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Marqo
Marqo is an open-source, multimodal vector search engine that lets developers index text and images, generate embeddings on the fly, and run tensor, lexical, and hybrid search through a single REST API. Built on Vespa for storage and retrieval and FastAPI for the HTTP surface, Marqo bundles model inference (Sentence Transformers, OpenCLIP, ONNX) inside the engine so a single `docker run` produces a working semantic search stack. The Apache 2.0 open-source engine has been marked deprecated by the maintainers as Marqo pivots to a hosted ecommerce search product, but the project remains widely forked, downloaded, and self-hosted, with active sibling repositories for the Python client, Terraform provider, InstantSearch client, ecommerce embedding models, and Generalised Contrastive Learning research.
Marqo publishes 7 APIs on the APIs.io network, including Documents API, Embeddings API, Indexes API, and 4 more. Tagged areas include Vector Database, Vector Search, Multimodal, Semantic Search, and Embeddings.
Marqo’s developer surface includes authentication, documentation, getting-started guide, engineering blog, pricing, tooling, code examples, and 18 more developer resources.
7 APIs12 Features5 Use Cases
Vector Database Vector Search Multimodal Semantic Search Embeddings AI Machine Learning Open Source Ecommerce Search
On this page
Kin Score
APIs 7
Open Collections 1
Pricing Plans 1
Rate Limits 1
FinOps 1
Features 12
Security Posture 2
Agentic Access 1
Use Cases 5
Integrations 10
Resources 25
apis.yml
26 Operational Transparency
Composite quality — 38.8/100 · thin
Contract Quality 13.2 / 25
Developer Ergonomics 7.8 / 20
Commercial Clarity 7.9 / 20
Operational Transparency 3.4 / 13
Agent readiness — 32/100 · agent aware
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Individual APIs this provider publishes, each with its own machine-readable definition.
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Open, tool-agnostic API collections (OpenAPI-derived and Bruno).
Published pricing tiers and plan structures.
Documented rate limits and quota policies.
Cost, billing, and metering signals for API financial operations.
Notable capabilities this provider offers.
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Authentication, domain security, vulnerability disclosure, and trust-center signals.
Recommended x-agentic-access execution contracts for AI agents.
What developers build with this provider.
Pre-built integrations with other platforms and tools.
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Get Started 1
Portal, sign-up, and the first successful call
Documentation 1
Reference material describing how the API behaves
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Build 8
SDKs, sample code, and the tooling you integrate with
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Access & Security 2
Authentication, authorization, and security posture
Learn 1
Tutorials, courses, talks, and written guidance
Operate 1
Status, limits, changes, and where to get help
Commercial 4
Pricing, plans, and the legal terms of use
Company 3
The organization behind the API
Other 3
Properties that don't map to a standard resource type
Source (apis.yml)
aid: marqo
name: Marqo
description: Marqo is an open-source, multimodal vector search engine that lets developers index text and images, generate
embeddings on the fly, and run tensor, lexical, and hybrid search through a single REST API. Built on Vespa for storage
and retrieval and FastAPI for the HTTP surface, Marqo bundles model inference (Sentence Transformers, OpenCLIP, ONNX) inside
the engine so a single `docker run` produces a working semantic search stack. The Apache 2.0 open-source engine has been
marked deprecated by the maintainers as Marqo pivots to a hosted ecommerce search product, but the project remains widely
forked, downloaded, and self-hosted, with active sibling repositories for the Python client, Terraform provider, InstantSearch
client, ecommerce embedding models, and Generalised Contrastive Learning research.
type: Index
accessModel:
pricing: unknown
onboarding: self-serve
trial: false
try_now: false
public: false
label: Self-serve signup
confidence: high
source:
- plans
- authentication
generated: '2026-07-22'
method: derived
position: Consumer
access: 3rd-Party
image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/marqo.png
tags:
- Vector Database
- Vector Search
- Multimodal
- Semantic Search
- Embeddings
- AI
- Machine Learning
- Open Source
- Ecommerce Search
url: https://raw.githubusercontent.com/api-evangelist/marqo/refs/heads/main/apis.yml
created: '2026-05-08'
modified: '2026-05-25'
specificationVersion: '0.19'
apis:
- aid: marqo:marqo-documents-api
name: Marqo Documents API
description: Add, update, get, and delete documents in an index.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Documents
properties:
- type: OpenAPI
url: openapi/marqo-documents-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-embeddings-api
name: Marqo Embeddings API
description: Generate embedding vectors using engine-loaded models.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Embeddings
properties:
- type: OpenAPI
url: openapi/marqo-embeddings-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-indexes-api
name: Marqo Indexes API
description: Create, list, inspect, and delete tensor / lexical indexes.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Indexes
properties:
- type: OpenAPI
url: openapi/marqo-indexes-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-models-api
name: Marqo Models API
description: Inspect, load, and eject embedding models from the engine.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Models
properties:
- type: OpenAPI
url: openapi/marqo-models-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-recommendations-api
name: Marqo Recommendations API
description: Return documents similar to one or more reference documents.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Recommendations
properties:
- type: OpenAPI
url: openapi/marqo-recommendations-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-search-api
name: Marqo Search API
description: Tensor, lexical, and hybrid search across an index.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Search
properties:
- type: OpenAPI
url: openapi/marqo-search-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
- aid: marqo:marqo-telemetry-api
name: Marqo Telemetry API
description: Health, readiness, and engine-level metadata.
