Timescale just raised $110 million in our Series C, led by Tiger Global alongside all existing investors: Benchmark, New Enterprise Associates, Redpoint Ventures, Icon Ventures, and Two Sigma Ventures. With this funding, Timescale is now valued at over $1 billion, and combined with earlier rounds (2018, 2019, 2021), has raised over $180 million to fuel its growth. In the past two years, Timescale has seen 7x community growth and 20x revenue growth, with over 500 paying customers and tens of thousands of other organizations using TimescaleDB in the community today. (And yep, the Timescale Tigers raised money from Tiger Global during the Year of the Tiger 🐯🐯🐯🦄🚀💥. We don’t believe in coincidences 😉.)
All data is time-series data.
This was our thesis when we launched Timescale five years ago.
We saw that time-series data was becoming ubiquitous, in part because of major trends like the rise of IoT/machine data, IT observability, and, more recently, web3/crypto applications. But also because we saw businesses in every industry starting to collect time-series data to build exceptional data-driven products and product experiences.
This might be measuring the temperature and humidity of soil to help farmers combat climate change. Or tracking streaming metrics to identify popular music and artists. Or analyzing NFT activity in real time. Or building new gaming experiences. Or observing every action that a user takes in a software application and the performance of the infrastructure underlying that application to help resolve support issues and increase customer happiness.
As computing continues to get more powerful and storage gets even cheaper, developers are able to collect data at higher fidelities than before, enabling them to build radically better data-driven product experiences and businesses. Time series—tracking trends, not static data points—represents data at the highest fidelity.
Five years ago, we also saw that time-series data is relentless, creating performance and scaling challenges that traditional databases were not built to handle.
That’s when we realized that what these new time-series applications needed was a new kind of database, something built for scale and performance but also something that offered the reliability and versatility only found in relational databases.
This thesis also included two other core beliefs: PostgreSQL is the best foundation for software applications, and SQL is and will continue to be, the universal language for data analysis.
That is why we built TimescaleDB, the first-ever relational database for time-series.
Welcoming Tiger Global to the Team
There are many successful data platforms today, like Snowflake (for data analysts) and Databricks (for data scientists).
Our vision is to build a data platform for developers anchored around a best-in-class developer experience for PostgreSQL, time-series data, analytics, and data-driven applications.
With this new round of financing, our second financing in the last 12 months, we are accelerating toward this vision: investing in product, engineering, and R&D, serving our community, customers, and developers worldwide, and growing our amazing team.
We’re also using this financing as an opportunity to give back to the larger developer community. For example, while we’ve employed PostgreSQL contributors for a few years now, with this financing, we are building an entire team dedicated to upstream PostgreSQL contributions.
And, with this financing, we are adding another great partner to the team, Tiger Global:
"Timescale's laser focus on driving community and consumption around its time-series database is what stood out to us. As time-series data becomes even more pervasive, Timescale is playing a pivotal role in how companies work with data," said John Curtius, Partner at Tiger Global. "They're a team that is committed to, and that we believe will, build the next great database company."
They’re joining our existing investors Benchmark, New Enterprise Associates, Redpoint Ventures, Icon Ventures, and Two Sigma Ventures:
"We partnered with Timescale over four years ago because we saw that all data was fundamentally time-series data and that Timescale was uniquely poised to alleviate the pain felt by developers. Since then, we have seen the team make true on their promise and witnessed their unwavering devotion to serving developers globally," said Peter Fenton, General Partner at Benchmark. "Sitting at the intersection of three transformative trends—time-series, PostgreSQL, and SQL—Timescale is building a foundational database company that is quickly becoming an indispensable part of the software application stack."
The large and rapidly growing TimescaleDB developer community
Timescale usage by our customers and community continues to grow exponentially every year, including by companies like Apple, Akamai, Bosch, Cisco WebEx, Comcast, DigitalOcean, Disney, Electronic Arts, GE, IBM, Marvel Studios, Microsoft, Nutanix, NYSE, Pfizer, Samsung, Schneider Electric, Siemens, Tesla, Uber, Walmart, and tens of thousands of others:
That’s the quantitative data. But there’s also the qualitative perspective on how developers use and view TimescaleDB today:
A cross-section of what developers say about TimescaleDB.
TimescaleDB is engineered on top of PostgreSQL, which means that TimescaleDB offers everything that PostgreSQL offers, plus additional capabilities for time-series workloads
TimescaleDB is the first-ever relational database for time series.
A database purpose-built for time-series data, engineered on top of PostgreSQL (packaged as a PostgreSQL extension), with full SQL support. 100% free, open source (“open core”, to be precise), and petabyte-scale. A database where you can store time-series data alongside ordinary data and get the best of both worlds.
We are big fans of PostgreSQL. Even with its long history, today PostgreSQL is still one of the fastest-growing databases in terms of usage and community size. Its popularity is largely due to the hard work and dedication of the PostgreSQL core developer community toward building a reliable and versatile database.
One of the many great features of PostgreSQL is that it is designed to be extensible. These “PostgreSQL extensions” add extra functionality without slowing down or adding complexity to core development.
