyticsIT Management

Tags

DatabaseIoTMonitoringCloud NativeObservabilityDeveloper Tools

The problem it solves#

how TDengine solves it

How it solves it#

Time-series database engine

TDengine centers ingestion, storage, and query behavior around timestamped metrics rather than treating telemetry as ordinary relational rows.

Industrial IoT positioning

The project documents use cases around sensors, connected devices, and operational monitoring, which makes it more specialized than a generic analytics database.

SQL-oriented access

Teams can query time-series data through SQL-style interfaces instead of forcing every analyst into a custom telemetry query language.

Edge and cloud deployment paths

TDengine supports self-managed deployments for teams that need data close to devices, with commercial cloud options available separately.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Purpose-built for telemetry volumeTDengine focuses on high-ingest time-series workloads, which is a clearer fit for sensor fleets than bolting telemetry tables onto a general OLTP database.
  • Open source core for data controlThe AGPL-3.0 repository gives infrastructure teams a self-managed path when operational data cannot leave a site or region.

Trade-offs

  • -Specialized operational surfaceTDengine is not a general PostgreSQL replacement. Teams need time-series expertise and must validate connector support, retention design, and backup procedures for their environment.
  • -AGPL-3.0 obligationsOrganizations modifying and offering the software over a network should review AGPL requirements before embedding TDengine into a commercial service.
versus alternatives

TDengine vs alternatives#

Coroot

Coroot

Instant observability with no-code setup.

7.6KGoApache-2.0
RuView

RuView

Intelligent AI agents for real-world applications

59.5KRustMIT
Sentry

Sentry

Real-time error tracking with performance monitoring and traces

2.2KPythonMIT
ClawTrace

ClawTrace

Visualize agent execution trees and track token costs per step

35TypeScriptApache-2.0
Uptime Kuma

Uptime Kuma

Track uptime for websites and APIs with 90+ alert integrations

86.7KJavaScriptMIT

TDengine is used for time-series data, especially industrial IoT telemetry, sensor measurements, device monitoring, and operational analytics.

Yes. TDengine is available under the AGPL-3.0 license, with commercial offerings available from TDengine's vendor.

Industrial telemetry creates data patterns that general databases handle poorly: millions of time-stamped points, many devices, retention rules, downsampling, and queries over recent windows. Teams often end up stitching together brokers, ETL jobs, and analytics databases before they can answer basic operational questions.

Managed time-series services reduce operations work, but they can introduce data residency concerns and recurring cost surprises when sensor volume grows. Plants, utilities, and edge deployments often need time-series storage close to the equipment, not only in a cloud account.

TDengine vs InfluxDB Cloud

TDengine and InfluxDB both target time-series data, but TDengine is especially positioned around industrial IoT and self-managed operational deployments.

CriteriaTDengineInfluxDB Cloud
LicenseAGPL-3.0 repositoryProprietary managed service plus open source components
Self-hostingYesManaged cloud focus
Primary fitIndustrial telemetry and device dataBroad metrics, monitoring, and time-series analytics

TDengine is stronger when industrial deployment control, edge placement, and SQL-style telemetry access matter. InfluxDB Cloud is still worth considering when the team wants a managed service and broad ecosystem support over running database infrastructure.

TDengine is not a general relational database replacement. It is a specialized time-series database for timestamped telemetry workloads.