Who is MongoDB best for?

MongoDB is built for Application developers and engineering teams building cloud-native or AI-powered applications, Platform and infrastructure engineers managing large-scale, distributed data architectures, Enterprise architects and DBAs modernizing legacy relational database estates, Data scientists and ML engineers building RAG pipelines and vector search applications. Common use cases include AI-native and generative AI application development (RAG pipelines, semantic search); Modern web, mobile, and microservices application backends; Legacy relational database modernization and migration.

How is MongoDB priced?

MongoDB Atlas offers three tiers: Free (M0, $0/month, 512 MB storage, shared compute, free forever), Flex ($0.011/hr, up to ~$30/month, 5 GB storage, usage-based burst), and Dedicated starting at $0.08/hr (~$56.94/month for M10 with 10 GB storage and 2 GB RAM), scaling to M700 ($33.26/hr, 768 GB RAM, 96 vCPUs). Add-on services include Atlas Search Nodes ($0.12–$3.27/hr), Atlas Vector Search Nodes (same tier pricing), Atlas Stream Processing ($0.06–$2.49/hr per processor), Data Federation ($5/TB processed), and Online Archive (from $0.001578/GB/month). Enterprise Advanced (self-managed, on-premises) is sold via custom contract with consultative support. All Atlas tiers are billed hourly on a pay-as-you-go basis. Startup, educator, and student credits are available.

What are the alternatives to MongoDB?

Common Databases & Data Infrastructure alternatives to MongoDB include Neon, Supabase, CockroachDB, PlanetScale, PingCAP. See the full comparison hub at /verticals/databases-data-infrastructure/compare.

What do users praise about MongoDB?

Users frequently praise: Flexible document schema with no rigid table structure; Fast and easy initial setup and cluster provisioning; Horizontal scalability via sharding; Rich multi-language driver and framework support; Atlas Vector Search capability for AI and RAG applications; Comprehensive documentation and MongoDB University resources; Intuitive Compass GUI for schema exploration and querying; Fully managed Atlas platform with auto-scaling and monitoring.

What are common complaints about MongoDB?

Frequently cited limitations: Complex multi-document joins compared to SQL databases; Unexpected Atlas cost spikes with usage-based scaling; Steep learning curve for advanced aggregation pipelines; VPC and network connectivity configuration challenges in Atlas; Data redundancy and storage overhead from denormalization; High RAM consumption for large active datasets; SSPL license not OSI-approved, limiting open-source redistribution; Less suitable for highly relational or transaction-intensive workloads.

When was MongoDB founded and where?

MongoDB was founded in 2007, headquartered in New York, NY, USA by Dwight Merriman, Eliot Horowitz, Kevin Ryan.

MongoDB reports 5000-8000 employees, 65,200+ customers, ~$2.46B ARR.

AI visibility report

AI visibility report for MongoDB in Databases & Data Infrastructure.

Outside the top three on 12 of the 25 prompts buyers actually ask.

Neon is cited on 5 of those losses.

25 prompts

6 platforms

Updated Oct 18, 2026 - refreshed weekly

Track MongoDB daily

14-day free trial. No card required. Setup comes pre-filled for MongoDB.

Track MongoDB across these prompts daily.

Start your 14-day trial

6percent

Presence Rate

Low presence

Still absent from 94% of tracked prompt responses

Top-3 citations across 150 prompt × platform pairs

No clearrank

Peer Ranking

#1#15

No clear rankin Databases & Data Infrastructure

Key Metrics

Presence Rate

6.0%

Share of Voice

9.2%

Avg Position

#4.1

Docs Presence

4.7%

Blog Presence

0.0%

Brand Mentions

23.3%

Platform Breakdown

ChatGPT

16%4/25 prompts

Perplexity

Research dossierCapabilities, use cases, sources, reviews, pricing, and FAQ

Overview

MongoDB is a publicly traded American software company (NASDAQ: MDB) founded in 2007 and headquartered in New York City. It develops and maintains the MongoDB document database engine and its flagship cloud-managed offering, MongoDB Atlas—a multi-cloud developer data platform available on AWS, Azure, and Google Cloud. MongoDB stores data in flexible, JSON-like BSON documents rather than rigid relational tables, enabling schema-less, horizontally scalable application development. Atlas has expanded beyond its database roots to include vector search, full-text search, stream processing, analytics, and AI-ready capabilities, and now represents approximately 73% of total company revenue. As of fiscal year 2026 (ending January 31, 2026), MongoDB reported $2.46 billion in annual revenue and served more than 65,200 customers globally, including 70% of the Fortune 100.

AI visibility report

AI visibility report for MongoDB in Databases & Data Infrastructure.

Outside the top three on 12 of the 25 prompts buyers actually ask.

Neon is cited on 5 of those losses.

25 prompts

6 platforms

Updated Oct 18, 2026 - refreshed weekly

Track MongoDB daily

14-day free trial. No card required. Setup comes pre-filled for MongoDB.

Track MongoDB across these prompts daily.

