turbopuffer

New

Vector and full-text search built on object storage

Vector DatabasesOttawa, Ontario, Canada11–50 employeesFounded 2023

#14 of 30 in Vector Databases 33% positive in AI answers

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Visibility score

4.6%

New vs last week

Weekly run · Nov 2

Data collected Nov 2, 2026·ChatGPT, Perplexity, Google AI Mode·coverage 100%·next run Nov 9, 2026

turbopuffer ranks #14 of 30 in Vector Databases with an AI visibility score of 4.6 (SlateIndex, Nov 2, 2026).
turbopuffer is most visible on ChatGPT (6.7) and least on Perplexity (2.8).
AI engines name turbopuffer in 5 of the 30 Vector Databases buyer prompts SlateIndex tracks.
github.com is the source AI engines cite most in answers about turbopuffer, with 6 citations.

About

Company details for turbopuffer

turbopuffer is a vector and full-text search database built on object storage. It keeps data in low-cost object storage and caches actively searched data on NVMe SSD and in memory. It offers automatic scaling, hybrid search and metadata filtering, and is priced by usage. It ranks #14 of 30 in Vector Databases on SlateIndex and has a visibility score of 4.6 in the latest weekly run.

Founded in 2023 and headquartered in Ottawa, Ontario, Canada, the company employs 11–50 people. Its differentiator: built from first principles on object storage, caching only actively searched data, which makes it up to 10x cheaper than alternatives.

Website: turbopuffer.com
Categories: Vector Databases
Company size: 11–50 employees
Headquarters: Ottawa, Ontario, Canada
Founded: 2023
Company type: Private, venture-backed
Funding: Seed
Pricing: $16 / mo · usage-based
Free trial: —
Deployment: Multi-tenant cloud on usage-based plans; single-tenant and bring-your-own-cloud (BYOC) deployments on the enterprise plan.
Compliance: SOC 2 Type 2, HIPAA (Business Associate Agreement available), GDPR-ready DPA
Support: Community Slack and email on all plans, Private Slack channel on higher plans, 24/7 support and 99.95% uptime SLA on enterprise

X · x.com/turbopuffer LinkedIn · linkedin.com/company/turbopuffer/GitHub · github.com/turbopuffer YouTube · youtube.com/@turbopufferdb

Read from cbinsights.com, linkedin.com, turbopuffer.com, g2.com, x.com, sacra.com · refreshed Nov 2, 2026.

AI visibility

Latest weekly run · Nov 2 · next run Nov 9

Visibility score

4.6%

across Vector Databases answers, weighted by position

ChatGPT6.7%

AI Mode4.3%

Perplexity2.8%

Change

New

vs last week’s run

Category rank

#14 of 30

in Vector Databases

Mentions

in this week’s run

Share of voice

1.9%

of AI mentions in Vector Databases

Visibility score trend

Weekly runs over the last 60 days, with up to 7 competitors. Click a name to hide or show its line.

Now4.6%

30-day change—

High · low4.6% · 4.6%

Rank#14

Changes appear after the second weekly run in Vector Databases. Pinecone leads the category at 71.8%.

Media

Website and product screenshots for turbopuffer

All media

Home page

Pricing page

AI mentions & citations

Recent answers that name turbopuffer

All prompts

Who makes the most used vector database?

Turbopuffer: 2, with 38%.

ChatGPT · run of Nov 2

Official website of the top vector database vendors

Turbopuffer : A serverless vector and full-text search database built from first principles directly on object storage (like S3), offering high performance at a fraction of traditional costs.[](https://turbopuffer.com/)

Google AI Mode · run of Nov 2

Which vector database has transparent pricing?

ChatGPT · run of Nov 2

Alternatives to the most popular vector database

Turbopuffer : A serverless vector search engine built directly on object storage (like S3).

Google AI Mode · run of Nov 2

What are the best Vector Databases tools in 2026?

Perplexity · run of Nov 2

Alternatives to the most popular vector database

Perplexity · run of Nov 2

What are the best Vector Databases tools in 2026?

Turbopuffer – cloud-native, cost-conscious vector search.

ChatGPT · run of Nov 2

Official website of the top vector database vendors

ChatGPT · run of Nov 2

Products & features

What turbopuffer sells

Vector Search

Approximate nearest neighbor search over vectors at large scale.

Full-Text Search

BM25 full-text search.

Hybrid Search

Combines vector and full-text search strategies in one query.

Strengths engines cite

serverless vector and full-text search built on object storage
high performance at a fraction of traditional costs
cloud-native, cost-conscious design
ranked among highest vendors in an industry ranking

Target market & buyers

Companies building AI and search features, from software startups to large enterprises.

Notable customers include Anthropic, Cognition, Notion, Atlassian, Ramp, Bridgewater Associates, Fal, Legora.

Industry	Primary use case
Productivity software	Searching every page, as Notion does.
Legal	Searching every case, as Legora does.
Financial services	Searching every financial document, as Kepler does.

Funding & recent developments

Funding

Round	Year	Amount
Angel	2024	—
Seed	2025	—

Latest known round	Total raised
Seed	—

Recent developments

Sep 2026SecuritySOC 2 Type 2 annual report available
The 2025–2026 SOC 2 Type 2 annual report is listed as available.
Dec 2025FundingSeed round led by Thrive Capital
Undisclosed seed round led by Thrive Capital, with Lachy Groom participating.
Sep 2025ProductIreland region added
turbopuffer added a Ireland region.