MongoDB
Cloud database platform for developers, enterprises, and regulated workloads.
Available information varies by company and source.
Profile record updated:
Company facts
- Official name
- MongoDB, Inc.
- Entity type
- COMPANY
- Founded
- 2007
- Headquarters
- 1633 Broadway, 38th Floor, New York, NY 10019
- Company size
- >5,000
- Market role
- B2B SaaS Provider
- Ticker
- MDB
- Official website
- mongodb.com
What MongoDB does
MongoDB operates an open-core infrastructure software model anchored by a commercial cloud platform. It creates value by offering a developer-friendly document database with managed operations, multi-cloud deployment, search, vector capabilities, and migration tooling, reducing infrastructure complexity for application teams. Revenue is captured through recurring cloud usage on Atlas, enterprise subscriptions for self-managed deployments, and associated support and commercial contracts.
Category differentiation
This is the public database software company, not merely an open-source project or a hyperscaler database product. It competes with cloud databases and database services rather than adtech, martech, or media platforms.
Strategic context
AI-supported assessment from the existing company research; distinguish interpretation from sourced facts.
MongoDB, Inc. is a public software company that sells database infrastructure for modern application development. Its main business is MongoDB Atlas, a managed multi-cloud database platform delivered across AWS, Microsoft Azure, and Google Cloud, alongside self-managed enterprise software and a free community edition. The company serves developers, database administrators, data engineers, enterprise IT teams, and public sector organisations that need scalable application data infrastructure. MongoDB makes money primarily through usage-based cloud consumption on Atlas and subscription licensing plus support for self-managed enterprise deployments. The free community and entry-level tiers widen adoption, while commercial tooling such as Compass and migration software help convert developer usage into enterprise standardisation and larger production workloads.
Company news briefing
Briefing updated:
MongoDB continues to enhance its Atlas platform, introducing Atlas App Connections for secure AI coding tool access and expanding Voyage AI capabilities alongside search features for on-premises and private cloud environments. Additionally, MongoDB has been integrated as a supported data source within OpenAI's new Data Agent for ChatGPT Work, enabling business users to query and analyse enterprise data through natural language.
Business model & monetisation
MongoDB monetises through a hybrid open-core and cloud software model. Atlas uses consumption-based pricing, including free entry tiers and paid usage for compute, storage, throughput, and higher-capacity clusters. Enterprise Advanced is sold through subscription licensing and commercial support for self-managed deployments. Community Edition is free and functions as an adoption funnel, while additional monetisation comes from enterprise agreements, support, and tooling that drives migration and production expansion.
- Atlas managed cloud platform
- Usage-based SaaS consumption
- Enterprise Advanced self-managed software
- Subscription licensing with support
- Government-compliant deployments
- Enterprise contract / compliant SaaS
- Community Edition
- Free adoption funnel
- Ancillary tooling and support
- Commercial support and enablement
Products & capabilities
No products with linked sources are available in this view.
Products & market categories
Competitors & alternatives
- Hevo
No-code ELT platform for cloud data pipelines and transformations.
- Oracle
Enterprise cloud infrastructure and customer experience software provider.
- Redis
In-memory database platform for caching, real-time data and AI workloads.
Side-by-side comparisons
Recent recorded signals
Dates refer to the source publication. Older entries are historical context, not evidence of a new event.
Bank of America Backs MongoDB, Raises Price Target
Cloud Data Warehouse / Data Lake · Recorded impact score: 2/5
Bank of America reiterated its buy rating on MongoDB and raised its 12-month price target to $540 from $450, citing accelerating AI adoption that boosts demand for data management systems. Analyst Koji Ikeda said MongoDB is positioned to win meaningful share of future AI workloads, praising its ability to handle large datasets, memory, scale, and real-time transactional data. The bank urged investors to view any fear-driven weakness as a buying opportunity. FactSet and LSEG data cited in the piece show MDB has rebounded strongly year-to-date and that the majority of Wall Street analysts rate the stock buy or strong buy.
- Bank of America reiterated its buy rating on MongoDB and raised its 12-month price target to $540 from $450.
- Bank of America analyst Koji Ikeda wrote that "MongoDB will win [a] meaningful share of future AI workloads."
Decoupling CRM from MDM for Device Management
Customer Relationship Management (CRM) · Recorded impact score: 2/5
The article describes an architecture for integrating Mobile Device Management (MDM) with an internal CRM without making the MDM a core dependency. Using NestJS, MongoDB/Mongoose, and TanStack Start with Fleet as the initial provider, the author separates business state (CRM-owned device records, device assignments, and immutable device action audit logs) from technical state (MDM-owned OS, hardware IDs, last check-in). Key patterns include a DeviceProvider abstraction (so different MDMs like Fleet, Intune, or Jamf can be swapped), a background DeviceSyncWorker that synchronizes technical device data into MongoDB every five minutes, and creation of DeviceAction audit records before executing destructive operations (lock/wipe) via the provider. The result is vendor independence, faster reads, auditable operations, and simpler frontend development.
