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MongoDB
Vector Database ยท 5.8M monthly visits

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.

VS
Weaviate
Vector Database ยท 137.9K monthly visits

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.

MongoDB vs Weaviate: pricing, features, traffic, and use cases

Compare MongoDB and Weaviate across positioning, pricing, traffic, and user feedback using structured factual data.

Updated Aug 19, 2026

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.

Preview

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.

Preview

Detailed feature comparison

FeatureMongoDBWeaviate
Primary categoryVector DatabaseVector Database
Added2025-08-062025-09-10
PricingFreemiumFreemium
Official websitewww.mongodb.comweaviate.io
Product typeWebsiteWebsite
Performance data
User ratingNot verifiedNot verified
Comments00
Monthly visits5.8M137.9K
Monthly growth-6.2%-18.5%
Favorites134114
DetailsView detailsView 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 visits
5.8M
Avg. visit duration
6:09
Pages per visit
8.88
Bounce rate
31.08%
Data updated 2026-06-15

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/regionPercentageTraffic
๐Ÿ‡ฎ๐Ÿ‡ณIndia58.41%3.4M
๐Ÿ‡บ๐Ÿ‡ธUnited States26.77%1.6M
๐Ÿ‡ฌ๐Ÿ‡งUnited Kingdom5.84%341.3K
๐Ÿ‡ต๐Ÿ‡ฐPakistan4.71%275.2K
๐Ÿ‡จ๐Ÿ‡ดColombia4.27%249.5K

Traffic sources

Source typePercentageTraffic
Direct86.31%5M
Referral10.12%591.4K
Email3.57%208.6K

Search keywords

atlasmongo dbmongodbmongodb atlasmongodb compass

Weaviate monthly traffic:

Latest traffic

Monthly visits
137.9K
Avg. visit duration
0:32
Pages per visit
1.74
Bounce rate
43.21%
Data updated 2026-06-15

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/regionPercentageTraffic
๐Ÿ‡ฎ๐Ÿ‡ณIndia40.22%55.5K
๐Ÿ‡บ๐Ÿ‡ธUnited States29.54%40.7K
๐Ÿ‡ป๐Ÿ‡ณVietnam12%16.6K
๐Ÿ‡ฌ๐Ÿ‡งUnited Kingdom9.72%13.4K
๐Ÿ‡จ๐Ÿ‡ณChina8.52%11.8K

Traffic sources

Source typePercentageTraffic
Direct64.6%89.1K
Referral30.48%42K
Email4.92%6.8K

Search keywords

agentic workflowscontext engineeringweaviateweaviate academyweaviate import data
Traffic-based selection guidance: 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.

Usage comparison

Compare the core capabilities of MongoDB and Weaviate

MongoDB Core features

Vector Database
Database
Backend
Data Management

Weaviate Core features

Vector Database
Database

Use cases

MongoDB Use cases

database
Atlas
backend
cloud database
data management
developer platform
generative AI
nosql
scalability
vector search

Weaviate Use cases

database
AI-native
developer tool
hybrid search
machine learning
NLP
open source
RAG
semantic search
vector database

Best suited roles

MongoDB Best suited roles

No verified data available

Weaviate Best suited roles

AI Researcher
Data Scientist
DevOps Engineer
Machine Learning Engineer
Product Manager
Software Developer

MongoDB vs Weaviate๏ผšIn-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 โ€œVector Databaseโ€, while Weaviate is primarily listed under โ€œVector 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๏ผ›Weaviate 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 โ€œVector 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 โ€œVector 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?
Compare positioning, pricing, taxonomy, and traffic maturity, then verify the latest details on each official website.
Where does this comparison data come from?
The factual baseline is derived from product, taxonomy, traffic, and community data. Reviewed editorial conclusions show their source and verification date.
What do unknown fields mean?
Unknown means there is not enough reliable evidence; the page does not fill gaps with assumptions.

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