M2per month
$9/month
- 2 GB shared storage cluster tier
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Start here for the decision-making essentials: what MongoDB Atlas Vector Search does, who it is for, how it is accessed, and the first-party sources behind this profile.
$9/month
$25/month
MongoDB Atlas Vector Search integrates operational and vector databases on a unified multi-cloud platform, enabling semantic search, recommendation engines, Q&A systems, anomaly detection, and generative AI applications with ACID transactions and serverless horizontal scaling.
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Short answers to the questions buyers and builders commonly ask about MongoDB Atlas Vector Search. Each answer cites the shared ledger below, where every source is listed once.
MongoDB Atlas integrates operational and vector databases in a single, unified platform, s · Built-in query capabilities include geospatial search, lexical search, and vector search w · Vector Search: design intelligent apps with generative AI · Multi-cloud database service with a JSON-like document data model
MongoDB Atlas integrates operational and vector databases in a single, unified platform. Use vector representations of your data to perform semantic search, build recommendation engines, design Q&A systems, detect anomalies, or provide context for generative AI Apps.
More than 67,000 customers building applications on MongoDB
More than 67,000 customers have chosen to build the applications of today and tomorrow on MongoDB
Semantic search, recommendation engines, Q&A systems, anomaly detection, and providing con · Catalog and content search, in-app search, and single-view search combining database, sear · Building intelligent generative AI applications
Use vector representations of your data to perform semantic search, build recommendation engines, design Q&A systems, detect anomalies, or provide context for generative AI Apps.
Atlas free tier includes 512 MB of storage, 32 MB of sort memory, and up to 100 operations · New collections cannot be created in cross-shard write transactions · GeoJSON MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection types require 2d
Atlas's free tier includes 512 MB of storage, 32MB of sort memory, and up to 100 operations per second.
M0 | Free forever | 512 MB storage with shared RAM and vCPUs · M2 | $9/month | 2 GB storage with shared RAM and vCPUs · M5 | $25/month | 5 GB storage with shared RAM and vCPUs
M0 | 512 MB | Shared | Shared | Free forever
MongoDB Search provides $search and $searchMeta aggregation pipeline stages for querying s
MongoDB Search provides $search and $searchMeta stages, which you can use with other aggregation pipeline stages in your query pipeline.
This profile connects the jobs MongoDB Atlas Vector Search is described as handling with its delivery model, access options and the subjects used to match it to related products in this directory.
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MongoDB: The World’s Leading Modern Data Platform
Integrates operational and vector databases in a single, unified platform · Supports semantic search, recommendation engines, Q&A systems, anomaly detection, and gene · Multi-cloud deployment available on AWS, Azure, and Google Cloud
MongoDB Atlas integrates operational and vector databases in a single, unified platform, supporting vector representations of data for semantic search and generative AI use cases
Semantic search, recommendation engines, Q&A systems, anomaly detection, and providing context for generative AI applications
Catalog and content search, in-app search, and single-view search combining database, search engine, and sync into one system
Multi-cloud deployment available on AWS, Azure, and Google Cloud
M0 | Free forever | 512 MB storage with shared RAM and vCPUs
M2 | $9/month | 2 GB storage with shared RAM and vCPUs
M5 | $25/month | 5 GB storage with shared RAM and vCPUs
Atlas free tier includes 512 MB of storage, 32 MB of sort memory, and up to 100 operations per second
Built-in query capabilities include geospatial search, lexical search, and vector search with strong consistency via ACID transactions
A concise view of the jobs, capabilities and integrations described in the recorded product sources.
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Press release announcing accurate AI retrieval capabilities across enterprise data locations, supporting Vector Search for production generative AI applications.
View source [13]Press release announcing capabilities to make enterprise AI production-ready, spanning retrieval and Vector Search workflows.
View source [13]Facts, answers, structured details, milestones and primary resource links cite this shared ledger. Each external page appears once; release tags from the same GitHub project are grouped under one release history.
These tools share a workflow, capability or audience with MongoDB Atlas Vector Search. They may complement it rather than replace it, and are not presented as integrations or endorsements.
Serverless horizontal scaling with geography-aware fault tolerance across all major clouds
Security primitives allow MongoDB to operate in the most demanding enterprise environments
Enterprise-grade support from MongoDB with technical support that extends beyond break/fix
Vector Search: design intelligent apps with generative AI
Building intelligent generative AI applications
AWS, Azure, and Google Cloud Platform
Cloud-native, AI-ready platform
Built-in enterprise-grade security with preconfigured authentication, authorization, encryption, network security, and data resiliency
FedRAMP Moderate Authorized environment
U.S. government (Atlas for Government US) for public sector and ISVs
More than 67,000 customers building applications on MongoDB
AI-powered intelligent applications
Supports EU financial institutions in meeting DORA obligations
Multi-cloud database service with a JSON-like document data model
Unified Query API supports full-text search, real-time analytics, and event-driven experiences on arrays, geospatial, and time series data
Can be deployed via Atlas UI, CLI, Kubernetes Operator, or Infrastructure-as-Code (IaC) resource provider
Free cluster, flex tier instance, or dedicated cluster configurations available
Kubernetes Operator is required to deploy MongoDB Search and Vector Search with MongoDB Enterprise Advanced
MongoDB Enterprise Server includes an in-memory storage engine for high throughput and low latency
MongoDB Enterprise Server includes LDAP and Kerberos access controls and encryption for data at rest
Ops Manager included with MongoDB Enterprise Advanced subscription automates deployment, upgrades, monitoring, backups, and query optimization
MongoDB Search provides $search and $searchMeta aggregation pipeline stages for querying search indexes
MongoDB supports distributed transactions across multiple collections, databases, documents, and shards for atomicity of reads/writes to multiple documents
MongoDB supports distributed transactions on replica sets and sharded clusters
New collections cannot be created in cross-shard write transactions
MongoDB geospatial queries on GeoJSON objects calculate on a sphere using the WGS84 reference system
MongoDB supports GeoJSON object types including Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection
GeoJSON MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection types require 2dsphere indexes
