MongoDB is a developer data platform built on a leading NoSQL document database. Its cloud offering, MongoDB Atlas, provides an integrated suite of services, including powerful Vector Search for generative AI, full-text search, and real-time analytics. It's designed for modern applications, offering flexibility, scalability, and a unified experience for developers to build faster and more efficiently across multiple clouds.
SingleStore is a high-performance, real-time data platform designed for enterprise AI and data-intensive applications. It unifies transactional (OLTP) and analytical (OLAP) workloads, including vector search, in a single, distributed SQL database, delivering millisecond latency at scale.
Product overview
MongoDB Product overview
MongoDB is a developer data platform built on a leading NoSQL document database. Its cloud offering, MongoDB Atlas, provides an integrated suite of services, including powerful Vector Search for generative AI, full-text search, and real-time analytics. It's designed for modern applications, offering flexibility, scalability, and a unified experience for developers to build faster and more efficiently across multiple clouds.
SingleStore Product overview
SingleStore is a high-performance, real-time data platform designed for enterprise AI and data-intensive applications. It unifies transactional (OLTP) and analytical (OLAP) workloads, including vector search, in a single, distributed SQL database, delivering millisecond latency at scale.
Detailed feature comparison
| Feature | MongoDB | SingleStore |
|---|---|---|
| Primary category | Vector Database | Vector Database |
| Added | 2025-08-06 | 2025-08-15 |
| Pricing | Freemium | Freemium |
| Official website | www.mongodb.com | www.singlestore.com |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 5.8M | 161K |
| Monthly growth | -6.2% | 31.5% |
| Favorites | 127 | 132 |
| Details | View details | View details |
MongoDB vs SingleStore monthly traffic
Compare MongoDB and SingleStore by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the MongoDB vs SingleStore monthly traffic comparison, MongoDB currently shows 5.8M visits and SingleStore shows 161K; MongoDB has about 36.3 times the visible traffic of SingleStore, an absolute difference of about 5.7M visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
MongoDB monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 5.6M Monthly visits
- 2026/1: 5.8M Monthly visits
- 2026/2: 5.5M Monthly visits
- 2026/3: 6.1M Monthly visits
- 2026/4: 6.2M Monthly visits
- 2026/5: 5.8M Monthly visits
Top regions
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇮🇳India | 58.41% | 3.4M |
| 🇺🇸United States | 26.77% | 1.6M |
| 🇬🇧United Kingdom | 5.84% | 341.3K |
| 🇵🇰Pakistan | 4.71% | 275.2K |
| 🇨🇴Colombia | 4.27% | 249.5K |
Traffic sources
| Source type | Percentage | Traffic |
|---|---|---|
| Direct | 86.31% | 5M |
| Referral | 10.12% | 591.4K |
| 3.57% | 208.6K |
Search keywords
SingleStore monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 178.9K Monthly visits
- 2026/1: 167.6K Monthly visits
- 2026/2: 141.6K Monthly visits
- 2026/3: 156.9K Monthly visits
- 2026/4: 122.5K Monthly visits
- 2026/5: 161K Monthly visits
Top regions
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| 🇮🇳India | 62.41% | 100.5K |
| 🇺🇸United States | 19.21% | 30.9K |
| 🇰🇷Korea, Republic of | 9.24% | 14.9K |
| 🇭🇷Croatia | 5.14% | 8.3K |
| 🇵🇹Portugal | 4% | 6.4K |
Traffic sources
| Source type | Percentage | Traffic |
|---|---|---|
| Direct | 70.02% | 112.7K |
| Referral | 20.65% | 33.3K |
| 9.33% | 15K |
Search keywords
Usage comparison
Compare the core capabilities of MongoDB and SingleStore
MongoDB Core features
SingleStore Core features
Use cases
MongoDB Use cases
SingleStore Use cases
MongoDB vs SingleStore:In-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth MongoDB vs SingleStore comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. MongoDB is primarily listed under “Vector Database”, while SingleStore is primarily listed under “Vector Database”, so the first decision is whether your actual task matches their recorded scope.
