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.
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
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
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.
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.
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
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
Secure and Familiar Database
What do you like best about the product?
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.
What do you dislike about the product?
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.
What problems is the product solving and how is that benefiting you?
MongoDB helps us handle flexible and fast-changing data without worrying about strict schemas. It’s great for building apps quickly, especially when data structures vary. We’ve saved time and avoided complex migrations, and with MongoDB Atlas, scaling and managing the database is super simple.
Room for improvement in data handling leads to enhanced cost-effective data management performance
What is our primary use case?
I primarily use Oracle databases, but I work with many other databases such as MongoDB Atlas and several cloud databases. I utilize MongoDB Atlas predominantly for training-level projects in resource grooming and for sub-projects at my office. It is used alongside Oracle and Postgres in these training layers.
What is most valuable?
MongoDB Atlas offers replication, which is cheaper than Oracle RAC, making it appealing to certain industries. It is particularly useful for unstructured and semi-structured data because of its performance in these areas. Sharding and partitioning are supported, though they don't reach the same level as Oracle's capabilities. This cost-effective solution assists organizations in data storage and management.
What needs improvement?
It would be beneficial if MongoDB Atlas could better support OLTP aspects and data frames, as well as enhance its capabilities for data pipelines and visualization dashboards. Furthermore, supporting the medallion architecture could be a valuable addition, and incorporating improved spatial and vector handling for geographical data could make it more competitive. Enhancing vector processing for AI capabilities would also be critical.
What do I think about the stability of the solution?
MongoDB Atlas is effective for unstructured and semi-structured data, but when it comes to OLTP transactions, its performance declines. This is a continuous challenge I face when utilizing MongoDB Atlas.
What do I think about the scalability of the solution?
MongoDB Atlas offers sharding as a scalability feature, although it does not perform as well as Oracle. Partitioning is also available; however, it lacks a multi-tenancy architecture, which affects its scalability in comparison.
How are customer service and support?
Technical support from MongoDB Atlas, which is open source, is satisfactory in most cases. However, when compared to top databases like EDB, Postgres, and Oracle, the features of MongoDB Atlas fall short, resulting in an average rating due to higher-expectation features still lacking in its offerings.
How would you rate customer service and support?
What's my experience with pricing, setup cost, and licensing?
The price of MongoDB Atlas is reasonable, which is why many organizations, including mine, are opting for it.
What other advice do I have?
The overall rating for MongoDB Atlas is around 5.5. To improve, MongoDB should enhance support for demanding graph databases, vector databases, and spatial handling. Additionally, improvements in AI capabilities, particularly vector processing, are imperative. These developments could provide MongoDB Atlas with a competitive edge.
Amazing DB
I recently had the opportunity to work with MongoDB Atlas on AWS, and I must say, the experience has been nothing short of impressive. Bringing together the power of MongoDB's flexible, scalable NoSQL database with the robust infrastructure and services of AWS creates a seamless, high-performance environment for managing data-intensive applications.
Performance optimization is another key advantage. With features like auto-scaling, performance monitoring, and workload isolation, MongoDB Atlas on AWS eliminates much of the operational overhead, allowing developers to focus on building applications rather than managing infrastructure. Additionally, the automated backups and failover mechanisms provide peace of mind, ensuring that critical data is always protected.
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