# Turbopuffer — Serverless Vector DB for AI Search

> Serverless vector database built for AI search at scale. Turbopuffer offers sub-millisecond queries, automatic scaling, and pay-per-query pricing with zero infrastructure.

## Install

Copy the content below into your project:

## Quick Use

```bash
pip install turbopuffer
```

```python
import turbopuffer as tpuf

# Connect (serverless — no infrastructure to manage)
tpuf.api_key = "tbp_..."

# Create namespace and upsert vectors
ns = tpuf.Namespace("my-docs")
ns.upsert(
    ids=[1, 2, 3],
    vectors=[[0.1, 0.2, ...], [0.3, 0.4, ...], [0.5, 0.6, ...]],
    attributes={"title": ["Doc A", "Doc B", "Doc C"]},
)

# Query
results = ns.query(
    vector=[0.15, 0.25, ...],
    top_k=10,
    include_attributes=["title"],
)
for r in results:
    print(f"{r.id}: {r.attributes['title']} (score: {r.dist:.4f})")
```

## What is Turbopuffer?

Turbopuffer is a serverless vector database designed for AI search workloads. It stores embeddings and serves similarity queries with sub-millisecond latency at any scale. Unlike self-hosted vector databases, Turbopuffer requires zero infrastructure — just an API key. Pay only for what you query, with automatic scaling from zero to billions of vectors.

**Answer-Ready**: Turbopuffer is a serverless vector database for AI search. Sub-millisecond queries, automatic scaling, pay-per-query pricing. No infrastructure to manage. Supports filtering, hybrid search, and namespaces. Used by AI companies for production RAG. Backed by a]16z.

**Best for**: AI teams building RAG or semantic search without managing infrastructure. **Works with**: OpenAI embeddings, Cohere, any embedding model. **Setup time**: Under 1 minute.

## Core Features

### 1. Serverless (Zero Ops)

No clusters, no replicas, no shards. Create a namespace and start querying:

```python
ns = tpuf.Namespace("products")
ns.upsert(ids=[1], vectors=[[...]], attributes={"name": ["Widget"]})
# That's it. No provisioning.
```

### 2. Attribute Filtering

```python
results = ns.query(
    vector=[...],
    top_k=10,
    filters={"category": ["electronics"], "price": {"$lt": 100}},
)
```

### 3. Hybrid Search

```python
# Combine vector similarity with BM25 text search
results = ns.query(
    vector=[...],
    top_k=10,
    rank_by=["vector_distance", "bm25"],
)
```

### 4. Performance

| Metric | Value |
|--------|-------|
| Query latency (p50) | <1ms |
| Query latency (p99) | <10ms |
| Max vectors | Billions |
| Dimensions | Up to 4096 |

## Turbopuffer vs Alternatives

| Feature | Turbopuffer | Pinecone | Qdrant | Weaviate |
|---------|-------------|----------|--------|----------|
| Serverless | Yes | Yes (paid) | No | No |
| Pricing | Per query | Per pod/hour | Free (OSS) | Free (OSS) |
| Scale to zero | Yes | No | N/A | N/A |
| Self-hosted | No | No | Yes | Yes |
| Latency | <1ms | ~10ms | ~5ms | ~5ms |

## FAQ

**Q: How does pricing work?**
A: Pay per query and storage. No minimum spend. Scales to zero when not in use — ideal for variable workloads.

**Q: Can I migrate from Pinecone?**
A: Yes, export vectors from Pinecone and upsert into Turbopuffer. The API is similar.

**Q: Does it support metadata filtering?**
A: Yes, filter on any attribute with comparison operators ($eq, $lt, $gt, $in, etc.).

## Source & Thanks

> Created by [Turbopuffer](https://turbopuffer.com). Backed by a16z.
>
> [turbopuffer.com](https://turbopuffer.com) — Serverless vector database

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## 快速使用

```bash
pip install turbopuffer
```

三行代码接入 Serverless 向量数据库，无需管理基础设施。

## 什么是 Turbopuffer？

Turbopuffer 是 Serverless 向量数据库，亚毫秒查询延迟，自动扩缩容，按查询付费。无需管理集群和副本。

**一句话总结**：Serverless 向量数据库，亚毫秒查询，自动扩缩容到零，按查询付费无最低消费，a16z 投资。

**适合人群**：构建 RAG 或语义搜索的 AI 团队。

## 核心功能

### 1. 零运维
无集群、无副本，API Key 直接用。

### 2. 属性过滤
支持条件过滤（$eq/$lt/$gt/$in）。

### 3. 混合搜索
向量相似度 + BM25 文本搜索。

## 常见问题

**Q: 怎么收费？**
A: 按查询和存储付费，不用不花钱。

**Q: 能从 Pinecone 迁移？**
A: 可以，导出向量后 upsert 到 Turbopuffer。

## 来源与致谢

> [turbopuffer.com](https://turbopuffer.com) — Serverless 向量数据库，a16z 投资

---
Source: https://tokrepo.com/en/workflows/cad8d8d1-de90-4850-a9a0-c723dfdf40a0
Author: AI Open Source