Timescale is now TigerData.

TL;DR: Eight years ago, we launched Timescale to bring time-series to PostgreSQL. Our mission was simple: help developers building time-series applications.

Since then, we have built a thriving business: 2,000 customers, mid 8-digit ARR (>100% growth year over year), $180 million raised from top investors.

We serve companies who are building real-time analytical products and large-scale AI workloads like: Mistral, HuggingFace, Nvidia, Toyota, Tesla, NASA, JP Morgan Chase, Schneider Electric, Palo Alto Networks, and Caterpillar. These are companies building developer tools, industrial dashboards, crypto exchanges, AI-native games, financial RAG applications, and more.

We’ve quietly evolved from a time-series database into the modern PostgreSQL for today’s and tomorrow’s computing, built for performance, scale, and the agentic future. So we’re changing our name: from Timescale to TigerData. Not to change who we are, but to reflect who we’ve become. TigerData is bold, fast, and built to power the next era of software.

Developers Thought We Were Crazy

When we started 8 years ago, SQL databases were “old fashioned.” NoSQL was the future. Hadoop, MongoDB, Cassandra, InfluxDB – these were the new, exciting NoSQL databases. PostgreSQL was old and boring.

That’s when we launched Timescale: a time-series database on PostgreSQL. Developers thought we were crazy. PostgreSQL didn’t scale. PostgreSQL wasn’t fast. Time-series needed a NoSQL database. Or so they said.

“While I appreciate PostgreSQL every day, am I the only one who thinks this is a rather bad idea?” – top HackerNews comment on our launch (link)

But we believed in PostgreSQL. We knew that boring could be awesome, especially with databases. And frankly, we were selfish: PostgreSQL was the only database that we wanted to use.

Today, PostgreSQL has won.

There are no more “SQL vs. NoSQL” debates. MongoDB, Cassandra, InfluxDB, and other NoSQL databases are seen as technical dead ends. Snowflake and Databricks are acquiring PostgreSQL companies. No one talks about Hadoop. The Lakehouse has won.

Today, agentic workloads are here.

Agents need a fast database. We see this in our customer base: private equity firms and hedge funds using agents to help understand market movements (“How did the market respond to Apple WWDC 2025?”); industrial equipment manufacturers building chat interfaces on top of internal manuals to help field technicians; developer platforms storing agentic interactions into history tables for greater transparency and trust; and so on.

What Started as a Heretical Idea Is Now a Thriving Business

We have also changed. We met in September 1997, during our first week at MIT. We soon became friends, roommates, even marathon training partners (Boston 1998).

TigerData (creators of TimescaleDB) cofounders — *While our hairlines and drinks (turmeric shots!) have changed, our enthusiasm has not*

That friendship became the foundation for an entrepreneurial journey that has surpassed even our boldest imaginations.

What started as a heretical idea is now a thriving business:

2,000 customers
Mid 8-digit ARR, growing >100% y/y
200 people in 25 countries
$180 million raised from top investors
60%+ gross margins

Cloud usage is up 5x in the last 18 months, based on paid customers alone.

Cloud usage is up 5x in the last 18 months

And that’s only the paid side of the story. Our open-source community is 10x-20x larger. (Based on telemetry, it’s 10x, but we estimate that at least half of all deployments have telemetry turned off.)

TimescaleDB is everywhere. It’s included in PostgreSQL offerings around the world: from Azure, Alibaba, and Huawei to Supabase, DigitalOcean, and Fly.io. You’ll also find it on Databricks Neon, Snowflake Crunchy Bridge, OVHCloud, Render, Vultr, Linode, Aiven, and more.

We Are TigerData

Today, we are more than a time-series database. We are powering developer tools, SaaS applications, AI-native games, financial RAG applications, and more. The majority of workloads on our Cloud product aren’t time-series. Companies are running entire applications on us. CTOs would say to us, “You keep talking about how you are the best time-series database, but I see you as the best PostgreSQL.”

So we are now “TigerData.” We offer the fastest PostgreSQL. Speed without sacrifice.

Our cloud offering is “Tiger Cloud.” Our logo stays the same: the tiger, looking forward, focused and fast. Some things do not change. Our open source time-series PostgreSQL extension remains TimescaleDB. Our vector extension is still pgvectorscale.

Why “Tiger”? The tiger has been our mascot since 2017, symbolizing the speed, power, and precision we strive for in our database. Over time, it’s become a core part of our culture: from weekly “Tiger Time” All Hands and monthly “State of the Tiger” business reviews, to welcoming new teammates as “tiger cubs” to the “jungle.” As we reflected on our products, performance, and community, we realized: we aren’t just Timescale. We’re Tiger. Today, we’re making that official.

