Turbopuffer is live · pricing from $16/mo · last verified 24 Oct 2026 · viability healthy
Turbopuffer
Vector and full-text search on object storage, with sub-10ms warm queries and pricing roughly 10x under compute-based vector databases.
The published warm-vs-cold gap is the whole product, and turbopuffer is upfront about it — that candor is why the buy is credible. Repeat-heavy retrieval at scale gets a rare price-to-throughput ratio here. Cache-hostile workloads get p90 latency in the second, so benchmark your own access pattern before committing.
Verdict generatedVerified 6d ago · liveness 82/100 · cite: rightaichoice.com/tools/turbopuffer
- AI engineering teams running RAG over hundreds of millions to billions of vectors on a tight cost budget
- Multi-tenant SaaS that needs namespace-per-customer isolation without dedicating compute per tenant
- Code search or document search that mixes vector and BM25 in one index with one bill
- High-throughput first-stage retrieval pushing tens of thousands of queries per second with repeat traffic
- Interactive apps with spiky, cache-hostile traffic — the vendor's own cold benchmark shows p90 above a second
- Projects with a few thousand vectors, where SQLite or fixed-cost compute is simpler and cheaper
- Teams that need SQL or ACID transactions across their search data
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Skip Turbopuffer if your queries rarely repeat the same data — the docs report p90 1214ms on cold queries over 1M documents, so cache-hostile interactive workloads will hit latency you can't tune away.
Scale and Enterprise add audit log streams at +$128/mo on top of the plan minimum, which is easy to miss when comparing tiers.
Launch's $16/month minimum usage makes turbopuffer cheaper to start than most managed vector databases, and Scale's $256/month is still below the entry commitments typical of Pinecone or Weaviate enterprise tiers. Enterprise at $4,096/month plus a 35% usage premium is priced for single-tenancy, BYOC and a 99.95% uptime SLA — if you don't need those, staying on Scale avoids the premium entirely.
In short
Turbopuffer — Vector and full-text search on object storage, with sub-10ms warm queries and pricing roughly 10x under compute-based vector databases. Best for AI engineering teams running RAG over hundreds of millions to billions of vectors on a tight cost budget, Multi-tenant SaaS that needs namespace-per-customer isolation without dedicating compute per tenant, Code search or document search that mixes vector and BM25 in one index with one bill. Plans from $16/mo.
No head-to-head comparison for Turbopuffer yet — the alternatives below are the closest matches we list.
What is Turbopuffer?
Turbopuffer — Vector and full-text search on object storage, with sub-10ms warm queries and pricing roughly 10x under compute-based vector databases. Best for AI engineering teams running RAG over hundreds of millions to billions of vectors on a tight cost budget, Multi-tenant SaaS that needs namespace-per-customer isolation without dedicating compute per tenant, Code search or document search that mixes vector and BM25 in one index with one bill. Plans from $16/mo.
Turbopuffer is a vector and full-text search database whose only durable store is object storage — S3 — with a memory/NVMe cache in front of it to absorb reads. That architecture drives both the bill and the latency, and the pages publish both sides of it: warm namespaces answer at sub-10ms p50 for vector search, while unpinned data runs well past a second at p90 on the benchmark posted today (1,214ms p90 on a 10M-doc, ~40GB workload). It's built for AI engineering teams running retrieval at volume: RAG, semantic and code search, recommendations, multi-tenant SaaS. The site reports production traffic of 1T+ documents, 10M+ writes/s and 100k+ queries/s, with Anthropic, Notion, Atlassian, Linear, Harvey, Cognition, Granola, Bridgewater Associates and The New York Times among the named customers. Capability spans approximate nearest neighbor vector search at 90-100% recall@10, BM25 full-text search, and hybrid vector-plus-BM25 through server-side rerank_by. Metadata filtering sits on an inverted index with ordering across up to 8 attributes, and regex, glob and IGlob filters are backed by trigram indexes. Recent releases added native embedding (GA with four models in August 2026), native reranking with Jev and Voyage Rerank 3 in private beta, namespace sharding to 256TB in a single index (GA September 2026), i8 quantization at roughly 75% lower storage and query cost than f32, copy-on-write namespace branching, offset pagination, read-only namespaces, sparse vectors, and a 2025-2026 SOC 2 Type 2 report. Pricing is consumption-based against a monthly minimum — $16/month minimum usage on launch — with the same database features on every plan. Against Pinecone or Weaviate you trade always-on compute for cache-warm speed and a much lower floor.
