# turbopuffer review

> turbopuffer is a hosted vector and full-text search database built on object storage, with an official Python client library.

Devtoolwatch Board, the week of Oct 25–31, 2026. Canonical page: https://index.devtoolwatch.com/tools/turbopuffer/

## TL;DR

turbopuffer is a vector and full-text search database built on object storage, reached through an HTTP API. The official Python library gives Python 3.10+ applications typed sync and async clients for it. It fits developers building semantic search, retrieval, or recommendation features who want search backed by object storage with a memory/SSD cache.

## turbopuffer at a glance

- **Category:** [RAG, data and memory](https://index.devtoolwatch.com/categories/rag-data-and-memory/)
- **Maintained by:** turbopuffer (organization, [@turbopuffer](https://github.com/turbopuffer) on GitHub)
- **License:** MIT
- **Language:** Python, Shell, Dockerfile
- **Repository:** [github.com/turbopuffer/turbopuffer-python](https://github.com/turbopuffer/turbopuffer-python)
- **Website:** [turbopuffer.com](https://turbopuffer.com)
- **npm package:** [@turbopuffer/turbopuffer](https://www.npmjs.com/package/@turbopuffer/turbopuffer) 2.11.1
- **PyPI package:** [turbopuffer](https://pypi.org/project/turbopuffer/) 2.14.0
- **Latest release:** [v2.14.0](https://github.com/turbopuffer/turbopuffer-python/releases/tag/v2.14.0) on GitHub, Oct 21, 2026
- **First published:** Dec 12, 2023

## What turbopuffer does

- turbopuffer runs approximate nearest neighbor vector queries with attribute filters and a top_k limit.
- turbopuffer supports BM25 full-text search on attributes, alongside hybrid search and metadata filtering.
- turbopuffer stores documents in namespaces that you write to by upserting rows with vectors and a distance metric such as cosine distance.
- turbopuffer's Python client offers synchronous and asynchronous clients with type definitions for all request params and response fields.
- turbopuffer's client auto-paginates list methods, retries certain errors with exponential backoff, and times out requests after 1 minute by default.
- turbopuffer's Python client supports reranking with a model as of version 2.13.0.
- turbopuffer lets you drop attributes from a namespace schema and create a namespace with a write that contains no documents.

### How it works

turbopuffer is a hosted service. Your client calls its HTTP API, which serves queries from a memory/SSD cache in front of object storage (S3). The Python library wraps that API using httpx, or optionally aiohttp for async. You create a client with a region and an API key, open a namespace, then write rows and run vector or full-text queries.

## How to install turbopuffer

**With pip**

```
pip install turbopuffer
```

**With pip and the aiohttp backend**

```
pip install turbopuffer[aiohttp]
```

### Requirements

- Python 3.10 or later
- A turbopuffer API key, read from TURBOPUFFER_API_KEY by default
- A turbopuffer region chosen when creating the client

### First steps

1. Sign up for turbopuffer and get an API key.
2. Add TURBOPUFFER_API_KEY to your .env file with python-dotenv so the key stays out of source control.
3. Create a Turbopuffer client with your region, for example gcp-us-central1, and open a namespace with tpuf.namespace().
4. Run ns.query() with rank_by set to a vector ANN query or a BM25 text query.

## Who turbopuffer is for

- Python developers adding vector or full-text search to AI applications
- Teams building semantic search or recommendation systems on a hosted search database
- Async Python services that need a typed search client

### Limitations

- turbopuffer is a hosted API, so the client needs network access and an account API key to do anything.
- turbopuffer's Python library requires Python 3.10 or later.
- turbopuffer's Python client does not use compression by default, which favors CPU efficiency over bandwidth.

## Usage data (the week of Oct 25–31, 2026)

- Board rank: #170 overall, #18 in RAG, data and memory, momentum score 49
- Downloads that week: 3.4M (+32% vs 4-week average)
- New GitHub stars per week (last 12 weeks): Aug 9: 0; Aug 16: 2; Aug 23: 1; Aug 30: 4; Sep 6: 1; Sep 13: 3; Sep 20: 0; Sep 27: 3; Oct 4: 2; Oct 11: 4; Oct 18: 2; Oct 25: 5

## turbopuffer alternatives

| Rank | Tool | Stars | Downloads/wk |
|---|---|---|---|
| #1 | [XERJ](https://index.devtoolwatch.com/tools/xerj-org-xerj/) | 17.9k | – |
| #2 | [Memvid](https://index.devtoolwatch.com/tools/memvid-memvid/) | 17.3k | – |
| #3 | [LEANN](https://index.devtoolwatch.com/tools/startrail-org-leann/) | 13.7k | – |
| #4 | [Graphify](https://index.devtoolwatch.com/tools/graphify-labs-graphify/) | 140k | 480.7k |
| #5 | [MarkItDown](https://index.devtoolwatch.com/tools/markitdown/) | 193.2k | 4.7M |
| #6 | [Firecrawl](https://index.devtoolwatch.com/tools/firecrawl/) | 197.4k | 2.9M |

## turbopuffer FAQ

### Is turbopuffer free and open source?

Yes. turbopuffer's source code is on GitHub (turbopuffer/turbopuffer-python) under the MIT license, so you can use it for free. Its website also links to a pricing page (https://turbopuffer.com/pricing) for any paid plans.