While many PostgreSQL extensions only add new functions or datatypes, we’ve leveraged this framework extensively to build a radically better database for time-series workloads while preserving all the goodness of PostgreSQL. (Notably, this means that TimescaleDB is not a fork but an extension of PostgreSQL —so it stays aligned with core/mainline PostgreSQL.)
Some of the groundbreaking capabilities that we’ve added over the past five years include hypertables (the illusion of a single table across all space and time, despite 2D chunking), columnar compression in a row-oriented database (90%+ compression using best-in-class compression algorithms), continuous aggregates and real-time aggregation (real-time, incremental materialized views), hyperfunctions (new SQL functions to simplify working with data in PostgreSQL), function pipelines (functional programming in PostgreSQL using custom operators), and more.
And we’ve done this while maintaining all of the goodness of PostgreSQL and the PostgreSQL ecosystem:
All of the flexibility and power of SQL (yes, full SQL, not “SQL-like” or “SQLish”)
All of the PostgreSQL compatible language and ORM connectors (Python, JavaScript, Ruby, Go, R, Django, Node.js, and so many more)
Geospatial support via PostGIS and other domain-specific capabilities from the breadth of PostgreSQL extensions
All of the management and administration tools already available for PostgreSQL (e.g., backup/restore, high-availability, physical replication)
All of the SQL compatible visualization tools and connectors like Tableau, Looker, Grafana, PowerBI, Retool, Metabase, and more
As a result, we’ve built not just a better PostgreSQL for time-series but also a best-in-class product that outperforms other databases like MongoDB, AWS Timestream, Clickhouse, InfluxDB, and others for time-series workloads. This is because TimescaleDB, unlike general purpose databases, is purpose-built for time-series; and also because TimescaleDB, unlike other time-series databases, is not just a time-series database but also a relational database, i.e., a PostgreSQL database, all-in-one.
In fact, we’ve done a lot more over the past five years:
We also built a remote-first culture and globally distributed, diverse team of 100+ amazing individuals across 20+ countries and six continents. (And we’re still hiring!)
And thanks to all of these efforts, built a business that has seen 7x community growth and 20x revenue growth in just the last two years, with over 500 paying customers and tens of thousands of other organizations using TimescaleDB in our community today.
“Timescale has built a brilliant abstraction layer on top of PostgreSQL that lets us treat our gigantic / arbitrarily large data sets like they're in one table. At Messari, we have time-series data coming in by the tens or hundreds of thousands of rows per minute, 24/7/365. Timescale lets us manage this gigantic throughput in what initially appears to be simple, singular SQL tables. Because it's Postgres, there are no surprises in terms of how to query the data later, and it's a lot easier to build new functionality on top of our core database layer than with similar products. - Adam Inoue, Software Engineer, Messari
“TimescaleDB has absolutely changed my perspective on how relational databases can perform so well in ingesting and querying time series data. We were using a SQL database and decided to give a try with TimescaleDB and it did not disappoint. The community on Slack is extremely helpful. Keep up the good work TimescaleDB!” - Esther Toh, Senior Lead Software Engineer, Bridgestone Asia Pacific
“TimescaleDB is one of those rare pieces of technology that is both extremely capable yet easy to learn. The more familiar you become with TimescaleDB, the more you'll see what it is capable of. Not to mention, the TimescaleDB docs are some of the most thorough and well-organizedan docs you'll find for any programming tool.” - Alex Koutmos, Senior Software Engineer, Whoosh.io
Timescale Tomorrow: Building a Better PostgreSQL
We are grateful for the opportunity to serve this growing community and developers worldwide. We’ve made a lot of progress in the past five years, but, of course, we’re just getting started.
Every company today is either a software company, becoming a software company, or getting replaced by a software company. Developers are the vanguard of this transformation.
What these developers need isn’t just a better time-series database but a better PostgreSQL for their workloads.
We started off building a “PostgreSQL for time series.” But to our community, we are also: “PostgreSQL for IoT,” “PostgreSQL for web3”, “PostgreSQL for analytics,” “PostgreSQL for observability,” “PostgreSQL for gaming,” “PostgreSQL for events,” and more.
Looking ahead, our goal is to keep innovating on top of PostgreSQL and to continue adding breakthrough capabilities that enable more developers to build exceptional data-driven applications.
To name a few engineering efforts currently underway and slated for release this year:
An enhanced cloud-native PostgreSQL experience, including at scale with cost-effective serverless storage
All-around performance improvements and improved developer ergonomics for analyzing data in PostgreSQL
An enhanced developer console and cloud APIs to better manage and understand your fleet of databases
And much, much more.
Come join us!
To all our users, we thank you for your support and feedback and for building alongside us. To everyone who is not yet a user, we invite you to try Timescale for free today.
Once you are using TimescaleDB, please join the TimescaleDB community in Slack or in our new forums and ask any questions you may have about time-series data, databases, and more.
And, for those who share our mission and values and want to join our fully remote, global team, we’re hiring!
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