Timescale review: A data-backed look
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Timescale overview
Timescale is an open-source time-series database built on PostgreSQL, designed for developers and data teams managing time-stamped data at scale. It combines the reliability of Postgres with features purpose-built for time-series workloads, such as fast ingest, advanced compression, and flexible analytics.
Timescale is well-suited for IoT, observability, financial analytics, and real-time monitoring use cases, providing easier scaling and SQL compatibility compared to specialized NoSQL time-series databases.
How much do businesses spend on Timescale?
The chart below illustrates average spending on Timescale across different business sizes.
Mid-market and enterprise businesses demonstrate the highest spending on Timescale, with quarterly expenditure showing steady and accelerating growth throughout the measured period.
Small and medium-sized businesses maintain moderate spending with a consistent upward trajectory over time. Micro businesses show fluctuating spending patterns with initial stability followed by a decline in the final quarter.
Who is Timescale best for?
The chart below breaks down Timescale’s user base by industry and company size.
Micro businesses represent the largest portion of Timescale's user base, making up the dominant segment of platform adoption.
Small and medium-sized companies constitute a significant portion of users, while mid-market and enterprise businesses represent the smallest segment.
Timescale key features
Time-series data management
- What it does: Stores and manages large volumes of time-stamped data efficiently.
- Key benefit: Delivers high performance and scalability for time-series workloads.
PostgreSQL compatibility
- What it does: Extends standard PostgreSQL with time-series functions and optimizations.
- Key benefit: Allows developers to use familiar SQL tools and integrations.
Advanced compression
- What it does: Reduces storage costs with columnar compression optimized for time-series data.
- Key benefit: Enables cost-effective retention of high-frequency data.
Continuous aggregation
- What it does: Pre-computes and stores aggregated results for rapid querying.
- Key benefit: Accelerates analytics on large and granular datasets.
Automated data retention policies
- What it does: Manages data lifecycle with automatic data expiration and archiving.
- Key benefit: Simplifies compliance and storage management.
Timescale pricing
Plan | Price | Key features | Ideal for |
|---|---|---|---|
Free (Self-hosted) | $0/mo | Open-source, self-hosted deployment | Developers preferring self-hosting |
Performance | $30/mo | Solid performance, basic security, up to 4 services, S3 tiered storage | New apps and internal tools |
Scale | $36/mo | High performance, advanced security, high-availability, read replicas, point-in-time recovery | High-volume, production workloads |
Enterprise | Custom pricing | All Scale features, advanced security, 24/7 support, expert services | Large and enterprise-level compliance-focused organizations |
Timescale pros & cons
Timescale is a good fit if:
- Your team needs scalable storage for time-series and event data.
- Your team needs full SQL support with PostgreSQL compatibility.
- Your team needs fast ingest and real-time analytics.
- Your team needs a solution for IoT, monitoring, or financial data workloads.
Consider alternatives if:
- Your team needs a non-SQL native time-series database.
- Your team needs ultra-low-latency writes for massive sensor data.
- Your team needs a tool focused on visualization or dashboarding.
- Your team needs a simple deployment without cloud or managed options.