Start your 14-day trial

6percent

Presence Rate

Low presence

Still absent from 94% of tracked prompt responses

Top-3 citations across 150 prompt × platform pairs

No clearrank

Peer Ranking

#1#15

No clear rankin Databases & Data Infrastructure

Key Metrics

Presence Rate

6.0%

Share of Voice

9.2%

Avg Position

#4.1

Docs Presence

4.7%

Blog Presence

0.0%

Brand Mentions

23.3%

Platform Breakdown

ChatGPT

16%4/25 prompts

Perplexity

Research dossierCapabilities, use cases, sources, reviews, pricing, and FAQ

Overview

Reviews

4.5/5Gartner Peer Insights·1214+

Praised

Flexible document schema with no rigid table structure
Fast and easy initial setup and cluster provisioning
Horizontal scalability via sharding
Rich multi-language driver and framework support
Atlas Vector Search capability for AI and RAG applications
Comprehensive documentation and MongoDB University resources
Intuitive Compass GUI for schema exploration and querying
Fully managed Atlas platform with auto-scaling and monitoring

Criticized

Complex multi-document joins compared to SQL databases
Unexpected Atlas cost spikes with usage-based scaling
Steep learning curve for advanced aggregation pipelines
VPC and network connectivity configuration challenges in Atlas
Data redundancy and storage overhead from denormalization
High RAM consumption for large active datasets
SSPL license not OSI-approved, limiting open-source redistribution
Less suitable for highly relational or transaction-intensive workloads

MongoDB earns strong reviews across major analyst platforms, rated 4.5/5 from over 1,200 verified Gartner Peer Insights reviews in the Cloud Database Management Systems category. Users consistently praise its flexible document schema, ease of initial setup, horizontal scalability, and broad multi-language driver support. The Atlas vector search capability has received notable positive attention from AI engineers. Common criticisms include the complexity of multi-document joins compared to SQL, unexpected Atlas cost increases at scale, a steeper learning curve for advanced aggregation pipelines, and challenges with VPC and network connectivity configuration in Atlas.

Prompt	Perplexity	Google AI Mode	Bing Copilot	ChatGPT	Gemini Search	Grok
Capability0/5 cited (0%)
What are the best dedicated vector databases, and how do they compare to adding vector search extensions to an existing relational database?	A competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited

#	Brand	PresencePres.	Share of VoiceSoV	DocsDocs	BlogBlog	MentionsMent.	Avg PosPos
1	Neon	8.7%	15.8%	8.0%	0.0%	28.0%	#5.3
2	Supabase	8.7%	21.2%	8.7%	0.7%	21.3%	#9.8
3	PlanetScale	8.0%	14.7%	6.0%	3.3%	20.7%	#6.9
4	CockroachDB	8.0%	10.3%	2.0%	2.0%	2.0%	#7.4
5	MongoDB	6.0%	9.2%	4.7%	0.0%	23.3%	#4.1
6	PingCAP	6.0%	7.1%	4.0%	0.7%	5.3%	#6.3
7	QuestDB	4.0%	6.5%	0.0%	2.7%	6.0%	#7.8
8	ClickHouse	3.3%	5.4%	0.7%	0.7%	10.7%	#5.9
9	Redis	1.3%	2.2%	0.7%	0.0%	6.7%	#2.0
10	SingleStore	1.3%	3.3%	1.3%	0.7%	4.0%	#4.0
11	Timescale	1.3%	1.1%	0.7%	0.0%	1.3%	#5.0
12	Turso	1.3%	2.2%	0.7%	0.0%	8.0%	#5.0
13	Xata	0.7%	1.1%	0.0%	0.7%	2.0%	#4.0
14	Fauna	0.0%	0.0%	0.0%	0.0%	0.7%	—
15	Gel	0.0%	0.0%	0.0%	0.0%	0.0%	—

Developer Experience2/5 cited (40%)
Which developer-focused databases offer the best local development experience that actually mirrors the production setup?	A competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Your brand and a competitor were cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited

Integrations & Ecosystem2/5 cited (40%)
What tools sync data from a primary operational database to an analytics warehouse for real-time reporting without heavy ETL infrastructure?	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited

Performance & Reliability0/5 cited (0%)
What tools and benchmarks help compare database platforms for high-concurrency transactional workloads before committing to one?	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited

AI visibility report for MongoDB in Databases & Data Infrastructure.

Key Metrics

Platform Breakdown

Overview

AI visibility report for MongoDB in Databases & Data Infrastructure.

Key Metrics

Platform Breakdown

Overview

Prompts where competitors are visible and MongoDB is not.

Where MongoDB is winning2

Where MongoDB is losing5

Key Facts

Key Capabilities10

Key Use Cases8

MongoDB customer outcomes

Recent Trend

How AI describes MongoDB3

Most cited sources8

Alternatives in Databases & Data Infrastructure6

Reviews

Pricing

Limitations

Topic Coverage

Prompt-Level Results

Vertical Ranking

Turn this into your team dashboard

Frequently asked questions

Setup & First Run0/5 cited (0%)
What's the fastest serverless relational database to spin up and connect to a Node.js backend for a new SaaS app?	A competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited	A competitor was cited	Neither your brand nor a competitor was cited	Neither your brand nor a competitor was cited

AI visibility report for MongoDB in Databases & Data Infrastructure.

Key Metrics

Platform Breakdown

Overview

AI visibility report for MongoDB in Databases & Data Infrastructure.

Key Metrics

Platform Breakdown

Overview

Prompts where competitors are visible and MongoDB is not.

Where MongoDB is winning2

Where MongoDB is losing5

Key Facts

Key Capabilities10

Key Use Cases8

MongoDB customer outcomes

Recent Trend

How AI describes MongoDB3

Most cited sources8

Alternatives in Databases & Data Infrastructure6

Reviews

Pricing

Limitations

Topic Coverage

Prompt-Level Results

Vertical Ranking

Turn this into your team dashboard

Frequently asked questions

What does MongoDB do?

Who is MongoDB best for?

How is MongoDB priced?

What are the alternatives to MongoDB?

What do users praise about MongoDB?

What are common complaints about MongoDB?

When was MongoDB founded and where?

How big is MongoDB?