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Perplexity builds CobbleDB in two months, cutting batch-read latency by 82%

Perplexity unveiled CobbleDB on September 14, 2026, a custom key-value hot store written in roughly 40,000 lines of Rust that cut median batch-read latency from 31.4 ms to 5.60 ms, an improvement of about 82%, while replacing Amazon DynamoDB. The 90th-percentile latency fell from 56.7 ms to 9.77 ms and the 99th percentile from 123 ms to 24.2 ms, and Perplexity's internal projections suggest CobbleDB could reduce storage-layer costs by more than 20% versus DynamoDB at scale. Perplexity built the system with a swarm of internal AI coding agents in approximately two months of calendar time and has committed to open-sourcing CobbleDB after production validation at hundreds of thousands of requests per second.

read2 min views1 publishedSep 15, 2026
Perplexity builds CobbleDB in two months, cutting batch-read latency by 82%
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Perplexity's custom key-value database replaces DynamoDB and slashes query times from 31.4 ms to 5.60 ms using AI coding agents and Rust.

Perplexity unveiled CobbleDB on September 14, 2026, a custom key-value hot store written in approximately 40,000 lines of Rust. The system was purpose-built to handle the specific read-heavy demands of AI-powered search, and it replaced a prior architecture built on Amazon DynamoDB.

What the numbers actually say #

Median batch-read latency dropped from 31.4 ms to 5.60 ms, an improvement of roughly 82%. The 90th-percentile latency fell from 56.7 ms to 9.77 ms. At the 99th percentile, latency dropped from 123 ms to 24.2 ms.

Cost is the other side of the ledger. Perplexity’s internal projections suggest CobbleDB could reduce storage-layer costs by more than 20% compared to DynamoDB at scale. The savings come primarily from eliminating DynamoDB’s per-byte pricing model and replacing it with workload-specific optimizations that map more cleanly to how AI search actually behaves.

How the architecture works #

CobbleDB follows a decoupled design with three distinct components. Pillar manages document durability and handles updates. Lorry handles batched ingestion, acting as the pipeline that moves data into the system efficiently. CobbleDB itself sits at the serving layer, dedicated entirely to low-latency reads at query time.

Under the hood, CobbleDB uses RocksDB for its actual data transactions. On top of that foundation, Perplexity layered tunable partitioning and caching strategies designed to squeeze performance during peak loads.

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CobbleDB does not support strong consistency or complex transactions.

AI agents building AI infrastructure #

Perplexity built CobbleDB using a swarm of internal AI coding agents, a workflow that compressed the project into approximately two months of calendar time.

What comes next #

Perplexity has committed to open-sourcing CobbleDB after the system demonstrates production validation at scales of hundreds of thousands of requests per second.

Disclosure: This article was edited by Editorial Team. For more information on how we create and review content, see our

Editorial Policy.

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