Key features
- Approximate nearest neighbor vector search at 90-100% recall@10
- BM25 full-text search with word_v4 tokenizer, ~3x faster than word_v3
- Hybrid vector plus BM25 search via server-side rerank_by
- Native embedding with four embedding models (GA August 2026)
- Native reranking with Jev and Voyage Rerank 3 (private beta)
- Namespace sharding to 256TB in a single index (GA September 2026)
- i8 vector quantization at roughly 75% lower storage and query cost than f32
- Copy-on-write namespace branching for instant clones and test environments
- Metadata filtering on an inverted index, ordered by up to 8 attributes
- Regex, glob and IGlob filters backed by trigram indexes
- Typo-tolerant fuzzy filter with case-insensitive substring matching
- Passage highlighting of text matching a query (beta)
- Offset-based pagination and limit.per result diversification
- Sparse vector search and multiple vectors per document
- Read-only namespaces and a bytes type for arbitrary binary payloads
Behind the verdict
We'd reach for turbopuffer when retrieval traffic repeats enough to keep namespaces pinned. The published production numbers — 1T+ documents, 100k+ queries/s, sub-10ms p50 warm — are the shape of workload it was priced for, and the consumption model has a low enough floor that you can test the same system you'll scale on. Where it bites is any app that asks for a different slice of the corpus on every request. The vendor's own benchmark puts p90 at 1,214ms on a cold 10M-document namespace against 17ms warm. Run your query distribution through their cost calculator and their benchmark before you sign anything; that single graph decides whether this is a bargain or a latency incident. Multi-tenant SaaS is the cleanest fit. Namespace-per-customer isolation without dedicating compute per tenant is exactly what the object-storage design buys you, and copy-on-write branching gives you cheap clones for staging and eval. Versus Pinecone and Weaviate, the honest comparison is not features — it's the cost curve. Those products keep compute warm for you; turbopuffer makes you earn warmness and charges far less when you do. Versus pgvector, stay on Postgres until you're past a few million vectors or until BM25 and hybrid search stop being an afterthought. Watch the roadmap items still landing: rank-by-attribute and rank-by-distance, aggregate functions, late interaction and faster cache warming are listed but not all shipped. Native embedding and reranking in-database are attractive for cutting a hop out of the pipeline, yet reranking is still private beta, so plan a fallback. One caveat for procurement: the plan minimums don't roll over and the enterprise tier adds a 35% usage premium on top of a $4,096/month floor. That premium buys private networking, CMEK, 24/7 support
Limitations
- Cold queries on uncached or unpinned data are slower than the sub-10ms p50 warm figure, so latency-sensitive apps need warm caches or pinning.
- Writes go to object storage (the docs cite p90 248ms for 512KB upserts) rather than an in-memory index.
- Pinned namespaces are capped at 256, limiting how many tenants can stay warm in large multi-tenant deployments.
- Enterprise pricing carries a 35% usage premium above the $4,096/month minimum, and namespace sharding is needed to scale a single namespace beyond 256TB.
as of 2026-10-19
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Is Turbopuffer still active?
Turbopuffer is live · pricing from $16/mo · last verified 24 Oct 2026 · viability healthy.
Viability score
How well maintained and how widely used is Turbopuffer? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →What's new in Turbopuffer
Checked 11 days agoAcross the latest 5 updates: 3 feature updates, 1 pricing change and 1 changelog entry.
Turbopuffer roadmap update: cache warming, dashboard, full-text search features
Roadmap lists faster cache warming, dashboard improvements, more full-text search features (rank by attribute, rank by distance, highlighting, fuzzy), aggregate functions, late interaction, multi-vector columns and namespace branching/sharding work.
Sharding GA, native reranking private beta, offset pagination, SOC 2 Type 2
Namespace sharding reaches general availability, scaling a single namespace to 256TB. Native reranking with Jev and Voyage Rerank 3 enters private beta, offset-based pagination ships, and the 2025-2026 SOC 2 Type 2 report is available.
