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    Perplexity replaces DynamoDB with CobbleDB for search storage

    Perplexity says two engineers and hundreds of always-on AI agents built CobbleDB’s core infrastructure in two months. The company plans to open-source it for other teams building AI search.

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    TLDR

    Perplexity is sharing research on CobbleDB, its key-value database serving web content for search. The company says two engineers and hundreds of always-on AI agents built the core infrastructure in two months.

    After replacing DynamoDB, Perplexity reports production measurements showing median batch-read latency falling from 31.4 to 5.60 milliseconds and p99 latency from 123 to 24.2 milliseconds. Its internal cost model estimates savings of at least 20% relative to DynamoDB.

    Perplexity says CobbleDB is purpose-built for repeated batch reads of prepared page records. It plans to open-source the database for other AI search teams.

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    2 Sources, first seen 15d ago

    Combined views

    27.8K

    2 Sources, first seen 15d ago

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    2 Sources

    @perplexity_aiCobbleDB is purpose-built for repeated batch reads of prepared page records, optimizing performance and cost for Perplexity’s search workload. We plan to open-source CobbleDB for other teams building AI search. Read more: https://www.perplexity.ai/hub/blog/cobbledb
    @denisyaratswe are sharing how we built CobbleDB, our replacement for AWS DynamoDB that powers key-value storage for our web-scale search engine. we optimized CobbleDB for our exact read and write patterns. result: 5x lower batch read latency and at least 20% lower serving cost (in practice much higher). what i'm most excited about is how it was built and deployed: 2 human engineers plus our internal system that runs a swarm of hundreds of persistent AI agents in an autonomous loop. the agents reviewed code and infra changes, caught blockers that were easy to miss, prepared fixes, tests, and monitoring, ran the migrations, and did head-to-head comparisons on real traffic to hunt down bugs and inefficiencies. they tracked CI and rollout gates and kept project readouts up to date. production actions stayed explicitly human-owned though. more details and some insights about our search engine in the blog: https://www.perplexity.ai/hub/blog/cobbledb if you want to work on problems like this, DM me or apply at https://www.perplexity.ai/hub/careers

    2 Sources

    @perplexity_aiCobbleDB is purpose-built for repeated batch reads of prepared page records, optimizing performance and cost for Perplexity’s search workload. We plan to open-source CobbleDB for other teams building AI search. Read more: https://www.perplexity.ai/hub/blog/cobbledb
    @denisyaratswe are sharing how we built CobbleDB, our replacement for AWS DynamoDB that powers key-value storage for our web-scale search engine. we optimized CobbleDB for our exact read and write patterns. result: 5x lower batch read latency and at least 20% lower serving cost (in practice much higher). what i'm most excited about is how it was built and deployed: 2 human engineers plus our internal system that runs a swarm of hundreds of persistent AI agents in an autonomous loop. the agents reviewed code and infra changes, caught blockers that were easy to miss, prepared fixes, tests, and monitoring, ran the migrations, and did head-to-head comparisons on real traffic to hunt down bugs and inefficiencies. they tracked CI and rollout gates and kept project readouts up to date. production actions stayed explicitly human-owned though. more details and some insights about our search engine in the blog: https://www.perplexity.ai/hub/blog/cobbledb if you want to work on problems like this, DM me or apply at https://www.perplexity.ai/hub/careers