TimescaleDB
PostgreSQL++ for time-series data, analytics, and AI workloads
Founded
2015
Starting Price
0
Category
Developer Tools
Last updated March 12, 2026
About TimescaleDB
TimescaleDB is an open-source time-series database built as a PostgreSQL extension, enabling teams to store, query, and analyze time-series data using standard SQL. It delivers automatic partitioning, continuous aggregates, and up to 97% compression while maintaining full PostgreSQL compatibility for tools, extensions, and existing workflows.
Pros & Cons
Pros
- Full PostgreSQL compatibility means zero learning curve for teams already using Postgres — all existing tools, extensions, and ORMs work seamlessly
- Industry-leading 94-97% compression dramatically reduces storage costs for large time-series datasets
- Continuous aggregates enable real-time dashboards without expensive re-scans of raw data
- Generous free self-hosted option with no feature restrictions on the open-source extension
- Tiered storage automatically optimizes cost by moving cold data to cheap object storage
Key Features
Hypertables & Automatic Partitioning
Automatically partitions time-series data into chunks for optimal query performance, without manual table management or schema changes
Continuous Aggregates
Materializes and incrementally updates aggregate queries in the background, enabling fast real-time dashboards without re-scanning raw data
Advanced Compression
Achieves 94-97% compression rates using column-oriented best-in-class algorithms, dramatically reducing storage costs for historical data
Tiered Storage
Automatically moves older data to low-cost bottomless object storage while keeping recent data on high-performance SSDs, optimizing cost and speed
Full SQL Compatibility
Supports standard PostgreSQL SQL including JOINs, CTEs, window functions, and subqueries — no proprietary query language to learn
Horizontal Scaling
Scales writes to millions of data points per second per node and can horizontally scale to petabytes across distributed clusters
Real-Time Analytics
Pricing
Community (Self-Hosted)
0/month
- Open-source PostgreSQL extension
- Hypertables & automatic partitioning
- Continuous aggregates
- Compression
- Community support
Best For
IoT & Sensor Data Management
Ingest and analyze millions of sensor readings per second from industrial equipment, smart devices, and connected infrastructure with automatic partitioning and compression
Infrastructure Monitoring & Observability
Store and query server metrics, application logs, and network telemetry data with real-time continuous aggregates powering operational dashboards
Financial Market Data Analysis
Capture and analyze high-frequency trading data, stock prices, and transaction histories using SQL JOINs with relational reference data
Energy & Utilities Metering
Track energy consumption, grid performance, and utility usage patterns at scale with cost-efficient tiered storage for historical data
Similar Tools
Featured In
#1
7 Best Time Series Databases for IoT & Monitoring (2026)
Best overall choice for teams that value PostgreSQL compatibility, mixed workload flexibility, and the ability to JOIN time series data with relational business data in a single database.
#2
Best Vector Search Integrations for Existing PostgreSQL Databases (2026)
Best for teams scaling past 10M vectors or running hybrid time-series + embeddings workloads where pgvector alone runs out of headroom.
