Marqo
Multimodal vector search engine for unstructured data
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What is Marqo?
Key features
Multimodal search
query using text, images, or combinations of both
Built-in embedding generation
automatically converts documents and queries into vectors
Vector storage and indexing
stores embeddings with metadata for fast retrieval
Weighted query construction
combine multiple search components with custom weightings
API-based access
integrate search functionality via REST or Python SDK
Scalable infrastructure
handles projects from small prototypes to large deployments
Pros & cons
Advantages
- Reduces complexity by combining embedding, storage, and search in one platform
- Supports multiple data types without requiring separate pipelines for each
- Multimodal capabilities allow searching across text and images simultaneously
- Relatively straightforward API for getting started quickly
Limitations
- Pricing details not publicly transparent, requiring contact with sales
- May be overkill for simple single-modality search tasks
- Requires familiarity with vector search concepts to use effectively
Use cases
Building image and text search for e-commerce product catalogues
Creating semantic search over documentation and code repositories
Developing content discovery systems that understand visual and textual similarity
Implementing search features in applications handling mixed media archives
Analysing unstructured company data across documents and images
Ready to try Marqo?
Pricing
Paid plans
Custom pricing
Production deployments with pricing based on usage and scale; contact sales for details
Get started with Marqo
Click through to Marqo and start using it now.