Object-storage-native search engine for vector, full-text, and hybrid retrieval at petabyte scale
turbopuffer documents a fast search engine at turbopuffer.com/docs built natively on object storage (S3, GCS, Azure Blob) with NVMe/memory caching for compute. Architecture docs describe a write-ahead log on object storage, SPFresh centroid-based ANN indexes for vectors, inverted BM25 indexes for full-text, exact metadata indexes with native filtering, and branching copy-on-write namespaces. The API supports vector ANN queries, BM25 full-text, hybrid multi-queries, regex/trigram search, filters, and encryption with customer keys per turbopuffer.com/docs/index and turbopuffer.com/docs/architecture. Docs cite production-scale limits observed (4T+ documents, 10M+ writes/s, 25k+ queries/s) with tradeoffs: higher write latency from object-storage durability and occasional cold queries on uncached namespaces.
Use cases
- First-stage retrieval narrowing millions of documents for RAG pipelines
- Cost-sensitive vector search where object storage economics beat in-memory-only DBs
- Hybrid lexical + semantic search with high-recall filtered ANN
- Petabyte-scale namespaces with pinned hot sets for low latency
- Agent search layers when paired with external embedders
Key features
- SPFresh centroid ANN index optimized for object storage roundtrips
- Native metadata filtering integrated into vector queries (not pre/post-filter only)
- Hybrid vector + BM25 full-text with branching namespaces
- Strongly consistent writes via WAL on object storage
- Multi-tenant, single-tenant, or BYOC deployment options
Who Is It For?
- Teams optimizing search cost at billion-document scale
- Engineers building RAG with strong filter requirements on vectors
- Startups wanting managed search without operating HNSW clusters
Frequently Asked Questions
- Is turbopuffer only a vector database?
- Docs position it as a search engine supporting vector, full-text, regex, and filters—not vector-only.
- What are the latency tradeoffs?
- Architecture docs cite ~165ms p50 write latency for 500kB upserts and cold query latency around hundreds of ms when uncached; cached queries can reach low tens of ms.
- Which index underpins vector search?
- turbopuffer.com/docs/architecture documents SPFresh centroid-based ANN rather than graph indexes like HNSW.
Related
Related
3 Indexed items
Meilisearch
Meilisearch documents an open-source search engine at meilisearch.com/docs for fast, typo-tolerant full-text search, faceting, filtering, and sorting. Meilisearch Cloud offers hosted deployment; self-hosted options include Docker and native binaries with REST API and official SDKs (JavaScript, Python, Rust, PHP, Java, .NET, Dart, Go). Recent docs and blog posts describe semantic and hybrid search capabilities, AI-powered search experiences, and the official meilisearch-mcp server for LLM clients. Meilisearch positions itself as a developer-friendly alternative focused on sub-50ms search experiences with simple index/document APIs.
Typesense
Typesense documents an open-source search engine at typesense.org/docs for fast typo-tolerant keyword search, faceting, and vector retrieval. Vector search docs at typesense.org/docs/30.2/api/vector-search describe KNN search on imported embeddings or auto-generated embeddings via OpenAI, Google PaLM API, or built-in Hugging Face models in huggingface.co/typesense/models (use the `ts` namespace prefix). Features include semantic search, hybrid search with rank fusion and adjustable `alpha` weighting, similar-document queries by ID, HNSW approximate search with optional `flat_search_cutoff` brute-force mode, and cosine `vector_distance` scoring. Deploy via Typesense Cloud or self-hosted Docker/binaries with REST API and official client libraries.
LanceDB
LanceDB documents a multimodal lakehouse for AI at docs.lancedb.com, built on the open-source Lance columnar format for storing vectors, metadata, raw bytes, and embeddings in unified tables. LanceDB OSS is an embedded library with Python, TypeScript, and Rust SDKs for local development; LanceDB Enterprise is a distributed managed lakehouse for search, curation, feature engineering, and training workflows per docs.lancedb.com. Features include vector/semantic search, BM25 full-text search, hybrid search with SQL filters, versioning, and cloud object-store integration (S3, GCS, Azure).