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ATLAS

Vector Search

Build intelligent applications powered by semantic search and generative AI using native, full-featured vector database capabilities.

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What is vector search?

Generative AI uses vectors to enable intelligent semantic search over unstructured data (text, images, and audio). Vectors are critical in building recommendation engines, anomaly detection, and conversational AI. The wide range of use cases, made possible with native capabilities in MongoDB, deliver transformative user experiences.

The combined power of vectors and MongoDB

Unparalleled simplicity

Avoid the synchronization tax. With Atlas Vector Search built into the core database, there’s no need to sync data between your operational and vector databases—saving time, reducing complexity, and preventing errors. Your operational and vector data stay in one place.

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Illustration with an example of how this feature works.
Illustration with an example of how this feature works.

Powerful query capabilities

Easily combine vector queries with filters on meta-data, graph lookups, aggregation pipelines, geo-spatial search, and lexical search for powerful hybrid search use cases within a single database.

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Superior scaling for vector search apps

Unlike other solutions, MongoDB’s distributed architecture scales vector search independently from the core database. This enables true workload isolation and optimization for vector queries, resulting in superior performance at scale.

Learn about Search Nodes
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Enterprise-ready vector database

Security and high availability are built in. Because vector data is stored directly in Atlas with your operational data, you can rest assured your workloads are running with the same trusted enterprise-grade security and availability MongoDB is known for.

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Atlas Vector Search customer successes

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10 minutesClinical report creation time
“Only MongoDB Atlas gives us the flexibility and scale at the data platform layer to experiment in how to harness one of the biggest technical advancements the industry has ever seen.”
Louise Lind Skov
Head of Content Digitalisation, Novo Nordisk
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10 minutesClinical report creation time
“Only MongoDB Atlas gives us the flexibility and scale at the data platform layer to experiment in how to harness one of the biggest technical advancements the industry has ever seen.”
Louise Lind Skov
Head of Content Digitalisation, Novo Nordisk
Read Case Study
30%Lower operating costs
EVENT-DRIVEN APPS
“Atlas Vector Search was the solution to our problems. It simplifies a lot of the work that goes into making Okta Inbox super user friendly for customers.”
Suchit Agarwal
Director of Engineering, Okta
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“With Atlas Vector Search we can compose sophisticated queries that quickly filter across product data, customer preferences, and vector embeddings to precisely identify hyper-relevant product recommendations in real time.”
Mundher Al-Shabi
Senior Data Scientist, Delivery Hero
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“We want to make it possible for users of our customers’ knowledge base to receive instant, trustworthy, and accurate answers to their questions using conversational search powered by MongoDB Atlas Vector Search and Generative AI capabilities.”
Saravana Kumar
CEO, Kovai
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“It was incredibly easy to deploy our search data to Atlas Search Nodes, requiring only a few button clicks. Furthermore, the memory requirements of vector search can now match our Atlas Search Node deployment exactly. This is a crucial consideration for keeping vector search fast and streamlined.”
Pierce Lamb
Senior Software Engineer, VISO TRUST
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FEATURED INTEGRATIONS

Vector search use cases

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Learning hub

FAQ

Get started with Atlas Vector Search

See how you can convert your data into vector embeddings, retrieve them with search capabilities, and build intelligent applications quickly and easily in MongoDB Atlas.
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Start building with:
  • Simplified deployment
  • Unified developer experience
  • Horizontal, vertical, independent scale
  • Integrated AI ecosystem
  • 118+ regions worldwide