Software Alternatives, Accelerators & Startups

Qdrant VS MongoDB

Compare Qdrant VS MongoDB and see what are their differences

Qdrant logo Qdrant

Qdrant is a high-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/

MongoDB logo MongoDB

MongoDB (from "humongous") is a scalable, high-performance NoSQL database.
  • Qdrant Landing page
    Landing page //
    2023-12-20

Qdrant is a leading open-source high-performance Vector Database written in Rust with extended metadata filtering support and advanced features. It deploys as an API service providing a search for the nearest high-dimensional vectors. With Qdrant, embeddings or neural network encoders can be turned into full-fledged applications. Powering vector similarity search solutions of any scale due to a flexible architecture and low-level optimization. Qdrant is trusted and high-rated by Machine Learning and Data Science teams of top-tier companies worldwide.

  • MongoDB Landing page
    Landing page //
    2023-10-21

Qdrant

$ Details
freemium
Platforms
Linux Windows Kubernetes Docker
Release Date
2021 May

MongoDB

Pricing URL
-
$ Details
Platforms
-
Release Date
-

Qdrant features and specs

  • Advanced Filtering
  • On-disc Storage
  • Scalar Quantization
  • Product Quantization
  • Binary Quantization
  • Sparse Vectors
  • Hybrid Search
  • Discovery API
  • Recommendation API

MongoDB features and specs

  • Scalability
    MongoDB offers horizontal scaling through sharding, allowing it to handle large volumes of data and enabling distributed computing.
  • Flexible Schema
    It allows for a flexible schema design using BSON (Binary JSON), making it easier to iterate and change application data models.
  • High Performance
    MongoDB is optimized for read and write throughput, making it suitable for real-time applications.
  • Rich Query Language
    Supports a rich and expressive query language that allows for efficient querying and analytics.
  • Built-in Replication
    Provides robust replication mechanisms for high availability and redundancy.
  • Geospatial Indexing
    Offers powerful geospatial indexing capabilities, useful for location-based applications.
  • Aggregation Framework
    Enables complex data manipulations and transformations using the aggregation pipeline framework.
  • Cross-Platform
    Works on multiple operating systems, enhancing its versatility and deployment options.

Possible disadvantages of MongoDB

  • Memory Usage
    MongoDB can consume a large amount of memory due to its use of memory-mapped files, which may be a concern for some applications.
  • Complex Transactions
    While MongoDB supports ACID transactions, they can be more complex to implement and less efficient compared to traditional relational databases.
  • Data Redundancy
    The flexible schema design can lead to data redundancy and increased storage costs if not managed carefully.
  • Limited Joins
    Joins are supported but can be less efficient and more limited compared to relational databases, affecting complex relational data querying.
  • Indexing Overhead
    Extensive indexing can introduce overhead and impact performance, especially during write operations.
  • Learning Curve
    Requires a different mindset and understanding compared to traditional relational databases, which can present a learning curve for new users.
  • Lacks Mature Analytical Tools
    The ecosystem for analytical tools around MongoDB is not as mature as those for traditional relational databases, which might limit advanced analytics capabilities.
  • Cost
    The cost of using MongoDB's cloud services (MongoDB Atlas) can be high, especially for large-scale deployments.

Qdrant videos

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MongoDB videos

MySQL vs MongoDB

More videos:

  • Review - The Good and Bad of MongoDB
  • Review - what is mongoDB

Category Popularity

0-100% (relative to Qdrant and MongoDB)
Databases
14 14%
86% 86
Search Engine
100 100%
0% 0
NoSQL Databases
0 0%
100% 100
Custom Search Engine
100 100%
0% 0

Questions and Answers

As answered by people managing Qdrant and MongoDB.

Why should a person choose your product over its competitors?

Qdrant's answer

Advanced Features, Performance, Scalability, Developer Experience, and Resources Saving.

What makes your product unique?

Qdrant's answer

Highest performance https://qdrant.tech/benchmarks/, scalability and ease of use.

Which are the primary technologies used for building your product?

Qdrant's answer

Qdrant is written completely in Rust. SDKs available for all popular languages Python, Go, Rust, Java, .NET, etc.

