The startup offers a database management platform that utilizes artificial intelligence and machine learning algorithms to enhance data access and performance beyond traditional in-memory database solutions. This technology enables businesses to efficiently manage their databases while overcoming limitations imposed by conventional data handling methods.
Funding
$7.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Managing and optimizing cloud databases, particularly on AWS RDS and Aurora, can be complex and costly, often leading to downtime and inefficient resource utilization. DevOps, platform engineering, and site reliability teams struggle to maintain optimal database performance and meet service level agreements (SLAs) without extensive manual analysis and optimization.
Solution
Rapydo offers a comprehensive database operations (DB Ops) platform designed for monitoring and automating AWS RDS and Aurora databases. It provides real-time observability, allowing users to visualize metrics from multiple databases in a single interface and detect SLA threats early. The platform includes Rapydo Scout for non-intrusive monitoring, health reports, alerts, and trend analysis, and Rapydo Cortex for automating fixes such as query rewriting, caching, and rate limiting. By automating tedious tasks and providing actionable insights, Rapydo reduces friction between development and operations, ensures compliance with database SLAs, and helps organizations save on RDS costs.
Target Audience
Rapydo is designed for DevOps, platform engineering, and site reliability teams managing MySQL databases on AWS RDS and Aurora, as well as database administrators (DBAs) seeking to automate database optimization and ensure SLA compliance.
Features
- Real-time, non-intrusive monitoring of MySQL databases on AWS RDS and Aurora
- Automated query rewriting and caching to improve database performance
- Configurable rules for rate limiting, query blocking, and throttling
- Historical trend analysis for query runtime and execution counts
- Customizable alerts for repetitive queries, resource overutilization, and suspicious query patterns
- Multi-RDS script execution for cross-database visibility and schema consistency
- Bottleneck analysis to identify and resolve query locks
- Drift detection to track configuration changes across databases
- Integration with existing AWS capabilities like floating IP or NLB for high availability