B2B SaaS Provider
MongoDB vs Neo4j
Structured technology and market comparison · 2026
Direct Feature Comparison
MongoDB · vs · Neo4jCloud database platform for developers, enterprises, and regulated workloads.
Enterprise graph database and analytics software provider.
Analyze all overlapping signals and tech stacks for MongoDB and Neo4j
Compare mutual enterprise clients, monetization models, live market signals, and partner networks directly in the interactive Knowledge Graph.
Comparison Analysis
What is the main difference between MongoDB and Neo4j?
When comparing MongoDB and Neo4j, both platforms operate within the Cloud Data Warehouse / Data Lake, B2B SaaS Provider, and Productivity & Collaboration SaaS ecosystem. MongoDB is positioned as Cloud database platform for developers, enterprises, and regulated workloads, whereas Neo4j focuses on Enterprise graph database and analytics software provider. Decision-makers evaluate both solutions when orchestrating their commercial monetization and technology stack.
What are the top alternatives to MongoDB and Neo4j?
When evaluating MongoDB and Neo4j, enterprise buyers also consider other platforms in Cloud Data Warehouse / Data Lake, B2B SaaS Provider, and Productivity & Collaboration SaaS. You can discover the full competitive landscape and evaluate other alternatives by viewing their respective footprint profiles on Polaris7.
How do the corporate scale, structure, and headquarters of MongoDB and Neo4j compare?
MongoDB is headquartered in United States (founded in 2007) with >5,000 employees. Neo4j is based in United States (founded in 2007) with 501–1,000 employees. Complete ownership, subsidiary trees, and corporate affiliations are documented across Polaris7.
Market Signals
Recent Market Signals & Activity: MongoDB vs Neo4j
Documented market movements, strategic partnerships, product releases, and regulatory developments mapped across Polaris7.
MongoDB
Recent Signals
- ·SEC APIfinancials
10-Q Financial Filing Analysis for MongoDB (2026-09-01)
MongoDB, Inc. reported its financial results for the second quarter of fiscal 2027 ended July 31, 2026, demonstrating continued top-line acceleration and an operational pivot to GAAP profitability. Total revenue grew 30.5% year-over-year to $771.8 million, driven by strong usage growth in MongoDB Atlas, which reached $565.9 million and represented 73% of quarterly revenue. The company generated operating income of $28.4 million and net income of $40.9 million, compared to a net loss of $47.0 million in the prior-year period, supported by gross margin expansion to 73.8% and operational leverage across operating expense lines. For the first six months of fiscal 2027, total revenue reached $1.46 billion, generating $343.5 million in operating cash flows. The company maintained a strong liquidity profile with $2.41 billion in cash, cash equivalents, and short-term investments, while repurchasing $200.0 million in common stock under its authorized $1.0 billion share repurchase plan.
- Q2 FY2027 total revenue increased 30.5% year-over-year to $771.8 million, with subscription revenue reaching $747.1 million and Atlas DBaaS contributing $565.9 million (73% of total revenue).
- MongoDB swung to GAAP profitability, delivering operating income of $28.4 million and diluted net income per share of $0.50, compared to an operating loss of $65.3 million and net loss per share of $0.58 in Q2 FY2026.
- Customer count expanded to over 70,600 (with over 69,300 on Atlas) and customers generating $100,000+ in ARR rose to 2,999, underpinning a net ARR expansion rate of 122%.
- ·SEC APIfinancials
8-K Financial Filing Analysis for MongoDB (2026-10-15)
MongoDB, Inc. entered into an executive employment agreement with Dev Ittycheria following his appointment as Interim President and Chief Executive Officer, effective September 28, 2026. The contract provides an annual base salary of $500,000, a $500,000 one-time sign-on cash bonus, a 70% target cash bonus ($350,000), and a $15,000,000 restricted stock unit (RSU) grant vesting quarterly through October 1, 2027. Additionally, Ittycheria agreed to remain in service for at least 12 months after a permanent CEO is appointed and not terminate before April 1, 2028. Concurrently, MongoDB granted Chief Financial Officer Michael Berry a one-time retention RSU package valued at $10,000,000, vesting in three equal installments through April 1, 2028, ensuring executive continuity amid leadership transition.
- Dev Ittycheria's interim CEO compensation includes a $500,000 base salary, a $500,000 cash sign-on bonus, a $350,000 target bonus, and $15,000,000 in RSUs vesting quarterly through October 1, 2027.
