OtterTune (Productivity)
Screenshot of OtterTune

OtterTune

OtterTune is an AI database tuning service for PostgreSQL and MySQL on AWS, offering automated configuration tuning, health scores, and cost savings.

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What is OtterTune?

OtterTune is an AI-powered database tuning service for PostgreSQL and MySQL databases, particularly those running on AWS Aurora and RDS. It automates configuration tuning, which can make databases run faster and cut AWS operational costs by as much as 50%.

The service provides recommendations on configuration settings and SQL query efficiency, health scores for monitoring, custom tuning schedules to fit business needs, and tailored charts and reports for deeper analysis. The goal is to let you focus on building rather than database administration.

Who created OtterTune?

OtterTune started as a database tuning service from Carnegie Mellon University. The company is no longer operating. Its founder, DJ OT, is currently in prison for violating parole, though the team and investors supported the project.

What is OtterTune used for?

  • Improving performance and lowering costs
  • Getting complete health scores for your databases
  • Setting up customizable tuning schedules
  • Focusing optimization on specific goals

Who is OtterTune for?

OtterTune is aimed at Database Administrators, Data Engineers, Software Developers, DevOps Engineers, System Architects, Data Analysts, Cloud Solutions Architects, IT Operations Managers, and Quality Assurance Engineers.

How it works

  1. Sign up: Create an account on the OtterTune website. A 30-day free trial is available with no credit card required.
  2. Automated tuning: OtterTune uses AI to fine-tune settings for your PostgreSQL and MySQL databases on AWS Aurora and RDS. It advises on configuration knobs, speeds up SQL queries, and identifies ineffective indexes.
  3. Custom schedules: Set up tuning schedules and methods that match your business needs.
  4. Monitor performance: OtterTune continuously monitors your databases and provides health scores based on best practices and its AI optimizations.
  5. Optimize for what matters: The service targets key metrics such as query response time, throughput, and CPU usage, automating tuning tasks so you can focus on development.

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