MongoDB Reviews & Product Details

I love how fast and easy it is to spin up a new cluster with MongoDB and store data. The ability to handle unstructured JSON data quickly and provide fast retrieval is incredibly valuable, especially for my AI-based application that utilizes data from web scraping. I find the document store feature of MongoDB particularly beneficial because it allows me to store any type of data without needing to create structures, which simplifies my development process significantly. The simplicity and ease of initial setup in MongoDB make it an ideal first choice for my database needs. Overall, my experience with MongoDB has been very positive, and I am likely to recommend it to others. Review collected by and hosted on G2.com.

I find it challenging to learn MongoDB with non-popular tech stacks like C# Dotnet Core Web API. There aren't enough resources available on the internet to facilitate learning how to effectively use MongoDB with Dotnet Core. Review collected by and hosted on G2.com.

I like the basic architecture of MongoDB and how easy it is to find my JSON with Python libraries. It provides a good score with Python libraries, making data export, encryption, and decryption very easy. The latest feature about vector databases is just amazing for me as an AI engineer and has changed the landscape for me. I no longer need to use any other vector database, and I'm really comfortable using MongoDB. The initial setup was very easy, especially with the Mongo Compass and the resources provided for Python, which make it easier than any other setup. Review collected by and hosted on G2.com.

The major thing with the record databases is that you need to set them up manually most of the time. I would prefer if there's a setup to define everything from Python code rather than having to go into the Mongo interface and change it there. They don't provide automatic integration of vectorized databases from the Python code, which is a bit of a setback for me. Review collected by and hosted on G2.com.

MongoDB is a highly flexible database system that brings several benefits, including support for document nesting and partial indexing across various fields. It continues to allow for robust aggregations, enabling the use of filters and regex operations. At the same time, MongoDB offers a more developer-friendly approach to viewing and modifying any JSON-type documents.

We have choose MongoDB for its inherit flexible and because it scales very well, which is exactly what we need. Review collected by and hosted on G2.com.

At times, it can be difficult to determine exactly how much data is being retrieved during an aggregation. Having this information readily available while performing such actions—whether in MongoDB Compass or similar tools—would be a significant advantage for assessing performance concerns and overall efficiency. Review collected by and hosted on G2.com.

MongoDB's best part is the flexibility it gives you as a developer. That schema-less structure makes it super easy to just start building something without overthinking all your tables and relations like you do in SQL. On my last project, we had to handle this dynamic insurance data where the fields weren't fixed at all, and Mongo just handled it perfectly. It's really easy to use, especially if you're already comfortable with JSON, 'cause the documents just feel natural. Integrating it with Spring Boot was smooth too – I didn't have to spend a ton of time configuring things, you basically just plug in the driver and go. Implementation-wise, it's not super heavy compared to some other databases, and scaling with replica sets and sharding works decent once you get the hang of it. For customer support, I've never used the enterprise version, but the community forums and the docs are pretty strong; I usually find answers quick. I use MongoDB a lot for side projects and at work, especially when the speed of development matters more than having a super strict schema.Overall, it just feels modern and fast and developer-friendly. It might not be the perfect choice for every single thing, but for projects where the requirements are always changing, MongoDB really saves you time. Review collected by and hosted on G2.com.

Yeah, what I don't love about MongoDB is how the performance can just fall off if you don't stay on top of your indexes. At first everything's super fast, but once your data gets bigger, some queries just start dragging and you realize you gotta spend all this time tuning indexes.And they do have transactions now, which is good, but it's still not as strong or smooth as what you get with a relational DB like Postgres. For stuff where you need really strict consistency, Mongo can feel a little risky sometimes. I also think the aggregation framework has a pretty high learning curve. Some queries that would just be a simple JOIN in SQL end up being these crazy long pipelines in Mongo, and it can get messy. It's a solid tool for sure, but it's definitely not a "set it and leave" kind of deal. You really gotta keep an eye on it and tune things regularly. Review collected by and hosted on G2.com.

Basically being as a developer i used it for making my schemas for backend in database and it have advantages of bson type structure which helps me to store the values of realtime type data and it helps me to implement aggregation pipeline as well as use the free tier of database of 512 mb which can be used in mongodb compass Review collected by and hosted on G2.com.

Nothing everything is fine just vpc connection is quite harder for new person Review collected by and hosted on G2.com.

Flexible schema, scalability and high performance ,built in horizontal scaling with sharding (distributes data across multiple servers). Review collected by and hosted on G2.com.

When it comes to data consistency compared to SQL, this system by default emphasizes availability and partition tolerance, as described by the CAP theorem. Achieving strong consistency is possible, but it demands careful setup, particularly in configuring write concerns and read preferences. Review collected by and hosted on G2.com.

The flexibility of its schema, and the amount of scalability it provides, be it horizontal or vertical. Aggregation & high availability makes it a wonderful choice. Extremely easy to use and integrate with any language, make it the first choice. Review collected by and hosted on G2.com.

Duplicity and no native joins are a concern. Review collected by and hosted on G2.com.

MongoDB excels with its flexible document model, which allows for dynamic schema design—making it perfect for fast-paced development cycles and agile teams. Its JSON-like BSON storage format aligns well with modern JavaScript-based stacks like MERN, ensuring smooth data handling from front end to backend. The built-in horizontal scaling, support for geospatial queries, and full-text search are game-changers in terms of versatility. Review collected by and hosted on G2.com.

While MongoDB is powerful, it does have some drawbacks. It can consume significantly more storage than traditional relational databases due to its denormalized document structure. For applications with complex relationships, performance tuning requires careful indexing and schema design. Review collected by and hosted on G2.com.

MongoDB’s document-oriented architecture uses JSON-like BSON documents for flexible, schema-less data storage. This allows dynamic adaptation to evolving data structures without rigid schema migrations.

Horizontal scalability via sharding efficiently distributes data across clusters, enabling seamless handling of massive datasets.

The aggregation framework supports complex data transformations, while built-in geospatial indexing and full-text search expand analytical capabilities.

Transaction support (from v4.0) ensures ACID compliance for multi-document operations. Review collected by and hosted on G2.com.

The transition from SQL can involve a steep learning curve, particularly for complex queries and aggregation pipelines.

Storage consumption is higher than relational databases due to denormalized data structures, impacting cost efficiency at scale.

While transactions are supported, performance tuning for complex ACID operations requires meticulous indexing and schema design. Review collected by and hosted on G2.com.

Below are the features which i like the most about the db:

1. scalability and performance

2. robust managed service

3. flexible data model Review collected by and hosted on G2.com.

The only things that concern me are the cost of the D,B which can spike with the scale Review collected by and hosted on G2.com.