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Text Content

BLOGAtlas Vector Search voted most loved vector database in 2024 Retool State of
AI report — Read more >>
Blog

Atlas Vector Search voted most loved vector database in 2024 Retool State of AI
report Read more >>

Case Study

Learn how Okta automates natural language queries with MongoDB Atlas Vector
Search. Read more >>

Blog

Announced at MongoDB.local NYC 2024: A recap of all announcements and updates —
Read more >>



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MONGODB ATLAS

Loved by developers. Built for

Time Series Data

You don't need a separate database to support transactions, rich search, or gen
AI. The world's most popular document database is now the world's most versatile
developer data platform.

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Learn More

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Atlas CLI Community Edition Enterprise Advanced
47k+
Customers →
118
Regions across AWS, Azure, and Google Cloud →
175K+
Developers join every month →
#1
Most used modern database →







THE MOST LOVED VECTOR DATABASE, ONCE AGAIN!

For the second year, MongoDB Atlas Vector Search was voted #1 in the latest
Retool State of AI global survey.

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Query

Use an expressive, developer-native query API to work with data anyway your
application needs. Retrieve and modify documents. Prompt LLMs with semantic
search across vectors. Aggregate, transform and analyze your data in place,
without having to ETL it to another system

Optimize

Optimize queries, react instantly to database changes, and generate materialized
views on-demand. MongoDB makes it easy to receive and process millions of
requests per day with no change in performance.

Javascript
Python
Java
C#
0
// Query on embedded documents
1

2
const cursor = db.collection('orders').aggregate([{
3
"$vectorSearch": {
4
"index" :"default",
5
"path" :"name_embedding",
6
"queryVector" : query_embedding,
7
"numCandidates" :150,
8
"limit" :10
9
}
10
}]);
11

12
// Process multiple documents and return
13
// computed results with aggregation operations
14

15
db.customers.aggregate([
16
//Stage 1: Vector Search for Jack Beanstalk
17
{
18
"$vectorSearch": {
19
"index": "default",
20
"path": "name_embedding",
21
"queryVector": query_embedding,
22
"numCandidates": 150,
23
"limit": 10,
24
"customer.name": "Jack Beanstalk"
25
},
26
//Stage 2: Unwind items array
27
{
28
'$unwind': '$items'
29
},
30
//Stage 3: Group remaining documents by item name and sum
31
{
32
'$group': {
33
'_id':'$items.name',
34
'totalOrders': {
35
'$sum': '$items.quantity'
36
}
37
}
38
}
39
])
40

Documentation

Docs

Reference manuals, articles, and code samples — all the technical documentation
you need to deploy, configure, and run MongoDB.

Documentation


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The latest MongoDB tutorials, videos, and code examples shown in the language of
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Don’t just take our word for it

Find out why MongoDB is the industry’s most used modern database for the 5th
year in a row.



“The granular updates you can perform on nested @MongoDB data is pretty
amazing.”

Charlie Lu @theCHARlister

Software Engineer; Typescript

“MongoDB is my favorite. Atlas is an amazing product and service”

John Hanley @NeoPrimeAWS

Enterprise Architect and SWE; PHP, Python, C# and Swift

"Finally achieved my MongoDB Certification from @MongoDB. The courses are
amazingly interactive. Loved every second of it💪🥇”

Steven Oketch @iSteve_zion

Web Developer; MERN Stack



MongoDB Atlas

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The developer data platform for any use case.

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Vector SearchStream ProcessingOperationalTransactionalText
SearchAnalyticalEdgeGraphGeospatial
Vector Search Stream Processing Operational Transactional Text Search Analytical
Edge Graph
Vector Search
Vector Search
Stream Processing
Operational
Transactional
Text Search
Analytical
Edge
Graph

Vector Search Use Cases

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 gen AI Apps.

Learn More
Documentation




LOVED BY DEVELOPERS, TRUSTED BY ENTERPRISES

View all customer stories


10 MINUTES

to generate reports instead of 12 weeks

50%

of the world's insulin production
Read Case Study


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10 minutes, with higher quality outputs and a fraction of the team. In terms of
value, each day sooner a medicine gets to market can add around $15 million in
revenue to the company.”

Read Case Study
MongoDB for Healthcare


70+

countries served

100M+

items in product catalog
Read Case Study


ARTIFICIAL INTELLIGENCE

“We needed to move to an up-to-second real-time recommendations system, and that
is what MongoDB Atlas Vector Search enabled us to do.”

Read Case Study
MongoDB for Artificial Intelligence


30%

reduction in operating costs

18K+

global customers
Read Case Study


SOFTWARE

“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.”

Read Case Study
MongoDB for Artificial Intelligence


65M+

daily events processed

40%

more resource efficient
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without a problem.”

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32

applications migrated in 30 days

2M+

customers supported
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us. It made it really easy for us to choose MongoDB because we didn’t have to
then hedge our bets on a particular cloud provider or a particular process — we
could be flexible.”

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with immediate gains and less risk.Learn More


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