turbopuffer
What is turbopuffer?
turbopuffer is an object-storage-native search engine for vector search, full-text search, and hybrid retrieval. It uses object storage for durable state with memory and NVMe SSD caching to deliver scalable search at lower infrastructure cost. The platform targets AI companies, developers, and teams building semantic search, recommendations, developer tools, agents, and retrieval-augmented applications. Its API supports document ingestion, metadata filtering, ranking, approximate nearest-neighbor search, BM25 full-text search, and hybrid queries. turbopuffer differentiates itself through usage-based pricing, automatic scaling, multi-region deployment options, and support for very large indexes.
How to use turbopuffer?
1. Create a turbopuffer account, choose a region, and generate an API key. 2. Install an official SDK or use the HTTP API to create a namespace and write documents with vectors and attributes. 3. Run vector, full-text, or hybrid queries with filters and ranking, then connect the returned results to your application or AI workflow.
turbopuffer's Core Features
Vector Search: Retrieve semantically similar documents with approximate nearest-neighbor search and configurable recall.
Full-Text Search: Search and rank textual content with BM25-based queries, boosting, and text filters.
Hybrid Search: Combine vector and full-text retrieval strategies to improve relevance for mixed search intent.
Metadata Filtering: Narrow results with structured filters and user-defined document attributes.
Object-Storage Architecture: Reduce infrastructure overhead by storing durable state in object storage with an intelligent cache layer.
Namespace Management: Organize indexes into namespaces and use copy-on-write branching for fast development and testing environments.
Large-Scale Sharding: Scale a single namespace up to 256TB with namespace sharding.
Usage-Based Billing: Pay according to storage, writes, queries, and selected performance options instead of provisioning fixed database capacity.
Security Controls: Support encryption, customer-managed keys, private networking, audit logs, and enterprise deployment models.
Developer APIs and SDKs: Integrate through an HTTP API and official client libraries for Python, TypeScript, Go, Java, and other supported environments.
turbopuffer's Use Cases
- #1
AI startups building retrieval-augmented generation over large document collections
- #2
SaaS teams adding semantic and keyword search to product documentation or knowledge bases
- #3
Developer tools searching source code, issues, logs, and technical documentation
- #4
Recommendation systems matching users with products, content, or other entities
- #5
AI agents grounding answers in internal company data and web content
- #6
Machine learning teams curating large datasets for model training and evaluation
- #7
Enterprise applications requiring private networking, single tenancy, or BYOC deployment
Frequently Asked Questions
Analytics of turbopuffer
Monthly Visits Trend: Jun 2025 - Aug 2026
Traffic Sources
AI Channel Traffic Trends
Top Regions
| Region | Traffic Share |
|---|---|
| United States | 61.16% |
| India | 10.37% |
| Canada | 6.12% |
| Brazil | 4.49% |
| Germany | 3.59% |
Top Keywords
| Keyword | Traffic | CPC |
|---|---|---|
| turbopuffer | 17.8K | $5.65 |
| turbo puffer | 1.9K | $6.38 |
| turbobuffer | 290 | -- |
| turbopuffer laravel | 110 | -- |
| turbopuffer pricing | 220 | -- |
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