This is not a reinvention: it’s a reflection of how we already serve our customers today.

Polymarket uses TigerData to track their price history. During the last election Polymarket ramped up 4x when trade volumes were extra high, to power over $3.7 billion dollars worth of trades.

Linktree uses TigerData for their premium analytics product, saving $17K per month on 12.6 TB from compression savings. They also compressed their time to launch, going from 2 weeks to 2 days for shipping analytical features.

Titan America uses TigerData’s compression and continuous aggregates to reduce costs and increase visibility into their facilities for manufacturing cement, ready-mixed concrete, and related materials.

Lucid Motors uses TigerData for real-time telemetry and autonomous driving analytics.

The Financial Times runs time-sensitive analytics and semantic search.

Tiger Is the Fastest Postgres for Modern Workloads

We are building the fastest Postgres: purpose-built for the modern operational workloads where traditional OLTP databases break down.

Operational workloads that go far beyond simple transactions are now the norm. They require real-time, user-facing analytics over massive high-cardinality datasets, from event streams to time-series to user-level behavioral data.

As the frontier moves further with agentic applications, the demands grow even more. These systems don’t just read and write: they observe, decide, and act. These AI applications require fast vector search across embeddings, and fast branching of data environments for experimentation and context-sensitive responses.

Tiger is not a fork. It’s not a wrapper. It is PostgreSQL, extended with innovations in the database engine and cloud infrastructure to deliver speed without sacrifice.

How are we so fast? Because of consistent, disciplined engineering efforts to serve customer needs over several years. Here is a non-exhaustive list:

Hypertables (2017)
Native columnar compression (2019)
Real-time materialized views for faster queries (2020)
Decoupled compute and storage (2021)
Tiered Storage to S3 Parquet (2022)
Vectorized query execution for fast analytics (2023)
Hybrid row-columnar store for faster queries on recent and historical data (2024)
Faster vector workloads on PostgreSQL via pgvectorscale (2024)
300x faster mutations (updates, upserts, deletes) to compressed columnar data (2024)
2500x faster distinct queries, 6x faster point queries on high-cardinality columns (2025)
Rapid horizontal scaling with load-balanced read replica sets (2025)
Enhanced high-performance storage up to 64 TB and 32,000 IOPS (2025)

Tiger brings together the familiarity and reliability of Postgres with the performance of purpose-built engines.

We built the fastest PostgreSQL. Not because we wanted to, but because our customers wanted us to.

Building the Modern PostgreSQL for the Analytical and Agentic Era

PostgreSQL has won. The Lakehouse has won. Every application is becoming an analytical application. Agents are here, in production, and need to be fast. The future is hybrid, developers and agents, with better latency and throughput needs.

In this era, modern applications must:

Handle terabytes and petabytes of data
Support real-time analytics
Integrate Gen AI features
Serve both humans and software agents, across dev, test, and production lifecycles
Meet sub-second latency and high concurrency expectations
Scale across operational databases and cost-efficient lakehouses
Maintain transactional integrity
Deliver all of this reliably and cost-effectively, because data volumes grow much faster than budgets

Our history to date, our time in this market, our lived experience watching all these changes unfold in real-time screams to us one thing: modern applications need a new kind of operational database.

One built for transactional, analytical, and agentic workloads. One that also acts as the operational serving layer for the Lakehouse. One built on Postgres.

That is what we are building.

And wow do we have some fun product announcements queued up for the upcoming weeks and months. A more agentic PostgreSQL. A deeper integration with the Lakehouse via Iceberg. A new compressed insert approach yielding 10 million rows per second. A new type of disaggregated storage architecture with zero-copy instant forks and replicas that we are deploying in our cloud for greater performance, as a replacement for EBS. And more.

We can’t wait to show it all to you. But first we had to clearly communicate who we are. We are TigerData.

Come Join Us

Tiger is the Fastest PostgreSQL. The operational database platform built for transactional, analytical, and agentic workloads. The only database platform that provides Speed without Sacrifice.

This is not a rebrand, but a recommitment to our customers, to our developers, and to our core mission.

If this mission resonates with you, come join us. Give us product feedback. Spread the word. Wear the swag. Join the team.

It’s Go Time. 🐯🚀

Speed Without Sacrifice: Building the Modern PostgreSQL for the Analytical and Agentic Era