This GigaOm report benchmarks Astra DB vector performance and TCO using several commonly-used vector benchmarking datasets that simulate production conditions. GigaOm then compares Astra DB performance to Pinecone, a widely used vector database.
The study finds that <a href="https://www.datastax.com/products/datastax-astra?utm_source=gigaom_blog&amp;utm_medium=blog&amp;utm_campaign=blogpost">Astra DB</a> had better performance than Pinecone, up to:
<ul>
<li>9x higher throughput than Pinecone when ingesting and indexing data</li>
<li>74x faster P99 query response time when ingesting and indexing data</li>
<li>20% higher F1 relevancy</li>
<li>80% lower total cost of ownership over a three-year period in three scenarios</li>
</ul>
Vector databases must deliver on four key metrics to successfully enable accurate generative AI and RAG (retrieval augmented generation) applications in production: throughput, latency, F1 relevancy, and total cost of ownership (TCO).

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Astra DB significantly outperforms in throughput, latency, F1 relevance, and TCO (total cost-of-ownership)

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GigaOm finds Astra DB beats Pinecone in the key performance metrics for relevance and production-ready GenAI and RAG apps.

GigaOm Study: Astra DB Smashes Pinecone in GenAI Performance

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