Vector database planning
Find the cost.
Understand
the search.
Turn your workload into monthly estimates. Explore the indexes and embeddings behind your search.
Compare managed services and self-hosted infrastructure.
S3 Vectors / Pinecone / OpenSearch / and more
Inside the vector spaceA conceptual view
Start with the data footprint
Example: 1 million vectors
3.07GB
Raw vector data
Excludes metadata, indexes, and replicas. Supported precision and billing vary by provider.
Go beyond the estimate
Explore the mechanics01
See how search works
Step through HNSW and IVF. Explore how index choices affect the search path.
Explore the algorithms02
Map a semantic neighborhood
Compare nearby points, exact neighbors, and approximate search in one view.
Open the semantic map03
Turn words into vectors
Generate embeddings from your own text and inspect their dimensions.
Try text embeddings04
Run the Workload Lab
Try repeatable cases and a public corpus. Compare local recall, latency, and index size.
Open Workload LabPlanning estimates, not vendor quotes. Review each provider’s rates, assumptions, and billing model before making a decision.