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Databases · head to head

Marqo vs turbopuffer

Marqo logo

Marqo

Databases

AI-native product discovery platform for ecommerce

From
On request
Rated
-
Full reviewWebsite
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

The short version

  • Each has a real cost: Marqo pricing not publicly available, enterprise sales-only model; 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.
  • They diverge on capability: Marqo covers Semantic search, turbopuffer covers Object storage architecture.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Marqo and turbopuffer actually diverge.

Attributes where Marqo and turbopuffer differ
AttributeMarqoturbopuffer
Starting priceOn request$16/month
Pricing modelcontact-salessubscription
PlatformsWeb API, SaaSWeb
Founded2022Unknown

Identical on both: free tier (No), 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 Marqo

  • Semantic search
  • Multimodal image search
  • AI model training
  • Automated merchandising
  • Multi-surface integration
  • Commerce platform integrations
  • API access

Only in turbopuffer

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

What people use each for

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

Marqo

  • Improve search revenue for fashion and beauty retailersnot turbopuffer
  • Implement multimodal image and product searchnot turbopuffer
  • Increase checkout conversion through better product discoverynot turbopuffer
  • Create personalized product recommendationsnot turbopuffer
  • Reduce customer support inquiries with relevant search resultsnot turbopuffer

turbopuffer

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

Where each one falls short

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

Marqo

  • Pricing not publicly available, enterprise sales-only model
  • Requires custom implementation and integration work
  • Limited to ecommerce use cases compared to general search
  • Model training requires sufficient customer behavior data

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.

Pricing, plan by plan

Marqo

On request

No published plan breakdown. See the Marqo review.

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

Which should you pick?

Choose Marqo if

  • You need semantic search.
  • You work on Web API, SaaS.
  • You also want multimodal image search.
Marqo review

Choose turbopuffer if

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

Questions people ask

Is Marqo or turbopuffer better?
Neither clearly leads. Marqo starts at On request and turbopuffer at $16/month, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Marqo or turbopuffer?
Marqo starts at On request and turbopuffer at $16/month.
Does Marqo or turbopuffer run on more platforms?
Marqo runs on Web API, SaaS. turbopuffer runs on Web.
What is Marqo best used for?
Marqo is most often used for improve search revenue for fashion and beauty retailers, implement multimodal image and product search, increase checkout conversion through better product discovery, create personalized product recommendations. Of those, improve search revenue for fashion and beauty retailers and implement multimodal image and product search are not what turbopuffer is typically brought in for.
What can Marqo do that turbopuffer cannot?
Marqo covers Semantic search, Multimodal image search, AI model training, Automated merchandising. turbopuffer covers Object storage architecture, Namespaces, Vector search, Full-text search.

Answered from the vendors’ own pages

Marqo: How do Marqo models get trained?

Marqo trains dedicated AI models on each retailer's product catalog and customer behavior data including clicks, purchases, and browsing patterns. This ensures models are optimized for the specific retailer's products and customers.

Source
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.

Marqo: What results can retailers expect from Marqo?

Enterprise retailers using Marqo report 10-23% increases in search revenue and conversion rates, with proven results across fashion, beauty, electronics, and home goods.

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.

Marqo: Which ecommerce platforms does Marqo integrate with?

Marqo has native integrations with Shopify, Adobe Commerce (Magento), and Salesforce Commerce Cloud, and also provides API access for custom implementations.

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.

Marqo: How is Marqo priced?

Marqo pricing is customized based on catalog size, search volume, and feature requirements. Contact their sales team to book a demo and discuss pricing.

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.

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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