We are using MongoDB Atlas for our log storage, transactional log storage, and we are into CPaaS business, communication platform as a service.
We are also using PostgresSQL in some of the applications, alongside MongoDB Atlas.
MongoDB Atlas (pay-as-you-go)
MongoDB, IncExternal reviews
500 reviews
from
and
External reviews are not included in the AWS star rating for the product.
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.
Ensures efficient team collaboration with quick deployment and easy integration
What is our primary use case?
What is most valuable?
The most valuable features of MongoDB Atlas in handling large data volumes include collection size and its NoSQL database capabilities.
The security features of MongoDB Atlas support our organization very well.
My company has seen financial benefits from using MongoDB Atlas because we are using open source.
What needs improvement?
There is nothing about MongoDB Atlas I would like to improve or any weak points at this time.
I have not thought through what other features I would like to see included in future updates.
MongoDB Atlas should support containerization.
For how long have I used the solution?
I have been using this product for the past 5 years.
What was my experience with deployment of the solution?
I find the installation process easy to deploy as it wasn't difficult to implement.
What do I think about the stability of the solution?
The stability of the product is very high, and I would rate it a nine out of ten for stability.
What do I think about the scalability of the solution?
It's very much scalable, and I would rate scalability a nine.
How are customer service and support?
For premium support, I would rate the support of MongoDB Atlas a nine.
Premium support requires additional payment; otherwise, you can manage whatever you can yourself.
Though I am currently not using support, I would rate it a nine.
How would you rate customer service and support?
Positive
How was the initial setup?
I personally took part in the installation process.
I can deploy MongoDB Atlas in 2-3 hours.
What about the implementation team?
When we make changes, responsibilities are always distributed. It will be a team whenever a production deployment comes.
What was our ROI?
My company has seen financial benefits from using MongoDB Atlas through savings because we are using open source.
Which other solutions did I evaluate?
Postgres is another option that is available for us. I have considered alternatives for MongoDB Atlas.
What other advice do I have?
The database team consists of five to six people.
We are not currently using the AI functionality in MongoDB Atlas, though AI-driven projects are available in their vector search.
Based on my experience, I would recommend MongoDB Atlas to other users looking for NoSQL databases.
We do everything on our own and are not using third-party services for maintenance.
I am involved in the maintenance process.
We are using MongoDB Atlas for commercial purposes.
The number of people currently using this product in my organization is related to my platform hosted on MongoDB Atlas.
I think it's a competitive solution compared to others, though I cannot comment on pricing as I haven't seen pricing for other products.
I rate MongoDB Atlas a nine out of ten.
Which deployment model are you using for this solution?
On-premises
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Best Document No Sql alternative
What do you like best about the product?
Ease of use, Ease of implementation, Ease of integration, and a lot of documentation online.
What do you dislike about the product?
Cluster reliability, database error protection, data consistency
What problems is the product solving and how is that benefiting you?
Database for a transactional payments system
Supportive features enable effective data management and growth
What is our primary use case?
I used MongoDB Atlas for structured data storage as part of an application service provided to one of our customers. The application was based on MongoDB and Atlas. While Google Cloud SQL was used for consulting, I interacted with Google Cloud but was not the final decision maker.
How has it helped my organization?
From an operational point of view, there were no costs associated with maintaining the database on my side, and service costs were acceptable from both my side and the customer’s perspective.
What is most valuable?
I find MongoDB Atlas highly scalable and easy to use, with very good support. The pricing is quite scalable and applies to various scenarios, both for smaller and bigger companies.
MongoDB Atlas has supported our data growth well, and my overall impression is very positive. It is easy to work with and has a reliable support structure. For structured data storage and performance, it provides a comprehensive solution, and the feedback was generally positive.
What needs improvement?
I am not an expert on what improvements could be made to MongoDB. The service is continually evolving with new features while maintaining reasonable pricing, making it attractive for developers.
For how long have I used the solution?
I have been using MongoDB Atlas since 2017 and Google Cloud Platform since 2018.
What do I think about the stability of the solution?
There are no issues mentioned regarding stability. I evaluated MongoDB Atlas as not the best solution for the application in the long term, specifically when the services consolidate themselves.
What do I think about the scalability of the solution?
MongoDB Atlas scales well and supports data growth effectively.
How are customer service and support?
The technical support is very good. I have used them sometimes, even recently, and found the feedback to be spot on our needs.
How would you rate customer service and support?
Neutral
What's my experience with pricing, setup cost, and licensing?
The pricing is quite acceptable and scalable. For our service, it was around 300 to 600 euros per month, which was acceptable for our customers. We could scale up for better performance and scale down when needed.
What other advice do I have?
I highly recommend MongoDB Atlas for both smaller and larger companies.
It is rated an eight out of ten, depending on the use case. As a document-based database, it is one of the better products on the market.
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Other
Scalable and Highly Flexible NoSQL Database
What do you like best about the product?
