> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/cuvs/getting-started/introduction/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/cuvs/_mcp/server.

# Introduction

Start here to learn the main concepts behind NVIDIA cuVS, how they fit together, and which guide to read next.

- [Vector Search](/getting-started/introduction/vector-search): learn the main index families, how they trade recall for performance, and where to start when choosing an index.
- [Clustering](/getting-started/introduction/clustering): compare clustering methods, including why K-Means is often the preferred partitioning method for large vector search systems.
- [Vector Compression](/getting-started/introduction/compression): compare quantization and dimensionality reduction methods for reducing vector storage, bandwidth, and search cost.
- [Vector Database](/getting-started/introduction/vector-database): understand the difference between a vector index and a vector database, including local, global, and hybrid partitioning.
- [Tuning Indexes](/getting-started/introduction/tuning-indexes): tune standalone indexes and vector databases for recall, latency, throughput, build time, and memory.

## Docs

- [Vector Search](https://docs.nvidia.com/cuvs/getting-started/introduction/vector-search.md)
- [Clustering](https://docs.nvidia.com/cuvs/getting-started/introduction/clustering.md)
- [Compression](https://docs.nvidia.com/cuvs/getting-started/introduction/compression.md)
- [Vector Database](https://docs.nvidia.com/cuvs/getting-started/introduction/vector-database.md)
- [Tuning Indexes](https://docs.nvidia.com/cuvs/getting-started/introduction/tuning-indexes.md)