Marqo

Tensor search database with built-in embedding generation that handles text and image vectorization automatically, supporting multimodal structured and unstructured index types with both self-hosted and Marqo Cloud deployment options.

Evaluated Mar 07, 2026 (0d ago) vcurrent
Homepage ↗ Repo ↗ Other vector-database tensor-search multimodal embeddings open-source image-search text-search self-hosted
⚙ Agent Friendliness
58
/ 100
Can an agent use this?
🔒 Security
78
/ 100
Is it safe for agents?
⚡ Reliability
72
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
--
Documentation
78
Error Messages
74
Auth Simplicity
88
Rate Limits
68

🔒 Security

TLS Enforcement
100
Auth Strength
72
Scope Granularity
65
Dep. Hygiene
78
Secret Handling
76

No built-in auth on self-hosted deployment is a significant risk; Marqo Cloud adds API key auth but lacks per-index key scoping.

⚡ Reliability

Uptime/SLA
72
Version Stability
74
Breaking Changes
72
Error Recovery
72
AF Security Reliability

Best When

You want an all-in-one tensor search database where embedding generation is handled automatically and you need multimodal (text + image) search without managing separate ML infrastructure.

Avoid When

You already have embeddings and need only a vector store, or require a distributed multi-node cluster you can scale independently.

Use Cases

  • Index a product catalog with text descriptions and images in a single API call and search across both modalities with one query
  • Build a semantic search pipeline without managing a separate embedding model service — Marqo handles model loading and inference internally
  • Create structured indexes with field-level embedding control so agents can search within specific document sections
  • Run cross-modal queries where a text prompt retrieves the most similar product images from a multimodal index
  • Self-host Marqo on GPU-enabled infrastructure to reduce embedding inference latency for high-throughput agent workloads

Not For

  • Teams that need to bring their own pre-computed embeddings without running Marqo's internal model inference pipeline
  • Workloads requiring distributed horizontal scaling across many nodes — Marqo's architecture is primarily single-node or Marqo Cloud
  • Pure keyword BM25 search without semantic components — use Elasticsearch or OpenSearch for traditional full-text search

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
Yes
Webhooks
No

Authentication

Methods: api_key
OAuth: No Scopes: No

Self-hosted Marqo has no auth by default; Marqo Cloud requires API key authentication per index.

Pricing

Model: open_source
Free tier: Yes
Requires CC: No

Apache 2.0 open source for self-hosted; Marqo Cloud is the commercial managed option.

Agent Metadata

Pagination
offset
Idempotent
Partial
Retry Guidance
Not documented

Known Gotchas

  • Self-hosted Marqo exposes no authentication by default — place it behind an authenticated reverse proxy before exposing to agents over a network
  • Model download occurs at first use of an embedding model; initial indexing calls may time out while the model is being fetched
  • Unstructured indexes embed all text fields together — agents cannot search within specific fields without switching to a structured index type
  • Deleting an index is irreversible with no soft-delete or trash — agents with index management permissions can permanently destroy data
  • Image URLs indexed by Marqo are fetched and embedded at index time; broken or rate-limited image URLs cause silent indexing failures for those documents

Alternatives

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Scores are editorial opinions as of 2026-03-07.

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