Marqo – Vectorless Vector Search
Marqo is an end-to-end vector search engine. It contains everything required to integrate vector search into an application in a single API. Here is a code snippet for a minimal example of vector search with Marqo: mq = marqo.Client() mq.create_index("my-first-index") mq.index("my-first-index").add_documents([{"title": "The Travels of Marco Polo"}]) results = mq.index("my-first-index").search(q="Marqo Polo") Why Marqo? Vector similarity alone is not enough for vector search. Vector search requires more than a vector database - it also requires machine learning (ML) deployment and management,…
In plain words
Marqo is an end-to-end vector search engine that provides everything needed to add vector search capabilities to applications through a single API. It combines vector database functionality with machine learning deployment, data preprocessing, and search customization tools, eliminating the need to integrate multiple separate systems. Developers can index documents and perform semantic searches with minimal code, making it suitable for applications requiring similarity-based retrieval without extensive infrastructure setup.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Marqo is an end-to-end vector search engine. It contains everything required to integrate vector search into an application in a single API. Here is a code snippet for a minimal example of vector search with Marqo: mq = marqo.Client() mq.create_index("my-first-index") mq.index("my-first-index").add_documents([{"title": "The Travels of Marco Polo"}]) results = mq.index("my-first-index").search(q="Marqo Polo") Why Marqo? Vector similarity alone is not enough for vector search. Vector search requires more than a vector database - it also requires machine learning (ML) deployment and management, preprocessing and transformations of inputs as well as the ability to modify search behavior without retraining a model. Marqo contains all these pieces, enabling developers to build vector search into their application with minimal effort. Why not X, Y, Z vector database? Vector databases are specialized components for vector similarity. They are “vectors in - vectors out”. They still require the production of vectors, management of the ML models, associated orchestration and processing of the inputs. Marqo makes this easy by being “documents in, documents out”. Preprocessing of text and images, embedding the content, storing meta-data and deployment of inference and storage is all taken care of by Marqo. We have been running Marqo for production workloads with both low-latency and large index requirements. Marqo features: - Low-latency (10’s ms - configuration dependent), large scale (10’s - 100’s M vectors). - Easily integrates with LLM’s and other generative AI - augmented generation using a knowledge base. - Pre-configured open source embedding models - SBERT, Huggingface, CLIP/OpenCLIP. - Pre-filtering and lexical search. - Multimodal model support - search text and/or images. - Custom models - load models fine tuned from your own data. - Ranking with document meta data - bias the similarity with properties like popularity. - Multi-term multi-modal queries - allows per query personalization and topic avoidance. - Multi-modal representations - search over documents that have both text and images. - GPU/CPU/ONNX/PyTorch inference support. See some examples here: Multimodal search: [1] https://www.marqo.ai/blog/context-is-all-you-need-multimodal... Refining image quality and identifying unwanted content: [2] https://www.marqo.ai/blog/refining-image-quality-and-elimina... Question answering over transcripts of speech: [3] https://www.marqo.ai/blog/speech-processing Question and answering over technical documents and augmenting NPC's with a backstory: [4] https://www.marqo.ai/blog/from-iron-manual-to-ironman-augmen...
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