Startups > Timescale

Timescale

TimescaleDB helps engineering teams ingest and query massive time-series datasets fast.

Series C $110M+ total Founded 2015 New York, New York

TimescaleDB is an open-source PostgreSQL extension optimized for time-series data, delivering 10-100x faster query performance than MongoDB and InfluxDB while supporting millions of events per second with up to 95% storage compression. Built by Ajay Kulkarni and Michael Freedman (Princeton professor), it serves companies ingesting and analyzing massive volumes of time-stamped data across IoT, blockchain, maritime, and industrial applications. The product maintains full PostgreSQL compatibility—same drivers, clients, and SQL—while adding automatic time-partitioning (hypertables) and columnar compression.

Problem solved

Organizations struggle to ingest and query millions of time-series events per second efficiently without custom partitioning logic, slow query performance, and massive storage overhead.

Target customer

Engineering teams at infrastructure, fintech, IoT, blockchain, and industrial companies managing high-volume time-series workloads; developers needing PostgreSQL-native solutions for analytics and monitoring.

Website LinkedIn Crunchbase

Founders

Ajay Kulkarni

CEO & Co-Founder

Previously in product at GroupMe; founded Timescale to solve IoT database challenges.

Michael J. Freedman

CTO & Co-Founder

Robert E. Kahn Associate Professor of Computer Science at Princeton University; co-founder of Illuminics Systems.

Funding history

Series A & B $16M 2016-2017 Led by Unknown · Unknown

Series C $110M 2022-02-22 Led by Icon Ventures · Unknown

Total raised: $110M+

Industries

Information Services Database Software Computer

Pricing

Open-source and free; TimescaleDB Enterprise includes HA, backups, monitoring, admin console, and cloud sync with tiered support. Detailed pricing not publicly available; contact sales for custom quotes.

Notable customers

Cloudflare, Indexing, Conserv, Sentinel Marine Solutions, Bonito Tech

Integrations

Inductive Automation (strategic alliance), PopSQL (acquired April 2024), PostgreSQL ecosystem tools

Website

timescale.com/

Competitors

InfluxDB

Purpose-built time-series database but lacks PostgreSQL compatibility and SQL dialect familiarity.

MongoDB

Document database with slower time-series query performance and no native time-partitioning optimization.

Microsoft SQL Server / Azure SQL

Enterprise relational databases without time-series-specific optimizations like automatic hypertables and columnar compression.

CrateDB

Distributed SQL database for time-series but not a PostgreSQL extension, requiring separate infrastructure.

Kinetica

GPU-accelerated analytics platform with higher complexity and different architectural approach than PostgreSQL-native extension.

Why this matters: Timescale (rebranded TigerData in June 2025) has quietly become the de facto open-source time-series database for companies that can't stomach vendor lock-in or learning new SQL dialects. With $110M in funding, 16k GitHub stars, 500+ paying customers, and 10x performance gains over alternatives, it's bridging the gap between PostgreSQL's familiarity and specialized time-series demands—making it increasingly critical infrastructure for AI/ML, IoT, and blockchain workloads.

Best for: DevOps, data engineers, and architects at scale-ups and enterprises building real-time monitoring, analytics, or IoT platforms who need PostgreSQL-compatible time-series performance without learning new SQL dialects or managing separate infrastructure.

Use cases

Blockchain Data Indexing

Indexing uses TimescaleDB to ingest and query blockchain transaction data in real-time for Web3 businesses. The database handles millions of time-stamped events per second, enabling fast analytics on blockchain metrics and trends.

Environmental Monitoring & Preservation

Conserv tracks temperature, humidity, and light data continuously across museums and archives using TimescaleDB. The compressed storage reduces infrastructure costs while time-partitioning ensures fast queries on historical environmental conditions.

Maritime Fleet Analytics

Sentinel Marine Solutions powers real-time boat system monitoring with TimescaleDB, ingesting sensor data from fleets. Fast query performance enables immediate anomaly detection and historical trend analysis for maintenance planning.

Public Transportation Tracking

Bonito Tech's Sakai platform uses TimescaleDB for real-time analytics on vehicle GPS and telemetry data across transit networks. Automatic compression and partitioning reduce storage costs while query speed enables live dashboards.

Alternatives

InfluxDB Purpose-built time-series database with its own query language (InfluxQL/Flux) rather than PostgreSQL SQL; use if you need a dedicated, language-agnostic platform.

Prometheus Lightweight metrics database with pull-based ingestion; use for simpler monitoring scenarios with smaller data volumes.

Amazon DynamoDB / TimeStream Fully managed AWS services requiring vendor lock-in; use if you want zero operational overhead and are already in AWS ecosystem.

QuestDB High-performance time-series database with custom SQL dialect and C++ engine; use if raw ingestion speed is paramount and PostgreSQL compatibility is not required.

FAQ

What does TimescaleDB do? +

TimescaleDB is a PostgreSQL extension that optimizes ingestion and querying of time-series data at massive scale. It automatically partitions data by time intervals (hypertables), compresses storage by up to 95%, and delivers 10-100x faster query performance than MongoDB or InfluxDB—all while maintaining full PostgreSQL compatibility.

How much does TimescaleDB cost? +

TimescaleDB open-source is free. TimescaleDB Enterprise (with high availability, backups, monitoring, admin console, and cloud sync) is available with tiered support. Pricing is not publicly listed; contact Timescale sales for custom quotes based on your deployment and support needs.

What are alternatives to TimescaleDB? +

InfluxDB (purpose-built time-series with InfluxQL), Prometheus (lightweight metrics), Amazon TimeStream (fully managed AWS), QuestDB (C++ high-performance), and MongoDB (document database with slower time-series performance).

Who uses TimescaleDB? +

Cloudflare, Indexing (blockchain), Conserv (environmental monitoring), Sentinel Marine (maritime), and Bonito Tech (transportation). Target customers include infrastructure companies, fintech firms, IoT platforms, blockchain projects, and industrial automation businesses managing high-volume time-series workloads.

How does TimescaleDB compare to InfluxDB? +

TimescaleDB is a PostgreSQL extension maintaining SQL compatibility, while InfluxDB is purpose-built with its own query language. TimescaleDB offers better compression (95% vs. standard), faster complex queries due to full SQL/index support, and PostgreSQL ecosystem integration. InfluxDB is simpler for basic metrics but less flexible for advanced analytics.