Skip to content

GreptimeDB vs. TDengine

TDengine is licensed under AGPL v3, which may impose source-disclosure obligations for network-accessible deployments. Its IoT-focused design has limited observability support — no native PromQL, no OpenTelemetry, no log or trace ingestion.

TDengine comparison at a glance.
See what changes operationally at scale.

TDengine is a time-series database designed for IoT and industrial data. Licensed under AGPL v3, which may impose source-disclosure obligations for network-accessible deployments. Features a "Super Table" concept for IoT device templates, but has no PromQL, no OpenTelemetry, and no log or trace ingestion.

CHALLENGER

TDengine

AGPL licensed · IoT-focused design · no PromQL or OTLP

VS

GREPTIMEDB

GreptimeDB

Apache 2.0 licensed, observability-native with PromQL + SQL

Feature comparison
Feature/AspectGreptimeDBTDengine
Data ModelMetrics, Logs & Traces in one databaseIndustrial IoT Time-Series Database
Value ModelMulti-Value (supports complex data structures)Multi-Value with Super Table templates
Multi-model SupportMetrics, Logs & Traces in one databaseTime-series data with IoT-specific optimizations
Ingestion ProtocolsSQL
gRPC
InfluxDB Line Protocol
Prometheus Remote Storage
OpenTelemetry
Loki Push API
Elasticsearch Bulk API
HTTP API
RESTful API
SQL INSERT
WebSocket
InfluxDB Line Protocol
OpenTSDB
Prometheus Remote Write/Read
StatsD, collectd
MQTT (Enterprise/Cloud)
OPC-UA (Enterprise/Cloud)
Industrial protocols
Query LanguagesSQL & PromQL (dual interface)Standard SQL with time-series functions
Data RetentionFlexible TTL policies with tiered storageConfigurable retention with automatic deletion
Continuous AggregationBuilt-in SQL aggregation, Pipeline ETL engine & Flow streaming computationStream processing with caching and data subscription
Use CasesUnified observability, real-time analytics, IoT monitoring, edge computingIndustrial IoT, smart manufacturing, connected vehicles, energy monitoring
ArchitectureCloud-native distributed with compute-storage disaggregationDistributed design with Super Table templates
Storage FormatApache Parquet (columnar, compressed)Native time-series optimized storage
Storage ScalabilityObject storage integration with unlimited capacityDistributed storage with automatic sharding
High AvailabilityNative clustering with automatic failoverMulti-replica deployment with data synchronization
LicenseApache 2.0AGPL v3.0 (core), Commercial (enterprise)
Deployment OptionsSingle-node, cluster, Kubernetes-native, edge-to-cloud with unified APIOn-premise, cloud, hybrid deployment with Kubernetes
Operational ComplexitySingle unified system with simplified Kubernetes operationsRequires understanding of Super Table concepts
Written LanguageRust (memory safety, performance)C (system-level performance)

Stay in the loop

Join our community