Turbopuffer — Fast vector & full-text search built on object storage, 10x cheaper. Best for Semantic search for AI chatbots and RAG pipelines at massive scale, Recommendation systems requiring high-throughput vector search, Multi-tenant search applications with instant namespace branching. Plans from $64/mo.
Semantic search for AI chatbots and RAG pipelines at massive scaleRecommendation systems requiring high-throughput vector searchMulti-tenant search applications with instant namespace branchingCost-sensitive deployments where 10x savings over alternatives matters
Not ideal for
Real-time transactional workloads with frequent updatesApplications needing complex aggregations or SQL-like queriesUse cases requiring multi-modal search (image, audio, etc.)Small-scale projects where simpler tools like Chroma suffice
Turbopuffer is a game-changer for teams needing high-scale vector search without the cost of dedicated infrastructure. Its object-storage-first design delivers exceptional economics, but if you need complex query features or multi-modal support, consider alternatives like Pinecone or Weaviate.
Last verified: June 2026
Behind the Verdict
Turbopuffer stands out for its use of object storage as the primary data layer, dramatically reducing costs compared to traditional vector databases. For high-throughput workloads (10M+ writes/s, 25k+ queries/s), it's proven at scale with known names like Anthropic and Notion. The instant namespace branching is a nice touch for multi-tenant or experiment-heavy environments. However, it's not suited for applications requiring real-time updates or complex aggregations — it's optimized for search, not transactions. If you need multi-modal search (image, audio) or rich indexing beyond vectors + text, look at Pinecone or Weaviate. That said, for pure vector and full-text search at massive scale with a lean budget, turbopuffer is hard to beat. The pricing isn't listed, but given the customer base, it's likely enterprise-tier. Caveat: cold namespace latency (874ms for full-text) could be a pain for less frequent queries.
Skip Turbopuffer if Skip Turbopuffer if you need sub-millisecond latency on every query or require a general-purpose database with transactions and joins.
Latest from Turbopuffer
Updated yesterday
Across the latest 10 updates: 8 feature updates and 2 news mentions.
How likely is Turbopuffer to still be operational in 12 months? Based on 6 signals including funding, development activity, and platform risk.
funding runway
60
website health
90
github activity
50
category mortality
70
wrapper dependency
100
hyperscaler overlap
80
About Turbopuffer
Turbopuffer is a high-performance search engine that combines vector and full-text search, built on object storage for cost-effective scalability. Trusted by companies like Cursor, Anthropic, Notion, and Atlassian, it handles 4T+ documents, 10M+ writes/s, and 25k+ queries/s in production. Key features include sub-10ms p50 latency, automatic scaling, support for billions of vectors, hybrid search, metadata filtering, and namespace branching for instant copy-on-write. Its architecture uses object storage (S3) with a memory/SSD cache, making it 10x cheaper than traditional vector databases. Ideal for AI applications, semantic search, and recommendation systems, turbopuffer offers a unique combination of speed and cost savings.
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Key Features
Vector search (ANN) with 90-100% recall@10
Full-text search using BM25
Hybrid search combining vector and text
Metadata filtering on queries
Built on object storage (S3) with SSD cache
Sub-10ms p50 latency for warm vectors
Automatic scaling to billions of vectors
Instant namespace branching (copy-on-write)
Supports 4T+ documents globally
10M+ writes/s throughput
25k+ queries/s throughput
Pinning up to 256 namespaces
Real-world workflow fit
Concrete scenarios for the personas Turbopuffer actually fits — and what changes day-one when you adopt it.
AI engineer at a mid-stage startup
Building a RAG pipeline for a customer support chatbot, expecting millions of support tickets and high query throughput.
Outcome: Ingest 10M documents into a namespace, configure hybrid search (vector + BM25) with metadata filters, and achieve sub-20ms p50 latency for cached queries, paying $256/month on Scale plan with HIPAA compliance.
Search infrastructure lead at a large SaaS company
Migrating from an in-memory vector database to reduce costs while maintaining performance for a global knowledge base.
Outcome: Migrate 100B+ vectors using copy_from_namespace across regions, use namespace branching to test ranking changes without downtime, and reduce infrastructure costs by 10x.
Founder building an AI-native product catalog search
Need typo-tolerant full-text search over a product catalog with 500M items, currently using Elasticsearch.
Outcome: Set up turbopuffer with Fuzzy filter and FTS v2, achieve 10x lower storage costs than Elasticsearch, and handle 1k+ QPS with sub-50ms p99 latency.
Use Cases
Search across millions of support tickets with hybrid vector + BM25 search.
Build a cost-effective semantic search for a knowledge base storing billions of embeddings.