- Author implemented an MDM-agnostic Device Management module using NestJS, MongoDB/Mongoose, and TanStack Start with Fleet as the initial MDM provider.
- The CRM owns three business concepts: Device (with a decoupled providerId), DeviceAssignment (historical ledger), and DeviceAction (immutable administrative audit record).
How to Export FTDC From MongoDB Atlas
Application Performance Monitoring (APM) · Recorded impact score: 1/5
This technical how-to shows how to extract FTDC (Full Time Diagnostic Data Capture) from MongoDB Atlas using an undocumented Atlas Admin API (v1.0) that creates a log collection job, polls for success, and downloads a bundle containing per-member diagnostic.data directories including the live metrics.interim file. The author lists the three required inputs (programmatic API key, project ID, replica set name), provides example curl calls, and describes bundle metadata (expiration ~30 days). Atlas sets diagnosticDataCollectionDirectorySizeMB to 400MB, which typically yields 2–5 days of FTDC under real load (longer on idle clusters). The post also warns that the built-in clusterMonitor role grants read access to the oplog and demonstrates a more restrictive diagnostics-only custom role. The author references tooling (Big Hole, keyhole) for reading FTDC locally.
- MongoDB servers write FTDC (Full Time Diagnostic Data Capture) into a diagnostic.data folder with roughly 5,700 metrics sampled once per second.
- MongoDB Atlas exposes a v1.0 Admin API endpoint (logCollectionJobs) that can package FTDC on demand, requiring a programmatic API key, project ID, and replica set name.
Instrumenting MERN E‑Commerce with SigNoz
Application Performance Monitoring (APM) · Recorded impact score: 2/5
A developer case study describing how the author instrumented a MERN-stack e-commerce application called Ram Store with SigNoz and OpenTelemetry during the Agents of SigNoz Hackathon 2026. The project used a self-hosted SigNoz instance (Docker) and the OpenTelemetry Node SDK with automatic instrumentation for Express, HTTP, and MongoDB, exporting via OTLP gRPC. The instrumentation enabled distributed traces, runtime metrics, and structured logs (Winston) correlated inside SigNoz. The post covers setup steps, observed telemetry (traces, metrics, logs), challenges (Docker networking, configuration), lessons learned, and planned improvements like custom dashboards and alerting. Project source code and a demo video are linked.
- Author integrated SigNoz into a MERN e-commerce application named Ram Store as part of the Agents of SigNoz Hackathon 2026.
- SigNoz was self-hosted locally using Docker to provide observability dashboards.
Dashboard optimized: 7.6s → 1.7s for 15 numbers
Application Performance Monitoring · Recorded impact score: 2/5
A developer post documents diagnosing and fixing a slow Next.js/MongoDB dashboard where rendering fifteen numbers took 7.6 seconds. Using Sentry (with explicit mongo/mongoose integrations) and a seeded high-volume dataset, the author found three issues: unprojected Invoice.find() returning 2,004 full documents (including base64 logos), client-side reductions scanning 4,806 tasks instead of server-side aggregation, and a non-deterministic sort comparator. Changes (add .select exclusions, use MongoDB aggregation with $facet, and fix comparator) reduced total server time from 7,580 ms to 1,671 ms, cut bytes read from ~24.8 MB to ~6.0 MB, and reduced overview bytes from 2,490 KB to 2.0 KB.
- DevLabs platform uses Next.js 16, React 19, and MongoDB/Mongoose.
- Sentry's default integrations do not include Mongo/Mongoose; the author enabled node.mongoIntegration() and node.mongooseIntegration() to see DB spans.
Explore company relationships
Questions about MongoDB
What is MongoDB?
MongoDB is a database software company that provides a managed multi-cloud database platform, self-managed enterprise database software, and a free community edition for developers.
Who uses MongoDB?
MongoDB is used by developers, data engineers, database administrators, enterprise IT teams, and government organisations building or running modern applications.
How does MongoDB make money?
MongoDB makes money mainly from usage-based Atlas cloud consumption and subscription licensing plus support for self-managed enterprise deployments.
Sources & coverage
This profile uses public, official and technically observable information. Missing information does not prove that a product or relationship does not exist. The list below does not imply that every profile statement has been verified.
15 publicly documented primary sources and citations linked across the market graph.
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