Effortless Setup, Needs Better Vectorization Support
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
I use MongoDB to easily store non-SQL data like JSON objects. It streamlines storing embeddings and integrates well with Python, saving me time and effort. MongoDB's ease of use and vector database feature are game-changers for my AI work.
Perfect for Developers: Flexible Schemas and Powerful Aggregation
What do you like best about the product?
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
What do you dislike about the product?
Nothing everything is fine just vpc connection is quite harder for new person
What problems is the product solving and how is that benefiting you?
Basically we are having chatting software as well as run time key addtion requirement at that time we have implement it for saving the data of different key as well as json type structure and it gives us benefits on pipeline of that schemas as. well
Flexible and scalable documental database system!
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
MongoDB serves as the primary database system for our team, handling both the storage and retrieval of business data. We rely on it across all our microservices, organizing different areas of concern by using separate namespaces.
Effortless Document Storage, Steep Learning Curve with C#
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
I use MongoDB for fast storage and quick retrieval of unstructured JSON data, which simplifies managing data from my AI-based application without needing predefined structures.
Developers have benefited from flexibility and performance but pricing has needed further attention
What is our primary use case?
I still have recent experience with MongoDB Atlas as I have a contact with a representative for Brazil.
Azure and OCI are what we use as our main cloud providers.
I have hands-on experience with OCI, although I don't have a cloud for MongoDB Atlas; I have a cloud for databases and DevOps.
I don't develop directly with only MongoDB Atlas. However, I know the organization has a license with the product.
What is most valuable?
It's a very elastic solution for the purposes of our systems and the developers appreciate it for software development.
MongoDB Atlas's encryption capabilities help ensure data confidentiality and integrity.
I believe the software has performed well for us regarding data confidentiality and integrity.
What needs improvement?
I would say pricing is an area where MongoDB Atlas could improve.
For how long have I used the solution?
I don't have extensive experience with Linux products since it's not my area in my organization.
What do I think about the stability of the solution?
I believe the support is very good because I don't have a problem with the availability of the software.
What do I think about the scalability of the solution?
I am aware of the horizontal scaling capability.
How are customer service and support?
I would be willing to provide a review for one of the Oracle solutions or other solutions such as Linux as we have a Linux server, X8H56. OCI is the server name I remember, it's OCP.
How would you rate customer service and support?
Which solution did I use previously and why did I switch?
Our main cloud provider is Azure, not AWS.
We have MongoDB Atlas; MongoDB Atlas is what we use.
How was the initial setup?
I have tried to use Coherence, but it was a bad experience for us.
I didn't purchase MongoDB Atlas through AWS Marketplace; I only have a MongoDB Atlas license, not AWS.
What about the implementation team?
I have no idea about the pricing or setup cost with MongoDB Atlas.
What was our ROI?
I find it easy to use.
I think it's a good product.
What's my experience with pricing, setup cost, and licensing?
I have no idea about the pricing or setup cost with MongoDB Atlas.
Which other solutions did I evaluate?
We have MongoDB Atlas; MongoDB Atlas is what we use.
What other advice do I have?
I am only familiar with databases and applications. I am from the development team and I am a user of database and cloud but I don't know the infrastructure.
As a user, I deal with the Oracle Database.
I know the organization has a license with the product.
We don't utilize real-time analytics with MongoDB Atlas.
I don't use MongoDB Atlas directly, so I don't know how it can be improved.
I would place MongoDB Atlas at a medium level. I would rate it at a six or seven. I believe MongoDB Atlas can improve a little. My overall review rating for this product is six out of ten.
Highly flexible and scalable database that can be used as general purpose db
What do you like best about the product?
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.
What do you dislike about the product?
Duplicity and no native joins are a concern.
What problems is the product solving and how is that benefiting you?
I had integrated ML features in my application, and SQL based databases, were a nightmare, so I switched over. Now, most of my applications are using mongo.
Easy Solution for data management
What do you like best about the product?
Flexible schema, scalability and high performance ,built in horizontal scaling with sharding (distributes data across multiple servers).
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
Rigid schemas are a hallmark of traditional SQL databases, which require you to define a fixed structure in advance. When your application changes rapidly—such as when you introduce new features or fields—updating the schema can be a cumbersome process. In contrast, MongoDB addresses this issue with its flexible schema approach, allowing you to add or modify fields in your documents without causing downtime.
Flexible Data Storage with Developer-Friendly Experience
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
So the main problem MongoDB solves for us is handling all this unstructured and semi-structured data. Like in our insurance systems, all these different partners send over data that's slightly different, with fields that are always changing or totally optional. With SQL it was a huge pain to constantly be altering tables, but with Mongo we just take the JSON and store it as-is, which honestly saves us a ton of time. We can just prototype and push feature super quick without getting stuck on some rigid schema designs. It makes the team way more agile and we don't have to rely on a DBA for every little schema change. Scalability is another area where it really helps. Once the dataset gets huge, we can scale out with replica sets or sharding without a massive rewrite on the code side. For stuff that's really read-heavy, it performs great—once you finally get the indexes sorted out anyway . Overall, it just lets us move faster, handle messy, evolving data, and there's a lot less friction between us backend devs and the whole database structure thing.
Seamless Managed MongoDB Experience
What do you like best about the product?
Below are the features which i like the most about the db:
1. scalability and performance
2. robust managed service
3. flexible data model
What do you dislike about the product?
The only things that concern me are the cost of the D,B which can spike with the scale
What problems is the product solving and how is that benefiting you?
Below are the major problems which is being solved by the mongodb
1.flexible data handling
2.high avaialability and scaling
3.Manged operation with atlas like auto backup , monitoring etc
Flexible and Scalable NoSQL Database
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
MongoDB eliminated rigid schema constraints, allowing our team to rapidly prototype and iterate applications with unstructured data. It streamlined handling high-velocity data streams in IoT projects, avoiding costly schema redesigns during development cycles. The scalability features supported our transition from prototype to production without operational bottlenecks, directly accelerating time-to-market for new features