Power the retrieval stage of a RAG pipeline for an AI assistant with high write throughput.
Implement recommendation systems with metadata filtering and vector similarity.
Enable fast full-text search with typo tolerance for a product catalog.
Use namespace branching to test search ranking changes without duplicating data.
Cold queries (when data is not in cache) can take p90 >1s, making turbopuffer less suitable for latency-sensitive applications that require instant responses on every query. The number of pinned namespaces is limited to 256 by default (custom limits available on Enterprise). Full-text search query length is capped at 8,192 characters. Maximum documents per namespace is 500 million (with 2TB storage), though global totals can be much higher.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Annual total
$768
Over 12 months
Effective monthly
$64
Billed monthly
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Turbopuffer tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Launch
$64/month minimum usage
Ideal for
Small to mid-size teams with predictable query volumes and moderate QPS, needing SOC2 compliance and multi-tenancy.
What this tier adds
Starting tier at $64/month minimum; includes all database features but limited to Community Slack & Email support, and up to 256 pinned namespaces.
Scale
$256/month minimum usage
Ideal for
Growth-stage companies requiring HIPAA readiness, SSO, audit logs, and guaranteed support hours for production workloads.
What this tier adds
Adds HIPAA-ready BAA, SSO, audit logs, and Private Slack Channel support (8-5 hours) over Launch, at $256/month minimum.
Enterprise
>= $4,096/month (35% usage premium)
Ideal for
Large organizations needing single-tenancy, BYOC, CMEK, private networking, and 24/7 support with SLA.
Hidden costs & gotchas
What the public pricing page doesn't put in bold. Captured from pricing-page footnotes, contract terms, and recurring complaints.
•Minimum monthly usage: $64/month on Launch, $256/month on Scale — unused commitment does not roll over.
•Enterprise plan has a 35% usage premium on top of the base $4,096/month minimum.
•Cold queries (uncached) incur higher latency and may cost more if you need to warm frequently.
•Pinning namespaces beyond 256 requires contacting sales for custom pricing.
Where the pricing makes sense
The company stage and team size where Turbopuffer's pricing actually pencils out — and where peers do it cheaper.
Turbopuffer's pricing fits mid-to-large-scale deployments where the cost of in-memory vector databases (like Pinecone or Weaviate) would be prohibitive. With minimums starting at $64/month, it's pricier than serverless alternatives like Qdrant's free tier but cheaper at high volumes due to object storage. The Launch plan is best for teams with predictable, moderate QPS; Scale adds HIPAA and SSO; Enterprise targets workloads needing single-tenancy or BYOC.
Setup time & first value
How long it actually takes to get something useful out of Turbopuffer — broken out by persona, not the marketing-page minute.
You can start querying within minutes: create an API key, sign up, and use the quickstart guide to write and query documents. For a simple namespace with a few million documents, expect <5 minutes to first query. For production-scale migration involving billions of documents and cross-region copies, allow a few hours to tune caching and pinning for your workload.
Switching to or from Turbopuffer
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
Migrating in
→From Pinecone: export vectors as JSON, use batch write API to import into turbopuffer; see migration guide in docs.
→From Weaviate: export object data with vectors, transform to turbopuffer's schema, and bulk import via write API.
→From Elasticsearch: index your documents with BM25 and optionally generate embeddings externally, then load them via write API.
→From Qdrant: export vectors and payloads, use copy_from_namespace if both are in same cloud, or REST API for manual migration.
Migrating out
↗To Pinecone: export documents via export API, transform to Pinecone's format, and bulk import.
↗To Weaviate: export data as JSON, use Weaviate's batch import endpoint.
↗To Elasticsearch: export documents, reindex using Logstash or custom scripts.
↗To Qdrant: export vectors and payloads, use Qdrant's REST API to re-upload.
Recent material changes
Pricing, brand, ownership, or deprecation changes worth knowing before you commit. Most-recent first.
•May 2026: Namespace branching (instant copy-on-write cloning) beta, Fuzzy filter for typo-tolerant matching, and C# API client released.
•April 2026: Pinning namespaces to cache for predictable cost and latency on high-QPS workloads; sparse vector search support; cross-cloud namespace copies (GCP <-> AWS); 3 new regions (São Paulo, South Carolina, Belgium).
•March 2026: Multiple vectors per document feature GA; audit logs with SIEM integration (beta); up to 30% faster AND queries.
•February 2026: Query pricing reduced by up to 94%; Regex index for faster Regex, Glob, IGlob filters; up to 20% faster filtered FTS queries.
Adds single-tenancy or BYOC, CMEK per namespace, private networking, 24/7 support with SLA, and 99.95% uptime SLA; minimum $4,096/month with 35% usage premium.
