Softwr

Databases · head to head

turbopuffer vs Vespa

turbopuffer logo

turbopuffer

Databases

Closed-source vector and full-text search service built directly on object storage, with cold queries measured in seconds rather than milliseconds.

From
$16/month
Rated
-
Full reviewWebsite
Vespa logo

Vespa

Databases

Distributed AI search platform for retrieval, ranking, and inference

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Vespa has a free tier, so it costs nothing to try first.
  • Each has a real cost: turbopuffer a cold namespace pays object storage latency on the first query, with a documented p90 around 1,214 ms on a million documents, so any interactive search box needs the data kept warm or the user waits about a second.; Vespa pricing not publicly listed, requires contacting sales
  • They diverge on capability: turbopuffer covers Object storage architecture, Vespa covers Text and structured search.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which turbopuffer and Vespa actually diverge.

Attributes where turbopuffer and Vespa differ
AttributeturbopufferVespa
Starting price$16/monthFree
Pricing modelsubscriptioncontact-sales
Free tierNoYes
PlatformsWebCloud, Self-hosted
FoundedUnknown2023

Identical on both: user rating (Not yet rated), category (Databases).

What each one covers

Drawn from each product's published feature list. An absence here means we hold no record of it - not that the product lacks it.

Only in turbopuffer

  • Object storage architecture
  • Namespaces
  • Full-text search
  • Attribute filtering
  • Documented limits
  • Configurable consistency
  • Durable writes
  • Multi-query requests

Only in Vespa

  • Text and structured search
  • Machine-learned ranking
  • Real-time serving
  • SQL interface
  • Automatic scaling
  • Open-source

Both cover

  • Vector search

What people use each for

The jobs each tool is most often brought in to do.

turbopuffer

  • A product with one search index per customer and thousands of customers, most of whose data is idle on any given daynot Vespa
  • Very large corpora where holding every vector in memory is the dominant cost and occasional cold-query latency is acceptablenot Vespa
  • Hybrid retrieval combining BM25 and vector search where running and synchronising two separate systems is the problem being solvednot Vespa
  • Retrieval for agent and assistant products where indexes are created and destroyed frequently and per-index overhead must be near zeronot Vespa

Vespa

  • Build RAG systems with semantic search over documentsnot turbopuffer
  • Power e-commerce search with ML rankingnot turbopuffer
  • Create recommendation engines for personalizationnot turbopuffer
  • Implement real-time search for news or feedsnot turbopuffer
  • Deploy private semantic search over sensitive datanot turbopuffer

Where each one falls short

Documented limitations, not opinions. Every one is a constraint you would hit in normal use.

turbopuffer

  • A cold namespace pays object storage latency on the first query, with a documented p90 around 1,214 ms on a million documents, so any interactive search box needs the data kept warm or the user waits about a second.
  • Queries are eventually consistent by default, and after roughly 128 MiB of outstanding writes new data is invisible until indexed, which the vendor puts at tens of seconds for small namespaces and tens of minutes for large ones, so a bulk re-index is not immediately queryable.
  • It is closed source with no community edition, so single-tenant or bring-your-own-cloud deployment is a commercial negotiation rather than a deployment choice, and there is no path to running it yourself if the relationship ends.
  • Per-namespace ceilings, roughly 10,000 writes per second, 32 MB/s and 500 million documents per shard, mean a single enormous index has to be sharded across namespaces by your application rather than by the service.
  • It is a search engine, not a database: there are no joins, no cross-document transactions and no SQL, so it sits beside a primary datastore and keeping the two in step is work that belongs to you.

Vespa

  • Pricing not publicly listed, requires contacting sales
  • Steeper learning curve compared to simpler search tools
  • Operational complexity for self-hosted deployments
  • Smaller ecosystem compared to cloud-native alternatives

Pricing, plan by plan

turbopuffer

$16/month
  • Launch$16/month
    • All database features
    • Multi-tenancy deployment
    • SOC2 & GDPR-ready DPA
  • Scale$256/month
    • Everything in Launch
    • HIPAA-ready BAA
    • Single Sign-On (SSO)
  • Enterprise$4096/month
    • Everything in Scale
    • Single-tenancy & BYOC deployment options
    • Private networking

Vespa

Free

No published plan breakdown. See the Vespa review.