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare Qdrant and MongoDB

Qdrant Reviews

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MongoDB Reviews

10 Top Firebase Alternatives to Ignite Your Development in 2024
MongoDB’s superpower lies in its flexibility. Its document-based model lets you store data in a free-form, schema-less way, making it adaptable to evolving application needs. Need to add a new field or change the structure of your data? No problem, MongoDB handles it with ease.
Source: genezio.com
Top 7 Firebase Alternatives for App Development in 2024
MongoDB Realm provides a robust alternative to Firebase, especially for apps requiring a flexible data model. Key features include:
Source: signoz.io
Announcing FerretDB 1.0 GA - a truly Open Source MongoDB alternative
MongoDB is no longer open source. We want to bring MongoDB database workloads back to its open source roots. We are enabling PostgreSQL and other database backends to run MongoDB workloads, retaining the opportunities provided by the existing ecosystem around MongoDB.
16 Top Big Data Analytics Tools You Should Know About
The database added a new feature to its list of attributes called MongoDB Atlas. It is a global cloud database technology that allows to deploy a fully managed MongoDB across AWS, Google Cloud, and Azure with its built-in automation for resource, workload optimization and to reduce the time required to handle the database.
9 Best MongoDB alternatives in 2019
MongoDB is an open source NoSQL DBMS which uses a document-oriented database model. It supports various forms of data. However, in MongoDB data consumption is high due to de-normalization.
Source: www.guru99.com

Social recommendations and mentions

Based on our record, Qdrant should be more popular than MongoDB. It has been mentiond 57 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

Qdrant mentions (57)

  • 10 open-source MCPs that make your AI agents smarter than your team lead
    Qdrant — open-source and super developer-friendly. - Source: dev.to / 1 day ago
  • Build Code-RAGent, an agent for your codebase
    The only thing left to do then was to build something that could showcase the power of code ingestion within a vector database, and it immediately clicked in my mind: "Why don't I ingest my entire codebase of solved Go exercises from Exercism?" That's how I created Code-RAGent, your friendly coding assistant based on your personal codebases and grounded in web search. It is built on top of GPT-4.1, powered by... - Source: dev.to / 11 days ago
  • Ingest (almost) any non-PDF document in a vector database, effortlessly
    Qdrant is an easy-to-set-up, highly performing and scalable vector database, that offers numerous functionalities (among which hybrid search and metadata filtering). - Source: dev.to / 15 days ago
  • Why You Shouldn’t Invest In Vector Databases?
    In cases where a company possesses a strong technological foundation and faces a substantial workload demanding advanced vector search capabilities, its ideal solution lies in adopting a specialized vector database. Prominent options in this domain include Chroma (having raised $20 million), Zilliz (having raised $113 million), Pinecone (having raised $138 million), Qdrant (having raised $9.8 million), Weaviate... - Source: dev.to / 16 days ago
  • Preview 2 of .NET AI Chat Web App Template Now Available
    /filters:no_upscale()/news/2025/04/microsoft-dotnet-ai-template-p2/en/resources/1use-aspire-orchestration-1745167526397.png) A notable addition in Preview 2 is the support for .NET Aspire, enhancing the development toolkit with advanced AI capabilities. The Qdrant vector database can be utilized alongside .NET Aspire to create scalable applications. The template continues to utilize the Retrieval Augmented... - Source: dev.to / 19 days ago
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MongoDB mentions (18)

  • Creating AI Memories using Rig & MongoDB
    In this article, we’ll build a CLI tool using the Rig AI framework and MongoDB for retrieval-augmented generation (RAG). This tool will store summarized conversations in a database and retrieve them when needed, enabling the AI to maintain context over time. - Source: dev.to / about 2 months ago
  • The Adventures of Blink S2e2: Database, Contained
    Have a Mongo database holding the various phrases we're going to use and potentially configuration data for the frontend as well. - Source: dev.to / 9 months ago
  • Introducing Perseid: The Product-oriented JS framework
    It's also worth mentioning that Perseid provides out-of-the-box support for React, VueJS, Svelte, MongoDB, MySQL, PostgreSQL, Express and Fastify. - Source: dev.to / 8 months ago
  • DocumentDB Elastic Cluster Pricing
    Does anyone know if the most basic Elastic Cluster instance of DocumentDB carries any monthly fixed cost or is it just on-demand cost? Another words if I run like 10,000 queries against the DB per month, what kind of bill would I expect? This is for a super small app. I am currently using mongodb free tier , but want to migrate everything to AWS. Can't seem to find a straight answer to the pricing question. Source: over 2 years ago
  • I wrote some scripts for converting the UTZOO Usenet archive to a Mongo Database
    You can use either MongoDB.com's dashboard (if you host a remote database) or Mongo Compass to run queries on the data or you can modify the express middleware with your own queries. I'm still working on the API, so it's not very robust yet. I will update this when it is. Source: over 2 years ago
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What are some alternatives?

When comparing Qdrant and MongoDB, you can also consider the following products

Milvus - Vector database built for scalable similarity search Open-source, highly scalable, and blazing fast.

Redis - Redis is an open source in-memory data structure project implementing a distributed, in-memory key-value database with optional durability.

Vespa.ai - Store, search, rank and organize big data

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

Weaviate - Welcome to Weaviate

MySQL - The world's most popular open source database