My favorite feature in this database is the flexibility of its use as well as the possibility to scale it. Because this model stores data in JSON-like formats, it is possible to handle unstructured and semi-structured data without having to prescribe very specific data models. This flexibility is especially good for application where the structure of the data being stored can change as time passes. Being able to scale a database horizontally through the use of sharding is one of MongoDB’s best features since it helps in loading big data and is efficient when dealing with traffic loads. Also, its complex query language; the power offered by the aggregation framework and indexing and data retrieval make the process very efficient and diverse. Moreover, features such as the MongoDB Atlas make cloud deployment and management easy and more enjoyable. MongoDB has gained many supporters of the community and had a great documentation, which makes it fit for today’s developments.
What do you dislike about the product?
Probably one of the biggest negatives of the MongoDB is its steep learning curve for those that have no prior experience with NoSQL systems or have recently migrated from SQL. On the positive side, it is also very flexible, allowing developers to easily create badly thought out schemas, which when the application starts getting a lot of use can slow down the performance immensely. Also, handling shard and replication in self-serving environment quite challenging and may need good understanding of structure. A third anotated limitation is the ACID properties of transactions while MongoDB has added support for these recently and to a far lesser extent than traditional relational database systems, it may not be suitable where high consistency is critical. Finally and as for the offering of MongoDB Atlas, which is a cloud managed service of MongoDB, there is a high likelihood of accumulating high costs particularly for those that are intending to develop jumbo scale applications for their start-ups or small business enterprises.
What problems is the product solving and how is that benefiting you?
It is solving several problems that are inherent in managing huge amount of unstructured or semi structured data. It’s for businesses who are struggling to scale and create flexibility, as well as develop rapidly. Organizations can operate massive datasets and high traffic applications, while performance stays consistent despite the increasing volume of data, thanks to its ability to scale horizontally, or sharding. For businesses with unpredictable or changing data structure, like e-commerce, IoT, social media, this feature is particularly useful.
The biggest benefit for me about MongoDB has to do with being able to modify the data model on the fly without a rigid schema, which helps speed up development due to the iterations necessary. This flexibility allows prototypes of new features to be easier or to simply pivot when business requirements change. Also, MongoDB’s powerful querying and aggregating functionalities make analyzing large dataset very efficient and help in making data driven decision. Also, MongoDB cloud service, MongoDB Atlas, removes my burden of infrastructure management so I can development application and less of database management. From an overall perspective, MongoDB provides for rapid development, scalability, and efficiency that are necessary to compete in the fast changing world.
The biggest benefit for me about MongoDB has to do with being able to modify the data model on the fly without a rigid schema, which helps speed up development due to the iterations necessary. This flexibility allows prototypes of new features to be easier or to simply pivot when business requirements change. Also, MongoDB’s powerful querying and aggregating functionalities make analyzing large dataset very efficient and help in making data driven decision. Also, MongoDB cloud service, MongoDB Atlas, removes my burden of infrastructure management so I can development application and less of database management. From an overall perspective, MongoDB provides for rapid development, scalability, and efficiency that are necessary to compete in the fast changing world.
Audio embedding resources
I’d like to suggest adding more resources on using audio embeddings with MongoDB's vector search. Additional guidance on best practices and examples would greatly benefit those looking to work with audio data in MongoDB.
Powerful and Flexible Database for Gen AI Projects, with Room for Onboarding Improvements
Creating Mentation, an AI-driven wellness assistant, was an enriching experience, and MongoDB supplied the foundation we required for effortlessly handling intricate and diverse data. By managing user interactions and emotional data as well as processing vector embeddings, MongoDB effortlessly fulfilled our requirements. Its adaptability and scalability proved essential, allowing us to broaden our project’s scope without having to repeatedly reconfigure the database.
Although the documentation is comprehensive and addresses various use cases, a concentrated, beginner-friendly crash course would have been immensely helpful—particularly for teams such as ours seeking to utilize AWS and Gen AI. Exploring the fundamentals of MongoDB, such as querying, vector indexing, and aggregation pipelines, prompted us to seek out external tutorials, especially to clarify information regarding vector indexing. At one stage, we came across contradictory data from these sources indicating that solely larger M10 clusters were capable of handling vector indexing, which resulted in additional testing and problem-solving.
Although there were some learning challenges, MongoDB demonstrated to be a robust solution for the requirements of our project. By providing a more efficient onboarding process—centered on key elements and better instructions for utilizing features such as vector indexing—MongoDB would become even more attainable for developers engaged with advanced technology. In general, we had a positive experience with MongoDB, and with some modifications, it could easily become the preferred choice for any developer venturing into Gen AI applications.
Improvement on Documentation
For my hackathon project, I chose MongoDB Atlas from AWS Marketplace. I particularly like the auto-scaling capability.
However, I encountered some challenges with the SDKs at multiple stages of use, so I had to look outside the official documentation for help. For example, while connecting to the cluster.
While the existing documentation is okay, it would be more beneficial if video resources were included (as this helps better than textual documentation). Additionally, integrating real-world examples and case studies into the documentation could greatly enhance its practical value.
The best solution out there
I've used mongodb professionally for 4 years and have found the product meets and exceeds the demands placed on it by the products i create.
showing 1 - 10