RAG vector search
RAG teams need more than a search library. They need memory estimates, private-data boundaries, filter policy, and a receipt that explains the chosen retrieval path.
RAG vector search
Raw embedding storage before index overhead.
Estimated packed index with norms and id overhead.
Workflow fit
Best when a corpus is growing and the team needs a smaller local or VPC-safe index before adding another managed service.
Capabilities
The planner packages turbovec and TurboQuant-adjacent decisions into a commercial workflow instead of repeating an open-source README.
Input: Corpus size, embedding dimension, bit width, id policy, and filter mode
Process: Estimates float32 baseline, TurboQuant packed vectors, norm/correction overhead, and id map cost
Output: Memory delta, compression ratio, readiness score, and paid receipt
Input: Current vector store, framework, dataset shape, latency concern, and privacy boundary
Process: Maps the workload to Python or Rust turbovec paths, persistence files, and benchmark checks
Output: Migration review steps, risk notes, and checkout-gated team export
Input: Tenant, ACL, SQL/BM25 candidate set, time window, or slot bitmask needs
Process: Captures where filtering happens and whether selective filters reduce wasted scan work
Output: Policy brief for engineering, security, and retrieval owners
Input: LangChain, LlamaIndex, Haystack, Agno, Python, or Rust target
Process: Converts the selected path into install, import, persistence, and validation notes
Output: Implementation receipt with exact package path and next step
Intent pages
Each page answers a specific search intent and routes the visitor back to the planner, pricing, or hosted checkout.
For visitors who found the turbovec GitHub project and need a practical evaluation path.
turbovec tutorial turbovec tutorial path with production handoffFor users who want to learn turbovec but also need a production-minded path.
turbovec python bindings turbovec Python bindings for compressed RAG indexesFor Python teams comparing in-memory vector stores, FAISS, and compressed local retrieval.
TurboQuant TurboQuant vector search sizing before migrationFor teams evaluating TurboQuant as a compression strategy rather than only reading a paper or README.
FAISS alternative FAISS alternative planning for compressed vector indexesFor teams searching for a FAISS alternative with smaller memory use and local control.
RAG vector search RAG vector search sizing with private data boundariesFor teams building private RAG indexes and trying to keep memory and latency under control.
vector database Vector database compression estimate for growing corporaFor buyers comparing managed vector databases with compressed local vector indexes.
Rust vector index Rust vector index migration planning for search teamsFor infrastructure teams evaluating Rust vector search components.
TurboQuant linux TurboQuant Linux deployment notes for compressed retrievalFor teams looking beyond a local demo toward Linux or VPC deployment.
Pricing
Yearly billing is selected by default and shows the annual total divided by 12. Each checkout request preserves plan, billing, period, and planId.
Solo builders validating one vector search workload
Due today: $114 for one year. Annual billing is 50% less than monthly. No automatic renewal.
RAG teams comparing FAISS, turbovec, and hosted search paths
Due today: $294 for one year. Annual billing is 50% less than monthly. No automatic renewal.
Platform teams standardizing compressed retrieval
Due today: $894 for one year. Annual billing is 50% less than monthly. No automatic renewal.
Hosted checkout
Preparing a hosted invoice while this page remains open.