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.
Pricing
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.
| Action | Pinecone | SvectorDB | Cost per million |
|---|---|---|---|
| Querying a 384-dimensional index with 100k entries for 50 results | 10 RUs | 1 read request | $82.50 vs $5 |
| Querying a 768-dimensional index with 1m entries for 50 results | 15 RUs | 1 read request | $123.75 vs $5 |
| Inserting a 384-dimensional vector | 3 WUs | 1 write request | $6 vs $20 |
| Inserting a 1536-dimensional vector | 7 WUs | 1 write request | $14 vs $20 |
| Storage | $0.33 / GB | $0.25 / GB |
Client support
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.
Conclusion
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.
Ready to experience the difference?