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Databases · head to head

Chroma vs Marqo

Chroma logo

Chroma

Databases

Apache 2.0 vector and full-text search engine that runs as an embedded library, a single server or a distributed cloud service.

From
Free
Rated
-
Marqo logo

Marqo

Databases

AI-native product discovery platform for ecommerce

From
On request
Rated
-

The short version

  • Only Chroma has a free tier, so it costs nothing to try first.
  • Each has a real cost: Chroma on a single node, available memory sets a hard upper bound on collection size, roughly 245,000 records per gigabyte of RAM at 1024 dimensions, so capacity planning is a memory purchase and the ceiling arrives without warning.; Marqo pricing not publicly available, enterprise sales-only model
  • They diverge on capability: Chroma covers Embedded mode, Marqo covers Semantic search.
  • Prices and features above were last checked on 30 August 2026.

Where they differ

Only the attributes on which Chroma and Marqo actually diverge.

Attributes where Chroma and Marqo differ
AttributeChromaMarqo
Starting priceFreeOn request
Pricing modelusage-basedcontact-sales
Free tierYesNo
PlatformsWebWeb API, SaaS
FoundedUnknown2022

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 Chroma

  • Embedded mode
  • Single-node server
  • Distributed architecture
  • Vector search
  • Full-text search
  • Metadata filtering
  • Consistent API across modes
  • Multi-language clients

Only in Marqo

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

What people use each for

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

Chroma

  • Prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent storenot Marqo
  • Agent memory in a single application process, where an embedded store avoids adding a network dependencynot Marqo
  • A departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroomnot Marqo
  • Local and CI testing of retrieval code with the same client library used in productionnot Marqo

Marqo

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

Where each one falls short

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

Chroma

  • On a single node, available memory sets a hard upper bound on collection size, roughly 245,000 records per gigabyte of RAM at 1024 dimensions, so capacity planning is a memory purchase and the ceiling arrives without warning.
  • Single-node queries parallelise only up to the number of vCPUs, after which requests queue and latency rises linearly with concurrency, so throughput problems appear as a slow application rather than as errors.
  • The distributed deployment behind Chroma Cloud is a different architecture from the embedded library, so latency, consistency and failure behaviour observed in a local prototype do not predict production behaviour.
  • The open source server has no built-in authentication or multi-tenancy worth relying on, so a self-hosted deployment needs its own auth proxy and network controls before anything untrusted can reach it.
  • The project has moved quickly through major internal rewrites and version changes, so upgrades have historically involved data migrations and client changes, and pinning versions is necessary rather than cautious.

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

Pricing, plan by plan

Chroma

Free
  • StarterFree
    • 10 databases
    • 10 team members
    • Community Slack access
  • Team$250/month
    • 100 databases
    • 30 team members
    • $100 in included credits
  • Enterprise$null/month
    • Unlimited databases
    • Unlimited team members
    • Dedicated support

Marqo

On request

No published plan breakdown. See the Marqo review.

Which should you pick?

Choose Chroma if

  • You need embedded mode.
  • You want to start without paying.
  • You also want single-node server.

Choose Marqo if

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

Questions people ask

Is Chroma or Marqo better?
Neither clearly leads. Chroma starts at Free and Marqo at On request, and user ratings are close enough to be indistinguishable. Choose on capability and platform support.
Which is cheaper, Chroma or Marqo?
Chroma has a free tier; the other does not. Paid plans start at Free for Chroma and On request for Marqo.
Does Chroma or Marqo run on more platforms?
Chroma runs on Web. Marqo runs on Web API, SaaS.
Can I use Chroma for free?
Yes. Chroma has a free tier, so you can try it without paying. Marqo starts at On request.
What is Chroma best used for?
Chroma is most often used for prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent store, agent memory in a single application process, where an embedded store avoids adding a network dependency, a departmental search application under roughly ten million records where one server is sufficient and simplicity is worth more than headroom, local and ci testing of retrieval code with the same client library used in production. Of those, prototyping retrieval-augmented generation where the priority is having a working index in minutes rather than choosing a permanent store and agent memory in a single application process, where an embedded store avoids adding a network dependency are not what Marqo is typically brought in for.
What can Chroma do that Marqo cannot?
Chroma covers Embedded mode, Single-node server, Distributed architecture, Vector search. Marqo covers Semantic search, Multimodal image search, AI model training, Automated merchandising.

Answered from the vendors’ own pages

Chroma: Do I need to run a server?

No. Chroma runs embedded in your process with persistence to a local directory, which is how most projects start. The server and distributed modes exist for when multiple clients or larger collections require them.

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
Chroma: How large can a single node get?

The project puts single-node deployments at fewer than about ten million records across a handful of collections, with collection size bounded by system memory at roughly 245,000 records per gigabyte at 1024 dimensions.

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
Chroma: Is Chroma Cloud the same software?

It is the same API and project, but the distributed deployment is a different architecture, using independent services, object storage and SSD caches rather than a single process. Behaviour under load differs accordingly.

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
Chroma: How does it compare with pgvector?

pgvector keeps vectors in a Postgres database you already operate, with SQL, joins and transactions. Chroma is a dedicated retrieval engine with a lower setup cost and a retrieval-shaped API. If you already run Postgres, pgvector removes a system; if you do not, Chroma removes a decision.

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
Chroma: What licence is it under?

Apache 2.0, which permits self-hosting and embedding in commercial products without a competing-use restriction.

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