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Pinecone (Serverless) vs SvectorDB: Choosing the right vector database

ยท 2 min read

With the recent introduction of Pinecone's serverless option, it's worth exploring how it stacks up against SvectorDB. While both Pinecone and SvectorDB share commonalities, their distinctions emerge prominently in their pricing models and client support.

Pricingโ€‹

Pinecone adopts a pricing model that hinges on three primary dimensions: read units, write units, and storage. While storage costs are straightforward, understanding read and write units can be a bit intricate. Though the exact calculation method for these units remains unclear, Pinecone provides some examples for illustration.

In contrast, SvectorDB's pricing model is notably more transparent. It is based on the number of read requests, write requests, and the compressed size of the index.

ActionPineconeSvectorDBCost per million
Querying a 384-dimensional index with 100k entries for 50 results10 RUs1 read request$82.50 vs $5
Querying a 768-dimensional index with 1m entries for 50 results15 RUs1 read request$123.75 vs $5
Inserting a 384-dimensional vector3 WUs1 write request$6 vs $20
Inserting a 1536-dimensional vector7 WUs1 write request$14 vs $20
Storage$0.33 / GB$0.25 / GB

Client supportโ€‹

Pinecone has native Python and JavaScript libraries. SvectorDB goes a step further by providing an official OpenAPI specification, enabling the automatic generation of client libraries for any programming language. This flexibility ensures that even if your preferred language is not natively supported by SvectorDB, you won't have to create a custom client library.

Conclusionโ€‹

While Pinecone holds a prominent position in the vector database domain, its complexity may present challenges for certain users. In contrast, SvectorDB stands out with its straightforward pricing model, extensive client support, and notably lower costs, making it an appealing choice for those seeking an efficient and budget-friendly vector database solution.

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