OtterTune is offering a private beta of the new OtterTune automatic database tuning service, providing users with a system backed by machine learning (ML) that automatically generates configuration settings to improve database performance and efficiency. The company also announced that it has received an initial seed funding round, led by Accel and others.
OtterTune's ML-based system automates tuning and continually adjusts the configuration to optimize the database for its specific workload. This helps customers reduce database costs by achieving better database efficiency and price-performance. It also frees up DBAs and developers to focus on more strategic work.
OtterTune represents a breakthrough in self-driving DBMS technology, based on R&D by Co-founders Andy Pavlo, Dana Van Aken, and Bohan Zhang at Carnegie Mellon University. They found that automatic configuration was an area of database optimization that no one to date had successfully tackled, according to the vendor. They began working on an ML-based tool to solve the problem.
“With hundreds of knobs that affect database performance, DBMSs now exceed the human DBA’s ability to optimize them,” said Andy Pavlo, CEO of OtterTune. “We’ve put years of research into solving this problem, which we know will lead to significant increases in efficiencies and cost savings for customers.”
To help launch the service and continue innovating autonomous DBMS technology, OtterTune has raised $2.5 million in seed funding from Accel and others.
“Databases are critically important but hard to operate at scale,” said Ping Li, Partner at Accel. “OtterTune’s founders are uniquely positioned to tackle this value proposition, given their thought-leading academic database research at CMU."
The OtterTune service works for both on-premises and cloud-based database deployments (PostgreSQL, MySQL, and Amazon RDS).
For more information about this news, visit https://ottertune.com/.