humanURL: https://docs.marqo.ai/
baseURL: http://localhost:8882
tags:
- Telemetry
properties:
- type: OpenAPI
url: openapi/marqo-telemetry-api-openapi.yml
- type: Documentation
url: https://docs.marqo.ai/
- type: APIReference
url: https://docs.marqo.ai/latest/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: SourceCode
url: https://github.com/marqo-ai/marqo
- type: Deprecation
url: https://github.com/marqo-ai/marqo
common:
- type: AgenticAccess
url: agentic-access/marqo-agentic-access.yml
- type: DomainSecurity
url: security/marqo-domain-security.yml
- type: Authentication
url: authentication/marqo-authentication.yml
- type: Website
url: https://www.marqo.ai/
- type: GitHubOrganization
url: https://github.com/marqo-ai
- type: GitHubRepository
url: https://github.com/marqo-ai/marqo
- type: LinkedIn
url: https://www.linkedin.com/company/marqo-ai
- type: Documentation
url: https://docs.marqo.ai/
- type: GettingStarted
url: https://github.com/marqo-ai/marqo#getting-started
- type: License
url: https://github.com/marqo-ai/marqo/blob/mainline/LICENSE
- type: Blog
url: https://www.marqo.ai/blog/
- type: Pricing
url: https://www.marqo.ai/pricing
- type: Plans
url: plans/marqo-plans-pricing.yml
- type: RateLimits
url: rate-limits/marqo-rate-limits.yml
- type: FinOps
url: finops/marqo-finops.yml
- type: SDKs
name: py-marqo (Python client)
url: https://github.com/marqo-ai/py-marqo
- type: SDKs
name: marqo-instantsearch-client (TypeScript)
url: https://github.com/marqo-ai/marqo-instantsearch-client
- type: Tools
name: terraform-provider-marqo
url: https://github.com/marqo-ai/terraform-provider-marqo
- type: Tools
name: marqo-base (Docker base image)
url: https://github.com/marqo-ai/marqo-base
- type: Tools
name: ingrain_server (Sentence Transformers / CLIP serving)
url: https://github.com/marqo-ai/ingrain_server
- type: Models
name: marqo-ecommerce-embeddings
url: https://github.com/marqo-ai/marqo-ecommerce-embeddings
- type: Models
name: marqo-FashionCLIP
url: https://github.com/marqo-ai/marqo-FashionCLIP
- type: Research
name: Generalised Contrastive Learning (GCL)
url: https://github.com/marqo-ai/GCL
- type: Examples
name: local-image-search-demo
url: https://github.com/marqo-ai/local-image-search-demo
- type: Course
name: Fine-Tuning Embedding Models for Semantic Search
url: https://github.com/marqo-ai/fine-tuning-embedding-models-course
- type: Features
data:
- Open-source Apache 2.0 vector search engine (project marked deprecated by maintainers; still 5,000+ stars and actively
forked)
- Single `docker run` install bundling Vespa storage and embedding model inference
- Tensor, lexical, and hybrid search through a unified REST API
- Multimodal indexing of text and images with on-engine inference
- Embedding generation via Sentence Transformers, OpenCLIP, and ONNX models
- Generalised Contrastive Learning (GCL) framework for fine-tuned retrieval
- Marqo-FashionCLIP and marqo-ecommerce-embeddings open-weight models
- Recommendations, filters, and structured + unstructured fields
- FastAPI runtime exposing live `/openapi.json` and Swagger UI at `/docs`
- Compose files for inference, model management, and Triton-backed serving
- Python client (py-marqo), Terraform provider, and InstantSearch client
- Hosted Marqo Cloud surface preserves API parity for legacy users
sources:
- https://github.com/marqo-ai/marqo
- https://github.com/marqo-ai
updated: '2026-05-25'
- type: UseCases
data:
- name: Multimodal Semantic Search
description: Index text and images into a single tensor store and query with natural-language or image inputs, with embedding
inference running inside the engine.
- name: Retrieval-Augmented Generation
description: Power RAG pipelines by serving the nearest-neighbor retrieval layer for LLM context windows over private
corpora.
- name: Ecommerce Product Discovery
description: Drive product search, recommendations, and merchandising-aware ranking using semantic relevance plus structured
filters and boosts.
- name: Visual Search
description: Search product catalogs and image libraries using image-to-image and text-to-image similarity through OpenCLIP
/ Marqo-FashionCLIP.
- name: Self-Hosted Vector Backend
description: Stand up an Apache 2.0 vector search service alongside your application stack with `docker run`, no separate
embedding service required.
- type: Integrations
data:
- name: Vespa
description: Vespa is the underlying storage and retrieval engine that backs every Marqo index.
- name: Sentence Transformers
description: Default text embedding model family loaded by the engine for tensor search.
- name: OpenCLIP
description: Multimodal text-and-image embedding family used for visual and cross-modal search.
- name: Hugging Face
description: Models are pulled from Hugging Face hubs at first use unless preloaded.
- name: NVIDIA Triton
description: '`compose-triton.yaml` ships a Triton-backed model server profile for GPU inference.'
- name: Terraform
description: terraform-provider-marqo manages Marqo Cloud indexes as infrastructure-as-code.
- name: Algolia InstantSearch
description: marqo-instantsearch-client adapts Marqo to the InstantSearch.js front-end conventions.
- name: Shopify
description: Hosted Marqo product offers one-click integration for Shopify catalogs.
- name: Adobe Commerce
description: Hosted Marqo product offers Adobe Commerce / Magento connector.
- name: Salesforce Commerce Cloud
description: Hosted Marqo product offers Salesforce Commerce Cloud connector.
maintainers:
- FN: Kin Lane
email: kin@apievangelist.com
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