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.
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.
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.
Weaviate Product overview
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.
Detailed feature comparison
| Feature | MongoDB | Weaviate |
|---|---|---|
| Primary category | Vector Database | Vector Database |
| Added | 2025-08-06 | 2025-09-10 |
| Pricing | Freemium | Freemium |
| Official website | www.mongodb.com | weaviate.io |
| Product type | Website | Website |
| Performance data | ||
| User rating | Not verified | Not verified |
| Comments | 0 | 0 |
| Monthly visits | 5.8M | 137.9K |
| Monthly growth | -6.2% | -18.5% |
| Favorites | 134 | 114 |
| Details | View details | View details |
MongoDB vs Weaviate monthly traffic
Compare MongoDB and Weaviate by monthly reach, traffic trend, visit depth, top regions, and acquisition sources.
How to interpret the traffic data
In the MongoDB vs Weaviate monthly traffic comparison, MongoDB currently shows 5.8M visits and Weaviate shows 137.9K; MongoDB has about 42.4 times the visible traffic of Weaviate, 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 |
|---|---|---|
| ๐ฎ๐ณIndia | 58.41% | 3.4M |
| ๐บ๐ธUnited States | 26.77% | 1.6M |
| ๐ฌ๐งUnited Kingdom | 5.84% | 341.3K |
| ๐ต๐ฐPakistan | 4.71% | 275.2K |
| ๐จ๐ดColombia | 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
Weaviate monthly traffic:
Latest traffic
Monthly traffic trend
- 2025/9: 287.8K Monthly visits
- 2026/1: 188.9K Monthly visits
- 2026/2: 165.9K Monthly visits
- 2026/3: 184.5K Monthly visits
- 2026/4: 169.2K Monthly visits
- 2026/5: 137.9K Monthly visits
Top regions
Top 5 countries/regions
| Country/region | Percentage | Traffic |
|---|---|---|
| ๐ฎ๐ณIndia | 40.22% | 55.5K |
| ๐บ๐ธUnited States | 29.54% | 40.7K |
| ๐ป๐ณVietnam | 12% | 16.6K |
| ๐ฌ๐งUnited Kingdom | 9.72% | 13.4K |
| ๐จ๐ณChina | 8.52% | 11.8K |
Traffic sources
| Source type | Percentage | Traffic |
|---|---|---|
| Direct | 64.6% | 89.1K |
| Referral | 30.48% | 42K |
| 4.92% | 6.8K |
Search keywords
Usage comparison
Compare the core capabilities of MongoDB and Weaviate
MongoDB Core features
Weaviate Core features
Use cases
MongoDB Use cases
Weaviate Use cases
Best suited roles
MongoDB Best suited roles
Weaviate Best suited roles
MongoDB vs Weaviate๏ผIn-depth comparison and selection guidance
First decide whether the products solve the same kind of need
This in-depth MongoDB vs Weaviate comparison uses only the product records, taxonomy, audience, traffic, and community signals available on this page. MongoDB is primarily listed under โVector Databaseโ, while Weaviate is primarily listed under โVector Databaseโ, 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; Weaviate: 137.9K); Monthly growth (MongoDB: -6.2%; Weaviate: -18.5%); Favorites (MongoDB: 134; Weaviate: 114); Website (MongoDB: www.mongodb.com; Weaviate: weaviate.io); Added (MongoDB: 2025-08-06; Weaviate: 2025-09-10). These facts are more useful for selection than brand visibility alone.
What market visibility and monthly traffic mean
In the MongoDB vs Weaviate monthly traffic comparison, MongoDB currently shows 5.8M visits and Weaviate shows 137.9K; MongoDB has about 42.4 times the visible traffic of Weaviate, 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 Weaviate currently overlap in shared categories: Vector Database and Database; shared tags: database. 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, Data Management, Atlas, backend, cloud database, data management, developer platform, and generative AI; Weaviate's are AI-native, developer tool, hybrid search, machine learning, NLP, open source, RAG, and semantic search. 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, 134 favorites, and 126 likes๏ผWeaviate has no verified rating, 0 comments, 114 favorites, and 119 likesใ
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 โVector Databaseโ and especially Backend, Data Management, Atlas, backend, cloud database, 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 Weaviate first
Put Weaviate on the priority trial list when the task aligns with โVector Databaseโ and especially AI-native, developer tool, hybrid search, machine learning, NLP, and open source, or the users include AI Researcher, Data Scientist, DevOps Engineer, and Machine Learning Engineer. This follows recorded positioning and does not imply unlisted capabilities are absent.
Weaviate also currently records: pricing is freemium, product type is website, 137.9K 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 Weaviate, 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 Weaviate?
Where does this comparison data come from?
What do unknown fields mean?
Related AI tools

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
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
Zilliz
Zilliz is an enterprise-grade vector database built for scalable AI applications. Powered by the popular open-source project Milvus, it provides a high-performance, cost-effective, and fully-managed service (Zilliz Cloud) for storing, indexing, and searching billions of vector embeddings. It's designed to power applications like RAG, recommendation systems, and multimodal search, with seamless integrations into major AI frameworks and cloud platforms.
Machine Learning
infiniflow
infiniflow is a high-performance, open-source, AI-native database specifically designed for LLM applications. It offers incredibly fast vector search, powerful hybrid search capabilities (vector, full-text, tensor), and simplified deployment. With an intuitive Python API, it's built to power demanding AI tasks like Retrieval-Augmented Generation (RAG) and semantic search with millisecond latency.
Vector Search
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
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
Vespa.ai
Vespa.ai is a high-performance AI search platform for building large-scale applications. It unifies vector search, text search, and machine-learned ranking to power advanced use cases like Retrieval-Augmented Generation (RAG), recommendation engines, and intelligent search. Designed for real-time inference and scalability, it's trusted by leading companies like Spotify and Perplexity to handle massive datasets with low latency.
Search
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
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
Neosync
Neosync is an open-source platform for data anonymization and synthetic data generation. It helps developers and data scientists create safe, privacy-compliant, and realistic datasets for testing, development, and AI model training, ensuring referential integrity across databases.
Data Generation
LastMile AI
LastMile AI is an enterprise-grade developer platform for testing, evaluating, and monitoring generative AI applications. It provides tools like AutoEval for custom evaluator fine-tuning, synthetic data generation, and real-time monitoring to ensure AI systems are reliable and production-ready.
Model Evaluation
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
SingleStore
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.
Vector Database
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
LanceDB
LanceDB is an open-source, AI-native multimodal lakehouse designed for building and scaling AI applications. It provides a unified platform for storing, searching, and managing complex data like text, images, voice, and vectors. Ideal for RAG, semantic search, and model training, LanceDB offers blazing-fast hybrid search, massive scalability to petabytes, and significant cost savings, making it a powerful foundation for enterprise-grade AI.
Vector Database