Which should you pick?

Choose turbopuffer if

  • You need object storage architecture.
  • You also want namespaces.
turbopuffer review

Choose Vespa if

  • You need text and structured search.
  • You want to start without paying.
  • You work on Cloud, Self-hosted.
  • You also want machine-learned ranking.
Vespa review

Questions people ask

Is turbopuffer or Vespa better?
Neither clearly leads. turbopuffer starts at $16/month and Vespa at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, turbopuffer or Vespa?
Vespa has a free tier; the other does not. Paid plans start at $16/month for turbopuffer and Free for Vespa.
Does turbopuffer or Vespa run on more platforms?
turbopuffer runs on Web. Vespa runs on Cloud, Self-hosted.
Can I use Vespa for free?
Yes. Vespa has a free tier, so you can try it without paying. turbopuffer starts at $16/month.
What is turbopuffer best used for?
turbopuffer is most often used for a product with one search index per customer and thousands of customers, most of whose data is idle on any given day, very large corpora where holding every vector in memory is the dominant cost and occasional cold-query latency is acceptable, hybrid retrieval combining bm25 and vector search where running and synchronising two separate systems is the problem being solved, retrieval for agent and assistant products where indexes are created and destroyed frequently and per-index overhead must be near zero. Of those, a product with one search index per customer and thousands of customers, most of whose data is idle on any given day and very large corpora where holding every vector in memory is the dominant cost and occasional cold-query latency is acceptable are not what Vespa is typically brought in for.
What can turbopuffer do that Vespa cannot?
turbopuffer covers Object storage architecture, Namespaces, Full-text search, Attribute filtering. Vespa covers Text and structured search, Machine-learned ranking, Real-time serving, SQL interface. Both handle Vector search.

Answered from the vendors’ own pages

turbopuffer: Can I self-host turbopuffer?

There is no open source or community edition. Single-tenant and bring-your-own-cloud deployments exist as commercial arrangements, but there is no way to run it independently of the vendor.

Vespa: Is Vespa open-source?

Yes, Vespa is open-source under the Apache 2.0 license. The code is available on GitHub, and you can self-host or use the managed cloud service.

Source
turbopuffer: How fast is it really?

Warm queries perform comparably to in-memory search engines. Cold queries, where data is not cached, have a documented p90 around 1,214 ms on a million documents. Write p90 is around 248 ms for a 512 KB upsert because writes go straight to object storage.

Vespa: What latency can Vespa achieve?

Vespa is designed for sub-100 millisecond latencies with thousands of queries per second, suitable for real-time search and recommendation applications.

Source
turbopuffer: Is it consistent?

Eventually consistent by default, with the vendor reporting that over 99.8% of queries return consistent data. Strong consistency can be requested per query at a latency cost. Large write bursts have a longer visibility delay while indexing catches up.

Vespa: Does Vespa support vector search?

Yes, Vespa provides native vector search capabilities alongside text, structured data, and tensor operations for building comprehensive search and AI applications.

Source
turbopuffer: What is it best at?

Large numbers of namespaces where most are idle. The architecture makes cold data cheap to keep, which is exactly the shape of a multi-tenant product with a long tail of inactive customers.

Vespa: What is the pricing model for Vespa Cloud?

Vespa Cloud pricing is not publicly listed and requires contacting their sales team to discuss your specific use case and scale requirements.

Source
turbopuffer: What are the hard limits?

Up to 128 billion documents and 256 TB per namespace, 500 million documents per shard, 64 MiB per document, 10,752 dense vector dimensions, roughly 10,000 writes per second per namespace and a maximum result set of 10,000.

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