Native embedding GA, Azure region preview, read-only namespaces
Native embedding is generally available with four new embedding models, the first public Azure region (azure-eastus2) enters private preview, read_only namespaces ship, and results can be ordered by up to 8 attributes.
Native embedding beta, namespace sharding beta, passage highlighting
Native embedding enters opt-in beta, namespace sharding beta scales a single namespace to 256TB, passage highlighting ships in beta, RRF reranker weighting arrives, and Google Cloud Marketplace commit drawdown goes live.
Launch plan minimum cut to $16/month, i8 vector type added
The Launch plan minimum usage drops from $64 to $16/month. A new i8 vector type claims roughly 75% lower storage and query cost than f32, and IP allowlisting ships.
What do users say about Turbopuffer?
What people actually say about Turbopuffer — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
Researched57 mentions across 3 sources (Hacker News, YouTube, Lemmy) · researched Aug 21, 2026.
Average across the 3 sources that answered — each source counts once, not each post.
- +Sub-10ms p50 cached latency on warm namespaces.
- +Dramatically cheaper search at scale — community references 10x vs Pinecone.
- +Hybrid search combining vector and BM25 works out of the box.
- +Built on object storage for automatic petabyte-scale scaling.
- +i8 vector quantization cuts storage cost by 75% versus f32.
- −Name is off-putting; some users question company legitimacy.
- −Vector DB approach seen as wrong for code intelligence in coding AIs.
- −Lacks LSP integration and branch-aware retrieval per YouTube feedback.
- −Limited community presence outside Hacker News and YouTube.
- −Cost advantage not yet validated by third-party benchmarks.
- • Though minimums are low, actual costs scale with usage, and users need to validate 10x savings against their own dataset.
- • Native embeddings are in private beta, so extra costs may surface once GA.
How we verified this
Every listed fact about Turbopuffer— pricing, features, links, status — is re-read from the vendor's own pages by our verification pipeline and each change is logged with a date; the last pass ran on 24 Oct 2026. Pages mix model-assisted synthesis with human review; see how we verify.
Verification history
We have re-verified Turbopuffer 21 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — changed description, features, summary, our verdict, who should skip it, who it suits, pricing tiers
- — changed limitations, pricing page
- — changed switching in, our verdict, who it suits, features, who should skip it, pricing fit, setup time, switching out, description, use cases, limitations, pricing tiers, hidden costs, summary, SSL certificate
- — changed features, pricing tiers, our verdict, who it suits, summary, description, who should skip it
- — changed pricing page
- — changed who should skip it, who it suits, our verdict, features, pricing tiers, summary, description
Showing the 6 most recent of 21 verification passes.
Free to cite with attribution — this page re-verifies continuously.
How much does Turbopuffer cost in 2026?
Turbopuffer: Plans from $16/mo. Pricing last verified 24 Oct 2026.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Where the pricing makes sense
The company stage and team size where Turbopuffer's pricing actually pencils out — and where peers do it cheaper.
Launch's $16/month minimum usage makes turbopuffer cheaper to start than most managed vector databases, and Scale's $256/month is still below the entry commitments typical of Pinecone or Weaviate enterprise tiers. Enterprise at $4,096/month plus a 35% usage premium is priced for single-tenancy, BYOC and a 99.95% uptime SLA — if you don't need those, staying on Scale avoids the premium entirely.
Has Turbopuffer's pricing changed?
No plan price for Turbopuffer has moved since we began logging in Aug 2026; the pricing page is re-read on every verification pass.
Who should pick Turbopuffer?
Pick Turbopuffer if you are AI engineering teams running RAG over hundreds of millions to billions of vectors on a tight cost budget, Multi-tenant SaaS that needs namespace-per-customer isolation without dedicating compute per tenant, Code search or document search that mixes vector and BM25 in one index with one bill, High-throughput first-stage retrieval pushing tens of thousands of queries per second with repeat traffic.
Look elsewhere if you are Interactive apps with spiky, cache-hostile traffic — the vendor's own cold benchmark shows p90 above a second, Projects with a few thousand vectors, where SQLite or fixed-cost compute is simpler and cheaper, Teams that need SQL or ACID transactions across their search data, Air-gapped or on-prem-only deployments — the architecture assumes object storage.
