search every byte

serverless vector and full-text search built from first principles on object storage: fast, 10x cheaper, and extremely scalable

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                        ╔═ turbopuffer ════════════════════════════╗
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║            ║░         ║  ┏━━━━━━━━━━━━━━━┓     ┏━━━━━━━━━━━━━━┓  ║░
║   client   ║░───API──▶║  ┃    Memory/    ┃────▶┃    Object    ┃  ║░
║            ║░         ║  ┃   SSD Cache   ┃     ┃ Storage (S3) ┃  ║░
╚════════════╝░         ║  ┗━━━━━━━━━━━━━━━┛     ┗━━━━━━━━━━━━━━┛  ║░
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      ║   client   ║░
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            ▼
╔═ turbopuffer ═════════════╗
║  ┏━━━━━━━━━━━━━━━━━━━━┓  ║░
║  ┃    Memory/SSD      ┃  ║░
║  ┃      Cache         ┃  ║░
║  ┗━━━━━━━━┳━━━━━━━━━━━┛  ║░
║           ▼              ║░
║  ┏━━━━━━━━━━━━━━━━━━━━┓  ║░
║  ┃    Object Storage  ┃  ║░
║  ┃      (S3)          ┃  ║░
║  ┗━━━━━━━━━━━━━━━━━━━━┛  ║░
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150B+ documents & 6K+ queries/s in prod

Case study

Cost calculator

Workload768 dimensions, 1M docs, ~3GB

p50

16ms

402ms

p90

21ms

524ms

p99

33ms

677ms

Warm namespace

Cold namespace

Approach (3 QPS with topk=10)

turbopuffer/tpuf-benchmark

“

After switching our vector db to @turbopuffer, we're saving an order of magnitude in costs and dealing with far less complexity!

Aman Sanger

Co-founder

Case study

“

turbopuffer's economics have changed the way we think about building products that connect data to users and LLMs.

Akshay Kothari

Co-founder

“

It's been fun to work with @turbopuffer on [the new search]. Admittedly keyword based search never really worked great for issues, and the results from embedding+FTS are now actually useful.

Jori Lallo

Co-founder

Case study

Limits

Metric	Observed in production	Production limits (current)	(upcoming)
Max documents (global)	150B+	Unlimited
Max documents (per namespace)	200M	100M	1B+
Max number of namespaces	40M+	Unlimited
Max write rate (global)	1M+ writes/s	Unlimited
Max write rate (per namespace)	10K writes/s	10K writes/s
Max queries (global)	6K+ queries/s	Unlimited
Max queries (per namespace)	1K+ queries/s	1K+ queries/s	10K queries/s
Vector search recall@10	90-100%	90-100%	Configurable
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