Softwr

Databases · head to head

turbopuffer vs Typesense

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
Typesense logo

Typesense

Databases

Open-source typo-tolerant search engine as an Algolia alternative

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Typesense 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.; Typesense holding the index in memory caps dataset size by available RAM, which becomes expensive at scale
  • They diverge on capability: turbopuffer covers Object storage architecture, Typesense covers In-memory index.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which turbopuffer and Typesense actually diverge.

Attributes where turbopuffer and Typesense differ
AttributeturbopufferTypesense
Starting price$16/monthFree
Pricing modelsubscriptionOpen source, no licence fee; managed cloud billed separately
Free tierNoYes
PlatformsWebLinux, macOS, Docker, Self-hosted

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 Typesense

  • In-memory index
  • Typo tolerance
  • Faceting and filtering

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 Typesense
  • Very large corpora where holding every vector in memory is the dominant cost and occasional cold-query latency is acceptablenot Typesense
  • Hybrid retrieval combining BM25 and vector search where running and synchronising two separate systems is the problem being solvednot Typesense
  • Retrieval for agent and assistant products where indexes are created and destroyed frequently and per-index overhead must be near zeronot Typesense

Typesense

  • Replacing Algolia when per-search pricing outgrows the valuenot turbopuffer
  • Instant search over a product catalogue or documentation sitenot turbopuffer
  • Hybrid keyword and vector search without running two systemsnot 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.

Typesense

  • Holding the index in memory caps dataset size by available RAM, which becomes expensive at scale
  • Narrower than Elasticsearch by design: no log analytics or complex aggregation pipelines
  • Smaller ecosystem and community than Algolia or Elasticsearch, so fewer integrations exist off the shelf

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

Typesense

Free
  • TypesenseFree
    • Full functionality
    • Self-hosted
    • No usage limits

Which should you pick?

Choose turbopuffer if

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

Choose Typesense if

  • You need in-memory index.
  • You want to start without paying.
  • You work on Linux, macOS, Docker, Self-hosted.
  • You also want typo tolerance.
Typesense review

Questions people ask

Is turbopuffer or Typesense better?
Neither clearly leads. turbopuffer starts at $16/month and Typesense at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, turbopuffer or Typesense?
Typesense has a free tier; the other does not. Paid plans start at $16/month for turbopuffer and Free for Typesense.
Does turbopuffer or Typesense run on more platforms?
turbopuffer runs on Web. Typesense runs on Linux, macOS, Docker, Self-hosted.
Can I use Typesense for free?
Yes. Typesense 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 Typesense is typically brought in for.
What can turbopuffer do that Typesense cannot?
turbopuffer covers Object storage architecture, Namespaces, Full-text search, Attribute filtering. Typesense covers In-memory index, Typo tolerance, Faceting and filtering. 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.

Typesense: Is Typesense free?

The engine is open source and free to self-host. Typesense Cloud is a paid managed option.

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.

Typesense: Why choose Typesense over Algolia?

Cost and control. Algolia charges per search and per record; Typesense can be self-hosted with no per-query fee, at the cost of running it yourself.

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.

Typesense: Does Typesense support vector search?

Yes, including hybrid search combining keyword and semantic matching in one query.

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.

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.

Share

Related pages

More on turbopuffer

More on Typesense

Keep looking

Other head to heads