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Databases · head to head

turbopuffer vs Valkey

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
Valkey logo

Valkey

Databases

Open-source in-memory data store forked from Redis

From
Free
Rated
-
Full reviewWebsite

The short version

  • Only Valkey 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.; Valkey younger project, so its track record is short even though the codebase is not
  • They diverge on capability: turbopuffer covers Object storage architecture, Valkey covers Redis-compatible.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which turbopuffer and Valkey actually diverge.

Attributes where turbopuffer and Valkey differ
AttributeturbopufferValkey
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
  • Vector search
  • Full-text search
  • Attribute filtering
  • Documented limits
  • Configurable consistency
  • Durable writes

Only in Valkey

  • Redis-compatible
  • BSD licensed
  • Rich data structures
  • Replication and persistence

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

Valkey

  • Continuing on a permissively licensed in-memory store after the Redis licence changenot turbopuffer
  • Caching and session storage where a foundation-governed project is a procurement requirementnot turbopuffer
  • Migrating from Redis without rewriting application codenot 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.

Valkey

  • Younger project, so its track record is short even though the codebase is not
  • Divergence from Redis grows over time, so compatibility is strongest near the fork point and weakens as both evolve
  • Ecosystem tooling and documentation still frequently assume Redis, leaving translation work

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

Valkey

Free
  • ValkeyFree
    • 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 Valkey if

  • You need redis-compatible.
  • You want to start without paying.
  • You work on Linux, macOS, Docker, Self-hosted.
  • You also want bsd licensed.
Valkey review

Questions people ask

Is turbopuffer or Valkey better?
Neither clearly leads. turbopuffer starts at $16/month and Valkey at Free, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, turbopuffer or Valkey?
Valkey has a free tier; the other does not. Paid plans start at $16/month for turbopuffer and Free for Valkey.
Does turbopuffer or Valkey run on more platforms?
turbopuffer runs on Web. Valkey runs on Linux, macOS, Docker, Self-hosted.
Can I use Valkey for free?
Yes. Valkey 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 Valkey is typically brought in for.
What can turbopuffer do that Valkey cannot?
turbopuffer covers Object storage architecture, Namespaces, Vector search, Full-text search. Valkey covers Redis-compatible, BSD licensed, Rich data structures, Replication and persistence.

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.

Valkey: Is Valkey free?

Yes, BSD-licensed open source under the Linux Foundation.

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.

Valkey: Why does Valkey exist?

Redis changed its licence away from BSD in 2024. Valkey is the community fork continuing under permissive terms, backed by AWS, Google Cloud and Oracle among others.

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.

Valkey: Can I switch from Redis to Valkey?

At the fork point it is drop-in compatible with existing clients and data. The further both projects move from that point, the more you should verify the specific features you use.

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.

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