- Ittycheria agreed to provide continuous service for at least 12 months following a permanent CEO's start date, terminating no earlier than April 1, 2028.
- CFO Michael Berry received a $10,000,000 retention RSU grant vesting across three equal tranches on April 1, 2027, October 1, 2027, and April 1, 2028.
- ·CNBC TechnologyAI
Meta may abandon cloud business for Muse AI agent
Meta Platforms may be forgoing billions in potential cloud revenue as it prioritizes its AI agent Muse. Wells Fargo analysts removed 500 megawatts of expected capacity resales from 2027 estimates, roughly $5 billion in high-margin revenue, due to Muse's early success. The firm raised its price target on Meta to $1,000 from $796, implying nearly 35% upside. Meta launched Muse on Sept. 8, allowing users to delegate tasks like email and travel booking. The company also launched Muse for Small Business and established the Meta Enterprise Platform, hiring MongoDB CEO CJ Desai to run it. Wells Fargo views 2027 as a potential trough with operating expenses around $212 billion, expecting AI revenue to contribute meaningfully in 2028. Meta's advertising engine provides flexibility to invest long-term.
- Wells Fargo removed 500 megawatts of expected capacity resales from 2027 estimates, citing Muse's success.
- Wells Fargo raised Meta's price target to $1,000 from $796, implying nearly 35% upside.
- Meta launched Muse on Sept. 8, allowing users to delegate tasks like email and travel booking.
Neo4j
Recent Signals
- ·Neo4j
Neo4j GraphAware Financial Crime Intelligence debuts for full-cycle detection, investigation & prevention
On September 16, 2026, Neo4j announced Neo4j GraphAware Financial Crime Intelligence, a graph-native detection and investigation solution for banks and insurers to prevent financial crime. It is built on a reusable knowledge layer for enterprise AI and is the first milestone since Neo4j closed its acquisition of GraphAware in August 2026. The solution covers the full crime cycle—Signal, Alert, Investigate, Decide—and is available now.
- Neo4j announced Neo4j GraphAware Financial Crime Intelligence on September 16, 2026, as a graph-native detection and investigation solution for banks and insurers.
- The release is the first milestone after Neo4j closed its acquisition of GraphAware in August 2026.
- The OECD reported that $442 billion was lost to consumer fraud globally in 2025; Interpol reported over 5,800 arrests across 97 countries from a single global fraud bust in July 2026.
- ·Neo4j
Introducing Neo4j GraphAware Financial Crime Intelligence
Neo4j introduced Neo4j GraphAware Financial Crime Intelligence, a graph-native foundation for financial crime operations. The solution unifies internal and external data into an entity-resolved knowledge layer to help banking and insurance teams detect connected fraud patterns, investigate alerts with full context, and make defensible decisions. It includes predefined detection models and works alongside existing detection engines and case management systems.
- Neo4j announced Neo4j GraphAware Financial Crime Intelligence on September 15, 2026.
- The solution provides a graph-native knowledge layer that continuously integrates and enriches internal and external data into an entity-resolved graph.
- It targets fraud investigations, AML and transaction monitoring, KYC/customer due diligence, sanctions screening, mule detection, and insurance claims investigations.
- ·DEV CommunityInfrastructure
Benchmark of Five Managed Graph Databases
A reproducible benchmark comparing CognoDB Cloud, Neo4j AuraDB, Memgraph, FalkorDB and ArangoDB revealed major pitfalls in naive measurement: geographic placement of managed instances skewed raw latency numbers, server-reported execution time (via Bolt drivers) is required for fair engine-to-engine comparison, and CognoDB v0.9.11 exhibited a background-indexing behaviour that caused indexed lookups to return no results while an index was building, silently dropping relationship writes. Concurrency characteristics differed: cloud-hosted databases scaled with client concurrency due to network latency hiding server idle time, while local instances became CPU-bound and slowed. The author published the full harness and raw data on GitHub.
- Five graph databases were benchmarked: CognoDB Cloud, Neo4j AuraDB, Memgraph, FalkorDB and ArangoDB.
- Geographic placement of managed cloud instances (e.g., CognoDB in Google Cloud us-east4 and Neo4j AuraDB in Google's Singapore range) skewed raw wall-clock latency measurements.
- Using server-reported execution time via Bolt drivers (network excluded) was necessary to fairly compare engines in different regions.
Compare their exact ecosystem overlaps.
Explore all deep relationships in Polaris7. Discover exactly which mutual clients, integrated technologies, and overlapping partners MongoDB and Neo4j share across the market ecosystem.
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