OtterTune Raises $12 Million Series A Funding to Revolutionize Cloud Database Operations, Led by Intel Capital and Race Capital

The funding will enable the expansion of the company’s automatic database tuning service to new cloud platforms and databases

PITTSBURGH — May 10, 2022 — OtterTune, the ultimate database automation and optimization platform, has raised $12 million in Series A funding led by Intel Capital and Race Capital with participation from Accel. Nick Washburn, Senior Managing Director of Intel Capital, will help accelerate the company’s growth by joining its board of directors.

Database management is a multi-billion dollar market and systems have hundreds of configuration settings that affect performance and cloud costs. OtterTune automates the complex and time-consuming process of database performance optimization. The company’s technology uses machine learning to safely analyze and continuously optimize those settings to help companies of all sizes run their databases more efficiently at a lower cost.

“We’ve been running OtterTune for several months, and it’s been a game changer for us,” said Daniel Rodgers-Pryor, CTO of educational technology platform developer Stile Education. “OtterTune keeps our databases running smoothly, and it has removed the risk and research time associated with manual tuning. With OtterTune in place, our team can focus more on new development, and be much less distracted by database administration.”

Since its launch last year, OtterTune expanded its service to include support for Amazon Aurora databases, as well as Amazon RDS databases. In addition, new database health checks to mitigate outages or performance drops have been added as well. The company will use the new round of funding to expand its engineering team and build support for additional databases on other cloud platforms as well as innovating additional autonomous optimization features.

“We’re achieving our goals of helping companies improve database performance at lower costs and freeing DBAs to focus on more strategic work,” said Andy Pavlo, co-founder and CEO of OtterTune. “Automatic knob configuration-using machine learning is proving to be even more fruitful than we expected it would be during our research period at Carnegie Mellon University.”

Automatic database optimization is strategic

OtterTune’s customers have seen substantial improvements on their database performance and stability, regardless of the workload, resulting in significant efficiencies and cost savings. By automating database optimization, OtterTune gives cloud teams peace of mind and time to focus on other priorities.

“OtterTune’s founders are multidisciplinary experts at the intersection of the database field and machine learning as evidenced by their cutting edge research while at Carnegie Mellon University,” said Nick Washburn, Senior Managing Director at Intel Capital. “Databases are the bedrock of all applications, and OtterTune is accelerating the journey for companies of all sizes to autonomously optimize this critical component of their tech stack, driving performance, managing cost and ultimately ensuring reliability.”

“OtterTune is the biggest breakthrough in database technology I have seen for a very long time. Having this smart self learning algorithm to infinitely tune database performance will allow us to take a big leap forward towards this $68.5 billion market by 2026,” said Alfred Chuang, General Partner at Race Capital.

“Relational Riverside Rumble 2022” - Human vs. OtterTune

OtterTune is also announcing its first-ever human vs. OtterTune tuning contest to be held in September. The bout will pit OtterTune’s automated approach to database optimization against an expert human database administrator. The competitor achieving the greatest database performance improvement will be crowned the Relational Riverside Rumble 2022 winner and will be rewarded with a cash prize of $10,000. Visit for more details.

About OtterTune

OtterTune is a database automation and optimization platform that observes your database's runtime metrics and then carries out machine learning to recommend and deploy configuration settings, improving its performance, uptime, and efficiency. It works for cloud-based PostgreSQL and MySQL databases (Amazon RDS and Amazon Aurora). For more information, visit

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