The structured fields currently show these decision-relevant differences: Monthly visits (MongoDB: 5.8M; SingleStore: 161K); Monthly growth (MongoDB: -6.2%; SingleStore: 31.5%); Favorites (MongoDB: 127; SingleStore: 132); Website (MongoDB: www.mongodb.com; SingleStore: www.singlestore.com); Added (MongoDB: 2025-08-06; SingleStore: 2025-08-15). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the MongoDB vs SingleStore monthly traffic comparison, MongoDB currently shows 5.8M visits and SingleStore shows 161K; MongoDB has about 36.3 times the visible traffic of SingleStore, an absolute difference of about 5.7M visits. This reflects visible reach, not feature quality or paid users.
Both tools provide verified traffic details, so monthly trends, visit depth, regions, and acquisition sources can be compared on the same basis.
If public market visibility is an important first-pass criterion, investigate MongoDB first. The final choice should still follow taxonomy, use case, and a real trial because higher traffic does not prove broader capabilities or better workflow fit.
Product positioning, use cases, and roles
MongoDB and SingleStore currently overlap in shared categories: Vector Database; shared tags: cloud database, database, and generative AI. This can place both on the same shortlist, but it does not prove equal implementation, depth, or cost.
MongoDB's unique categories/tags are Backend, Database, Data Management, Atlas, backend, data management, developer platform, and nosql; SingleStore's are Database, Backend, data platform, OLAP, OLTP, RAG, real-time analytics, and SQL. These unique fields are the strongest differentiators: validate the product whose recorded scope matches the task instead of following traffic alone.
What ratings, comments, and favorites can tell you
MongoDB has no verified rating, 0 comments, 127 favorites, and 123 likes;SingleStore has no verified rating, 0 comments, 132 favorites, and 132 likes。
Neither product has enough rating or comment samples for a credible reputation ranking.
Selection guidance by actual need
When to evaluate MongoDB first
Put MongoDB on the priority trial list when the task aligns with “Vector Database” and especially Backend, Database, Data Management, Atlas, backend, and data management. This follows recorded positioning and does not imply unlisted capabilities are absent.
MongoDB also currently records: pricing is freemium, product type is website, 5.8M verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
When to evaluate SingleStore first
Put SingleStore on the priority trial list when the task aligns with “Vector Database” and especially Database, Backend, data platform, OLAP, OLTP, and RAG. This follows recorded positioning and does not imply unlisted capabilities are absent.
SingleStore also currently records: pricing is freemium, product type is website, 161K verified monthly visits, no verified user rating. Verify any hard requirement around price, platform, or reach before trial, and do not let sparse review data substitute for testing.
How to validate the recommendation before deciding
The available data describes positioning, public visibility, and community signals, but it cannot prove output quality, speed, integration effort, privacy, or long-term cost in your workflow. Before deciding, run the same representative tasks in MongoDB and SingleStore, then record completion time, accuracy, manual corrections, and the real paid threshold. A like-for-like trial turns this comparison into a defensible adoption decision.
Comparison FAQ
How should I choose between MongoDB and SingleStore?
Where does this comparison data come from?
What do unknown fields mean?
Related AI tools

SurrealDB
SurrealDB is a next-generation, multi-model cloud database designed for modern applications. It simplifies backend development by unifying document, relational, graph, and time-series models with built-in full-text search, vector search, and in-database machine learning. Built for scalability and real-time data, it empowers developers to build complex, AI-powered applications with unprecedented ease and speed.
Vector Database
TiDB Cloud
TiDB Cloud is a fully managed, distributed SQL database-as-a-service (DBaaS). It offers horizontal scalability, MySQL compatibility, and Hybrid Transactional/Analytical Processing (HTAP) capabilities. Ideal for building modern, data-intensive applications and AI-powered services, it simplifies database operations and provides a powerful backend for applications that require both real-time transactions and complex analytics, including vector search for AI.
Vector Database
MindsDB
MindsDB is an open-source AI layer for databases, enabling developers to build, train, and deploy AI models and agents using standard SQL. It connects to hundreds of data sources, unifies structured and unstructured data into knowledge bases, and allows you to get AI-powered answers directly from your data without complex ETL pipelines.
