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Vector databases 2026 Rank: #2580

turbopuffer ✓

Search every byte with 10x cheaper vector search.

Source rating 5.0 (4) 2.7k views 4 saves Paid
Vector Search High-Performance Search Big Data Handling
More categories (4)
Semantic Search Recommendation Systems Scalable Database Vector databases
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from $64/mo

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Verdict

A quick read before you dig into the full page.

Best if
Search every byte with 10x cheaper vector search
Skip if
Limited pinned namespaces
Pick this when
10x cheaper vector search

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Overview

Turbopuffer is a trusted and scalable vector and full-text search database designed around object storage. Its main application lies in areas such as AI, semantic search, recommendation systems, and other use cases that require high-performance similarity search functionality.

It provides faster search capabilities at a lower cost. Turbopuffer's unique features include automated scaling, low latency in responses, the ability to support billions of vectors, hybrid search, metadata filtering, and substantial savings compared to traditional vector databases.

Instant namespace branching is another feature that enhances its flexibility and performance. In addition, Turbopuffer is noted for its scalability and capacity to handle massive amounts of data, reflected in the use by leading companies, underlining its capabilities.

Testimonials highlight its effectiveness in terms of cost savings and decreased complexity. However, the exact numbers regarding its capabilities, such as documents it can handle or queries it can process, may vary and will need to be confirmed by the service provider for current and accurate information.

turbopuffer website

Access & connections

Where you can use turbopuffer.

Platforms

Web

API

Public API available

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Capabilities

What this tool accepts, produces, and is strongest at.

Input

  • API

Output

  • API

Highlights

  • API

Supported features

  • Search
  • API

Pros & cons

Pros

  • 10x cheaper vector search
  • Faster search capabilities
  • Automated scaling
  • Low latency responses
  • Supports billions of vectors
  • Hybrid search capabilities
  • Metadata filtering
  • Instant namespace branching
Show 27 more
  • Scalable database
  • Object storage based
  • High-performance similarity search
  • Cost-effective
  • Handles massive data
  • 4T+ document handling
  • 10M+ writes/s
  • 25k+ queries/s
  • Sub-10ms p50 latency
  • Full-text search capabilities
  • Universal API
  • Good documentation
  • Trusted by leading companies
  • Simple integration
  • High recall rates
  • Unlimited scalability
  • Customizable
  • Low complexity
  • Order of magnitude cost savings
  • Easy to use
  • Flexible architecture
  • Fast performance
  • Reliable
  • Secure
  • Compliance features
  • Namespace metadata
  • Authentication support

Cons

  • Performance varies
  • Data handling ambiguity
  • Lack of SSL encryption
  • Cost savings unclear
  • Limited pinned namespaces
  • Vague production limits
  • Query restrictions per namespace
  • Exact capabilities unconfirmed
Show 2 more
  • Specific latency unclear
  • Queried document limitation

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Releases 1

Initial release June 5, 2026 Current
Initial release of turbopuffer.

FAQ 12

What is the main application of Turbopuffer?
The main application of Turbopuffer is primarily in areas such as artificial intelligence, semantic search, recommendation systems, and other use cases that require high-performance similarity search.
How does the performance of Turbopuffer compare with traditional vector databases?
Compared to traditional vector databases, Turbopuffer offers faster search capabilities with lower latency in responses. It's significantly cheaper and can support billions of vectors, exemplifying greater performance both in terms of speed and cost.
What are the key features of Turbopuffer?
Key features of Turbopuffer include automated scaling, low latency (sub-10ms p50), the ability to support billions of vectors, full-text search capabilities, hybrid search, metadata filtering, and significant cost savings compared to traditional vector databases. Another unique feature is 'namespace branching', which enhances flexibility and performance.
What is Turbopuffer's capacity for handling data?
Turbopuffer handles massive amounts of data, having the capacity to deal with 4 trillion+ documents, 10 million+ writes per second, and 25 thousand+ queries per second in production systems. This showcases its ability to manage big data and process exceedingly high volumes of information.
What types of search does Turbopuffer support?
Turbopuffer supports both full-text and vector search. It also enables hybrid search, which is a combination of full-text and vector search, providing comprehensive results.
In which areas is Turbopuffer most effectively used?
Turbopuffer is effectively used in fields that require high-performance similarity search. This includes, AI applications, semantic search, and recommendation systems.
How does automated scaling in Turbopuffer work?
I am unable to provide specific details on how automated scaling works in Turbopuffer based on the information on their website. However, this feature likely enables Turbopuffer to dynamically adjust to changes in workload ensuring optimal performance.
Can you explain the concept of 'namespace branching' in Turbopuffer?
Namespace branching in Turbopuffer is a unique feature that likely enhances both its flexibility and performance. The exact details of how 'namespace branching' is implemented or functions is not explicitly divulged on their website.
What is the response latency for Turbopuffer?
Turbopuffer's latency for responses is remarkably low, with a median duration (p50) of under 10 milliseconds. This contributes to a fast and smooth user experience.
What makes Turbopuffer a cost-effective choice?
Turbopuffer is cost-effective due to its fundamental design around object storage, which is cheaper compared to alternatives. It's cited to be 10 times cheaper than conventional vector databases whilst offering equivalent or superior performance metrics.
How does Turbopuffer handle similarity searches?
Turbopuffer handles similarity searches by providing high-performance functionality. It executes these searches quickly, efficiently, and scales smoothly due to its software architecture designed around object storage.
Who are some notable clients using Turbopuffer?
Turbopuffer is employed by a number of leading companies. These include Cursor, Anthropic, Notion, Atlassian, Ramp, Cognition, Clay, Legora, Linear, Grammarly, Superhuman, Harvey, Granola, Pylon, Telus, and Readwise, among others.
turbopuffer from $64/mo
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