Estimate what it costs to store and query your embeddings across Pinecone, Qdrant, and Weaviate. Adjust vector count, dimensions, and query volume — the estimate updates live.
Storage size is computed as vector count times dimensions times 4 bytes (float32 precision), converted to gigabytes and multiplied by the provider's per-GB monthly storage rate. Query cost is your monthly query volume divided by one million and multiplied by the provider's per-million read-unit rate.
It doesn't account for metadata storage overhead, index type (HNSW vs IVF), replication factor, write-unit costs (Pinecone charges ~$2/1M write units separately), or free-tier allowances. Qdrant Cloud actually bills by node-hour rather than per-GB — the rate shown here is a GB-equivalent approximation for comparison, not Qdrant's real invoice line item. Real-world vector database bills commonly run 2.5-4x higher than the sticker estimate once reranking, backups, and data transfer are added — treat this as a floor, not a ceiling.
Storage cost is based on vector count times dimensions times 4 bytes (float32), converted to GB and multiplied by the provider's per-GB monthly rate. Query cost is your monthly query volume multiplied by the provider's per-read-unit rate.
Each dimension adds 4 bytes per vector at float32 precision, so a 1536-dimension embedding model stores 6x more data per vector than a 256-dimension model, directly multiplying your storage bill.
Yes. Techniques like quantization (int8 or binary), dimensionality reduction, and deleting stale vectors can cut storage cost significantly, often with a small and testable accuracy tradeoff.
Embedding API cost · LLM API cost · AI agent cost · Compare: Pinecone vs Qdrant