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    Timescale Cloud

    A reliable PostgreSQL cloud for your workloads

    Support Services

    Support options for your use case, infrastructure, and budget

    Workloads

    Time Series

    Lightning-fast ingest and querying on PostgreSQL

    Real-Time Analytics

    The fastest real-time analytics on PostgreSQL

    AI and Vector

    Build RAG, search, and Al agents, all on PostgreSQL

    Open-Source Extensions and Tools

    Time Series and Real-Time Analytics

    A reliable PostgreSQL cloud for your workloads

    AI and Vector

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    Security, reliability, and support for demanding businesses.

  • Documentation

    Timescale Docs

    Learn how to make PostgreSQL faster with our documentation

    AI and Vector

    Learn how to use PostgreSQL for Al with our documentation

    Learn PostgreSQL

    Learn PostgreSQL

    Learn the PostgreSQL basics and scale your database performance

    Timescale Benchmarks

    Timescale benchmarks

    See how Timescale performs against the competition

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The Fastest Real-Time Analytics in Postgres

Run real-time queries and updates with a row-columnar engine built for speed.

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The proof is in production.

This is real-life scale on a single Timescale service.

250T+

data points stored

1T+

metrics ingested daily

1PB+

of data volume

I’m using Timescale because it’s the same as PostgreSQL, but magically faster.

Florian Herrengt

Co-founder at Nocodelytics

Test Real-Time Analytics Performance Yourself

Specialized databases might give you speed and scale — but they struggle with joins, mutability, and ACID guarantees.

Timescale gives you it all: blazing fast real-time analytics, high scale, and full SQL — built on PostgreSQL and ready to power your applications.

That’s why we created : a benchmark for real-time analytics as it happens in real apps — not just simplified, denormalized workloads.

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What makes Timescale so fast?

Filter fast. Scan less. Execute in parallel.

Timescale filters out irrelevant data before the query even begins—eliminating time-based partitions and using metadata to skip over large blocks of data.

The rowstore handles high-ingest and point queries with speed, while the columnstore powers fast, parallel scans over compressed columnar data.

With vectorized execution and SIMD acceleration, even complex queries return in an instant.

real-time analytics hypertable

Get started with Timescale Cloud for free