ChatFeatured boosts AI analytics performance

AI platform ChatFeatured cut its analytics query times from 2.5 minutes to under a second by switching its database to ClickHouse.

ChatFeatured, an AI platform for brand discovery, resolved a major performance bottleneck by migrating its analytics database. The company's queries on PlanetScale Postgres were taking up to 2.5 minutes, which was too slow for its real-time AI features. They switched to a Postgres-compatible service managed by ClickHouse, a database specifically designed for fast analytical processing. The entire migration was completed in just 30 minutes. This change resulted in a dramatic performance boost, with the same complex queries now running in under one second. The swift migration and immediate performance gains allowed the company to better support its core product without significant engineering overhead.

This case study is relevant for developers, CTOs, and founders building data-intensive applications. It demonstrates the significant performance advantages of using a specialized analytical database like ClickHouse over a general-purpose one for specific workloads. The key takeaway is the importance of the Postgres-compatible interface, which enabled ChatFeatured to switch its database backend without rewriting application code. This lowers the barrier to adoption for powerful, specialized tools and minimizes migration risks. The decision highlights a broader trend of using the right tool for the right job in a modern data stack, which is crucial for delivering the speed and responsiveness required by today's AI-powered services.

The case study shows how specialized databases can offer massive performance gains for analytics with minimal migration effort, especially when they offer compatibility with existing tools like Postgres. It's a practical example for teams struggling with slow queries.

Faster analytics queries directly improve the user experience of AI-powered features, making the product more responsive and effective. The quick, low-effort migration demonstrates a path to significant performance improvement without high engineering costs, impacting the bottom line.