Vector Database Costs: Build a Quote from the Workload
A useful vector-database estimate starts with storage, reads, writes, and operating requirements—not just the number of vectors. Two applications with the same corpus can have very different bills because one updates continuously and the other mostly serves occasional searches.
Use a common workload sheet when comparing vendors. Published billing units differ, so a low-looking unit price is not a like-for-like monthly estimate.
Calculate raw storage, then add the missing layers
For an illustrative float32 representation, each dimension uses four bytes. One million vectors with 1,536 dimensions contain 6.144 GB of raw vector values. Ten million contain 61.44 GB. These decimal figures exclude metadata, index structures, replicas, backups, and source documents.
Reducing dimensions can lower that raw component, but it does not prove the same reduction in total service cost. The storage engine, index layout, and billing model determine which savings become visible on the bill. The Matryoshka guide explains the representation tradeoff separately.
Express query load as a distribution
Ten queries per second sustained for 30 days produce 25.92 million queries. A short daily peak at ten queries per second is a different workload. Record typical traffic, peak duration, concurrency, filters, requested result count, and latency requirements.
Include repeated retrieval within one agent task. If an agent performs four lookups for each user request, user-request volume alone understates the search workload. Retries and background evaluations also consume capacity.
Account for changes to the corpus
A document revision can trigger extraction, embedding, index updates, and removal of old records. Model changes may require re-embedding the entire corpus. During a migration, both old and new indexes can coexist, temporarily increasing capacity needs.
Estimate writes and deletions separately from reads. Ask how the quoted configuration handles background indexing and whether write-heavy periods affect query performance. Compare this behavior using your workload rather than assuming a serverless or provisioned model always wins.
Compare quotes on the same assumptions
Give each provider the same region, dimensions, record count, read/write mix, availability expectations, retention, and growth scenario. Record the quote date and separate included capacity from variable charges. Current vendor rate cards or a written quote are required for actual prices; the arithmetic here is not a price forecast.
Use the vector database selection checklist to compare update, filtering, and recovery behavior alongside the quote.
The memory operating-cost guide adds engineering and recovery work above the database. To evaluate a managed memory option, start a Supermemory pilot and measure the same ingestion and query workload, then include the application work that remains in your comparison.