turbopuffer vs Green Vectors
turbopuffer is a vector and full-text search database that lowers cost by building on object storage instead of memory. Green Vectors is not a database and not a competitor to turbopuffer; it is an ingestion-time layer that reduces the number of vectors you store by eliminating semantic redundancy. turbopuffer makes each stored vector cheaper; Green Vectors makes there be fewer vectors to store. They operate on different axes and compound: reducing redundancy before storage lowers cost and improves performance on any backend, including object-storage databases like turbopuffer.
What turbopuffer is
turbopuffer, founded in 2023, is a serverless vector and full-text search database built from first principles on object storage (such as S3 or GCS) rather than in-memory storage. Its central idea is economic: memory is the most expensive place to store vectors, and object storage is dramatically cheaper, so turbopuffer keeps data in object storage with a tiered cache in front of it. It markets itself as roughly ten times cheaper than memory-based alternatives and reports running at very large scale for well-known AI products. It is a full database backend, the kind of system you would use in place of Pinecone, Qdrant, Weaviate, or pgvector.
A note on the name: turbopuffer is not TurboQuant
Because the names are similar, they are easy to confuse, but they are unrelated. turbopuffer is a vector database. TurboQuant is a vector quantization algorithm from Google Research that compresses individual vectors. This page is about turbopuffer the database. For the quantization algorithm, see TurboQuant vs Green Vectors.
What Green Vectors does, and why it is a different axis
Green Vectors does not store vectors or serve queries. It is an ingestion-time layer, delivered through the Kitana SDK, that eliminates semantically redundant vectors before they are written to whatever database you use. turbopuffer reduces the cost per stored vector by changing where vectors live. Green Vectors reduces how many vectors exist by removing redundancy. One optimizes storage economics; the other optimizes what gets stored in the first place. Neither makes the other unnecessary.
Why they compound
Object-storage databases like turbopuffer trade a little latency for large cost savings: cold queries that must reach object storage are slower, while cached queries are fast. Fewer vectors helps this directly. A smaller index is cheaper to store on any backend, fits more completely in the faster cache tiers, and means fewer candidates to scan per query. So reducing redundancy with Green Vectors before storing in turbopuffer lowers cost further and reduces the cold-query penalty that object-storage designs work hard to manage. If you have already moved to turbopuffer for cost reasons, Green Vectors extends the same goal a step earlier in the pipeline.
How to think about the choice
This is not an either-or decision. If you are choosing a vector database and cost at scale is the priority, turbopuffer is a strong option worth evaluating on its own merits. If your index is bloated with redundant vectors, reducing their number at ingestion lowers cost and improves retrieval on whatever database you choose, turbopuffer included. The most cost-efficient configuration is a clean, reduced index on a cost-efficient backend.