### How do I install turbopuffer?

Run `pip install turbopuffer`. The install section above has the other options from the project's README.

### Is turbopuffer still maintained?

Yes. The turbopuffer/turbopuffer-python repository was last updated on Oct 21, 2026, and the latest release, v2.14.0 on GitHub, came out on Oct 21, 2026.

### How popular is turbopuffer?

turbopuffer has 183 GitHub stars as of Nov 1, 2026. It gained 5 new stars in the week of Oct 25–31, 2026, against a 12-week average of 2 a week. Its main package had about 3.4M downloads that week, +32% against its own 4-week average. That made it #18 among RAG, data and memory tools on the Devtoolwatch Board.

### turbopuffer vs pgvector: how do they compare?

turbopuffer and pgvector are both tracked by Devtoolwatch as RAG, data and memory tools. turbopuffer has 183 GitHub stars and pgvector has 23.5k. In the week of Oct 25–31, 2026, turbopuffer's main package had 3.4M downloads and pgvector's had 9.4M. pgvector is an open-source Postgres extension for exact and approximate vector similarity search, storing vectors alongside the rest of your data. [pgvector review](https://index.devtoolwatch.com/tools/pgvector/)

### turbopuffer vs Qdrant: how do they compare?

turbopuffer and Qdrant are both tracked by Devtoolwatch as RAG, data and memory tools. turbopuffer has 183 GitHub stars and Qdrant has 35.4k. In the week of Oct 25–31, 2026, turbopuffer's main package had 3.4M downloads and Qdrant's had 4.5M. Qdrant is an open-source vector similarity search engine and vector database written in Rust, with filtering on JSON payloads. [Qdrant review](https://index.devtoolwatch.com/tools/qdrant/)

### What are the best alternatives to turbopuffer?

Other RAG, data and memory tools on the Devtoolwatch Board in the week of Oct 25–31, 2026, by momentum: XERJ (#1), Memvid (#2), LEANN (#3), Graphify (#4), MarkItDown (#5). The alternatives table above has their numbers.

### Is turbopuffer a vector database?

Yes. turbopuffer is a vector and full-text search database built on object storage. It supports vector search, full-text search, hybrid search, and metadata filtering.

### What is turbopuffer used for?

turbopuffer is used for search in AI applications, semantic search, recommendation systems, and other similarity search use cases. Queries can rank by vector ANN or by BM25 text relevance.

### How does turbopuffer work?

turbopuffer stores data in object storage (S3) with a memory/SSD cache in front of it. Clients reach it through an HTTP API, organizing data into namespaces they write to and query.

### Does the turbopuffer Python client support async?

Yes. turbopuffer's library provides AsyncTurbopuffer, which you use with await. Functionality matches the sync client. You can also install the aiohttp extra and pass DefaultAioHttpClient() for the HTTP backend.

### Is there a TypeScript client for turbopuffer?

Yes. turbopuffer publishes an official TypeScript library on npm as @turbopuffer/turbopuffer.

## Sources

1. [turbopuffer/turbopuffer-python on GitHub](https://github.com/turbopuffer/turbopuffer-python)
2. [turbopuffer README](https://github.com/turbopuffer/turbopuffer-python/blob/main/README.md)
3. [@turbopuffer/turbopuffer on npm](https://www.npmjs.com/package/@turbopuffer/turbopuffer)
4. [turbopuffer on PyPI](https://pypi.org/project/turbopuffer/)
5. [turbopuffer website](https://turbopuffer.com)
6. [Release v2.14.0 (Oct 21, 2026)](https://github.com/turbopuffer/turbopuffer-python/releases/tag/v2.14.0)
7. [Release v2.13.0 (Oct 13, 2026)](https://github.com/turbopuffer/turbopuffer-python/releases/tag/v2.13.0)
8. [Release v2.12.0 (Oct 9, 2026)](https://github.com/turbopuffer/turbopuffer-python/releases/tag/v2.12.0)
9. [Hacker News: RIP, vector database](https://news.ycombinator.com/item?id=49923466)
10. [Hacker News: Turbopuffer: Fast search on object storage](https://news.ycombinator.com/item?id=40916786)
11. [Hacker News: A distributed queue in a single JSON file on object storage](https://news.ycombinator.com/item?id=47099401)
12. [Devtoolwatch Board, the week of Oct 25–31, 2026](https://index.devtoolwatch.com/)