Tecton Reports Record Demand for Its Machine Learning Feature Platform as It Raises $100 Million in Funding

Tecton, the leading ML feature platform company, announced record demand for its platform and Feast, the most popular open source feature store:

• The company’s annual recurring revenue (ARR) nearly tripled from fiscal year 2021 to fiscal year 2022, and its annual ARR growth rate accelerated to more than 180% in the latest fiscal quarter that ended April 2022
• Its customer base increased more than 5 fold over the last 12 months. Customers span Fortune 500 across all major verticals as well as tech-forward innovators like Convoy, HelloFresh, Plaid and Tide
• The number of monthly active users for Feast increased more than 5 times annually to more than 800 monthly active users

“We believe that any company should be able to develop reliable operational ML applications and easily adopt real-time capabilities no matter the use case at hand or the engineering resources on staff. This new funding will help us further build and strengthen both Tecton’s feature platform for ML and the Feast open source feature store, enabling organizations of all sizes to build and deploy automated ML into live, customer-facing applications and business processes, quickly and at scale,” said Mike Del Balso, co-founder and CEO of Tecton.

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Tecton was founded by the creators of Uber’s Michelangelo platform to make world-class ML accessible to every company. Tecton is a fully-managed ML feature platform that orchestrates the complete lifecycle of features, from transformation to online serving, with enterprise-grade SLAs. The platform enables ML engineers and data scientists to automate the transformation of raw data, generate training data sets and serve features for online inference at scale. Whether organizations are building batch pipelines or already including real-time features in their ML initiatives, Tecton solves the many data and engineering hurdles that keep development times painfully high and, in many cases, preventing predictive applications from ever reaching production at all.

Tecton also announced that it has raised $100 million in Series C funding bringing the total raised to $160 million. This round was led by new investor Kleiner Perkins with participation from strategic investors Databricks and Snowflake Ventures, previous investors Andreessen Horowitz and Sequoia Capital and new investors Bain Capital Ventures and Tiger Global. Tecton plans to use the money to further deliver on customer value and to scale both engineering and go-to-market teams.

“We expect the software we use today to be highly personalized and intelligent. While ML makes this possible, it remains far from reality as the enabling infrastructure is prohibitively difficult to build for all but the most advanced companies,” said Bucky Moore, partner, Kleiner Perkins. “Tecton makes this infrastructure accessible to any team, enabling them to build ML apps faster. As this continues to accelerate their growth trajectory, we are proud to partner with Mike, Kevin and team to pioneer and lead this exciting new space.”

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“The investment in Tecton is a natural fit for Databricks Ventures as we look to extend the lakehouse ecosystem with best-in-class solutions and support companies that align with our mission to simplify and democratize data and AI,” said Andrew Ferguson, Head of Databricks Ventures. “We’re excited to deepen our partnership with the Tecton team and look forward to delivering continued innovation for our joint customers.”

“Together, Tecton and Snowflake enable data teams to securely and reliably store, process and manage the complete lifecycle of ML features for production in Snowflake, making it easier for users across data science, engineering and analyst teams to collaborate and work from a single source of data truth,” said Stefan Williams, VP Corporate Development and Snowflake Ventures at Snowflake. “This investment expands our partnership and is the latest example of Snowflake’s commitment to helping our customers effortlessly get the most value from their data.”

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