Skip it if Skip Turbopuffer if your queries rarely repeat the same data — the docs report p90 1214ms on cold queries over 1M documents, so cache-hostile interactive workloads will hit latency you can't tune away.
GeneratedReal-world workflow fit
Concrete scenarios for the personas Turbopuffer actually fits — and what changes day-one when you adopt it.
You upsert a few million chunks with native embedding enabled so the text never leaves the database for a separate embedding API call, then query with hybrid vector + BM25 through rerank_by.
Outcome: One index, one bill, and a shorter request path — the docs' warm-namespace profile of 14ms p50 on a 1024-dimension, 10M-document workload is the number to benchmark against.
You give each customer a namespace and pin the busiest 256 to cache, using namespace sharding if a single large tenant outgrows the 128B-document per-namespace ceiling.
Outcome: Tenant isolation without per-tenant compute — the cost scales with data and queries rather than with how many customers you sign.
You branch a production namespace copy-on-write, tune the RRF reranker weights and BM25 k3 on the clone, and compare recall@10 before promoting the change.
Outcome: Ranking experiments run against real data at full scale without duplicating terabytes of vectors or disturbing live traffic.
Use cases
- Cost-effective semantic search over a knowledge base storing billions of embeddings.
- Retrieval stage of a RAG pipeline for an AI assistant with high write throughput.
- Search across millions of support tickets with hybrid vector + BM25 retrieval.
- Recommendation systems combining metadata filters with vector similarity.
- Full-text product catalog search with typo tolerance and ranking boosts.
- Test ranking changes without duplicating data using copy-on-write namespace branching.
- First-stage retriever for agentic AI workflows at 100k+ queries/s globally.
- Multi-tenant SaaS search with a namespace per customer and no per-tenant compute overhead.
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.
A single-developer prototype reaches first query in well under an hour following the quickstart — create an organization, generate an API key, write a batch, query it. A production RAG pipeline with embedding, hybrid retrieval and cache pinning is a few days of work. Multi-tenant rollouts with SSO, audit logs and private networking are a one-to-two-week migration from an existing vector store.
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.
- →From Pinecone: export vectors and metadata, re-upsert through the write API, and keep the same embedding model to preserve recall during the cutover.
- →From pgvector: read rows out of Postgres, write them as turbopuffer batches, and move filtered queries onto the inverted index with typed attributes.
- →From Weaviate: map each class to a namespace, translate where filters to turbopuffer filter syntax, and use copy_from_namespace for a parallel-run test.
- →From Elasticsearch or OpenSearch: recreate BM25 full-text using the word_v4 tokenizer, and move any kNN workload alongside it in the same namespace.
- →From another turbopuffer region or org: use copy_from_namespace, which supports cross-region and cross-org copies and can encrypt with a different key at the destination.
- ↗To pgvector: export documents via the export API, load into Postgres with the pgvector extension, and rebuild filters as SQL where clauses.
- ↗To Pinecone: export embeddings and metadata, re-upsert into a Pinecone index, and re-implement hybrid search since BM25 ranking doesn't port directly.
- ↗To an in-memory engine like Qdrant: export and reload, accepting that you now pay for always-on compute to eliminate the cold-query tail.
Integrations
Resources & guides
Tutorials & Learning
YouTube returned 6 videos for “Turbopuffer”, and we withheld 6: 6 could not be judged, because “Turbopuffer” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Turbopuffer.
Official links
What are the best Turbopuffer alternatives?
The closest alternatives to Turbopuffer we list are Pinecone, pgvector and Weaviate. Ranked by direct product-type match, not category overlap.
| Alternative | Pricing | Starts at | Last verified |
|---|---|---|---|
| Pinecone | Freemium | Pricing not published | — |
| pgvector | Free | Free | — |
| Weaviate | Freemium | Pricing not published | — |
Alternatives to Turbopuffer
View all
Tools that pair well with Turbopuffer
Common stack mates teams adopt alongside Turbopuffer, with the specific reason each pairing earns its keep.
Pinecone
Fully managed vector database and knowledge platform: search, upsert, and rerank from one index and one API key.
pgvector
pgvector adds vector similarity search inside Postgres, so embeddings live next to your relational data.
Weaviate
Weaviate is an open-source AI database for vector search, hybrid search, and RAG with built-in embeddings and agent memory.
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