Machine Learning
Unbody
Unbody is an AI-native development stack, described as the "Supabase of the AI Era." It provides developers with a modular, open-source backend featuring built-in agents, vector storage, and a unified API. This allows for the rapid and cost-effective creation of intelligent, adaptive applications by transforming any data into a queryable knowledge base, eliminating the need for fragmented systems and complex AI pipelines.
Vector Database
InfluxData
InfluxData offers InfluxDB, the leading time series database platform built for real-time data and AI applications. It empowers developers to ingest, store, and analyze massive volumes of high-velocity data from IoT, applications, and infrastructure. Featuring high-performance querying, superior data compression, and seamless integration with data lakes and AI/ML pipelines, InfluxData is the engine for anomaly detection, predictive maintenance, and autonomous systems.
Data Management
Seek AI
Seek AI is a generative AI platform for data analytics that empowers users to query databases, generate reports, and create visualizations using natural language. It automates the text-to-SQL process, making data accessible to non-technical users and accelerating insights for data teams.
Business Intelligence
TiDB AI Assistant
An intelligent AI assistant for the TiDB database. Powered by a Knowledge Graph-based RAG system using TiDB Serverless Vector Storage, it provides fast, accurate answers to all your TiDB-related questions, from technical specifications to best practices.
Database
PostgresML
PostgresML is a powerful open-source extension that integrates machine learning and AI directly into your PostgreSQL database. It enables GPU-accelerated inference, vector search, and complete RAG pipelines using simple SQL commands, eliminating data movement and simplifying the MLOps stack for high-performance, scalable AI applications.
Mlops
Chroma
Chroma is the open-source, AI-native retrieval database designed for building powerful AI applications with Retrieval-Augmented Generation (RAG). It simplifies storing and searching embeddings, documents, and metadata, offering vector search, full-text search, and a scalable, serverless cloud platform. It's built to be easy to use, cost-effective, and powerful, from local development to large-scale production.
Vector Database
Weaviate
Weaviate is an open-source, AI-native vector database designed for developers. It enables scalable, low-latency vector, keyword, and hybrid search. Ideal for building AI applications like semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) systems, it integrates seamlessly with popular machine learning models to store and query data based on semantic meaning.
Vector Database
ClickHouse
ClickHouse is a high-performance, open-source, column-oriented OLAP database management system. It's designed for real-time analytics on large-scale data, enabling blazing-fast queries for observability, business intelligence, ML/GenAI, and more, while remaining resource-efficient and cost-effective.
Databases
Milvus
Milvus is a high-performance, open-source vector database built for AI applications. It enables developers to manage and search through billions of high-dimensional vectors with minimal latency. Ideal for building scalable systems like retrieval-augmented generation (RAG), recommendation engines, and semantic search, Milvus offers flexible deployment options from local prototyping to large-scale distributed clusters.
Machine Learning
Meilisearch
Meilisearch is an open-source, lightning-fast, and AI-powered search engine. It's designed for developers to easily integrate advanced search capabilities, including full-text, semantic, and hybrid search, into any website or application. It offers an exceptional developer experience with powerful APIs and SDKs.
Database
Vanna.AI
Vanna.AI is an open-source, personalized AI SQL agent that transforms natural language questions into accurate SQL queries. It uses a Retrieval-Augmented Generation (RAG) model trained on your specific database schema, documentation, and past queries to achieve high accuracy on complex datasets. It's designed for security, flexibility, and easy integration into any application, empowering both technical and non-technical users to gain insights from their data effortlessly.
Business Intelligence
Datascale
Datascale is a cloud-based data modeling and lineage platform designed for modern data teams. It uses AI to automatically visualize SQL dependencies, creating interactive data lineage graphs and ER diagrams. The platform helps users understand data flow, document models visually, and manage a centralized data catalog. With GenAI-powered search and seamless API integration, Datascale simplifies data discovery, impact analysis, and collaboration, ensuring your data knowledge is always clear, connected, and up-to-date.
Analytics



