OtterTune, taps uses AI to optimize databases, raises $12M

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Databases are widely used today, powering the applications people use every day for work and play. But building, configuring, and maintaining them can be tricky, especially as their use continues to grow. According to recent Redgate poll, 70% of companies now use more than one database in their stack, including on-premises and cloud databases. And most of the work remains manual, with only 51% saying they automate parts of the database deployment process.

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In search of an answer to these problems, Andy Pavlo co-founded OtterTune, a database optimization platform that today completed a $12 million Series A led by Intel Capital and Race Capital, with participation from Accel. Pavlo claims that OtterTune automates the process of optimizing database performance by using artificial intelligence to analyze and fine-tune parameters to run databases more efficiently at a lower cost.

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“Databases are the most important component of every application stack. It doesn’t matter if you’re building a trendy Web3 app or a more traditional online storefront. After all, you need a database,” Pavel told TechCrunch in an email interview. “But these systems have many facets… Open source databases such as PostgreSQL and MySQL getting better every year, but more features mean deployment issues. As organizations continue to migrate more and more databases to the cloud, they are investing in vendor tools to solve problems, but this can lead to diminishing returns.”

Pavlo says he was inspired to launch OtterTune after he became a professor at Carnegie Mellon University (CMU) in 2013. insisted on presenting research on automated methods for speeding up transaction processing in databases. In conversations with executives, he was surprised at how much the company paid administrators for what he considered to be basic database maintenance tasks.

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“DBAs almost brag about how much they get paid for doing trivial things to keep databases up and running,” Pavlo said. “It was… just at a time when powerful machine learning technologies became more accessible thanks to open source platforms and hardware acceleration. So I decided to consider applying machine learning techniques to try and get rid of the labor-intensive aspects of database systems in order to free up people to do more important tasks in their own time.”

Together with two of his students (now co-founders), Dana Van Aken and Bohan Zhang, Pavel created OtterTune from CMU in 2020, initially with the goal of commercializing a tool for optimizing database handle configurations. (In databases, “knobs” are configuration settings that control certain aspects of run-time behavior, such as caching policies.) Van Aken led the design and development of the prototype, which received an Amazon grant as a student, and Bohan soon joined him. . after graduating from CMU.

Database management automation is not an incredibly original idea. There are at least half a dozen vendors competing with OtterTune, including Akamas, Unravel Data, Pepper Data, EverSQL, Turbonomic, Opsani, Cloudhealth and Vantage. (Microsoft, IBM, and Oracle use their own flavors of stand-alone databases to keep up.) But Pavel argues that OtterTune is more developer-friendly than many products on the market, while supporting a wider variety of database types.

OtterTune uses algorithms to “understand” what higher performance means for a particular cloud or local database, considering spikes in workload during the week – for example, weekdays versus weekends. The platform checks to identify periods of peak database workload and provides “health checks” that alert OtterTune customers when databases are at risk of performance degradation.

“OtterTune machine learning algorithms make all decisions based on system metrics such as resource usage and I/O usage… [They] identify database issues such as cache misses and missing indexes that can cause unexpected problems,” explained Pavel. “One of the issues we’ve realized is that customers know there’s something wrong with their PostgreSQL or MySQL database, but they don’t know what’s causing it. Databases are so complex and people are too busy to understand what’s going on inside.”

This is just the beginning for OtterTune, but last year launched an “academic” version of the technology with support for Oracle databases. While refusing to disclose revenue figures, Pavel said the platform now has active users from “more than 100” organizations.

Capital from the latest round of funding, which raised OtterTune’s total to $14.5 million, will jump-start the development of advanced health checks, including database table-level health checks, Pavlo said. This will also focus on recruiting and hiring employees, increasing the size of the company’s team from 15 to 30 by 2023.

“Handle tuning is important and significant to many customers, but it’s only one aspect of the database lifecycle,” he said. “Just like people turn to Amazon to manage the physical hardware under their databases, OtterTune will provide automated features for the database. By observing the workload and behavior of many databases, OtterTune’s machine learning algorithms automatically ensure that any new database will run with the correct configuration, replication schemas, indexes, and query plans.”

When we reached out for comment, Intel Capital Senior Managing Director Nick Washburn said in a statement, “Effective database management is critical to the success of a technology-driven business. OtterTune is working to revolutionize this process by using machine learning to automate a time-consuming and obsolete operation. The visionary mission of the founders of OtterTune is backed by the research they conducted at CMU and their proven ability to help customers improve performance, reduce costs, and ultimately ensure the reliability of their databases.”

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