How Brainstore works: architecture for AI observability at scale

AI observability presents unique challenges that traditional database architectures struggle to address, prompting Braintrust to develop a custom solution called Brainstore. AI workloads generate complex, large-scale data that exceed the capabilities of typical observability systems, creating issues with data ingest, payload size, and trace longevity. The pre-Brainstore architecture, relying on a combination of open-source warehouse, Postgres, and DuckDB, proved fragile under the pressure of AI data's scale and complexity, leading to performance and reliability issues. Brainstore was designed to overcome these challenges with a focus on simplicity, scalability, and speed, using object storage for durability and partitioned data to optimize performance. The system is structured to handle high throughput without coordination bottlenecks, efficiently indexing data for fast reads and interactive queries. This architecture supports immediate data visibility, precise targeting of reads, and interactive exploration, making it well-suited for the demands of AI observability, while maintaining operational simplicity and developer ease of use.

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