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.
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.
A very good and friendly experience i had with the MongoDB till now.
What do you like best about the product?
Fast data fetching and easy to use and implementaion.
What do you dislike about the product?
Some time we face connective issue with the database. Without any appropriate reason
What problems is the product solving and how is that benefiting you?
Fast data serfing over the internet and easy to manage it even the GUI is really usefull.
MongoDB review
What do you like best about the product?
MongoDB's flexibility and scalability are standout features. Its document-oriented model (using BSON) allows for dynamic, schema-less data structures, making it super versatile for evolving applications. This is especially helpful when dealing with unstructured or semi-structured data.
I also appreciate its horizontal scaling capabilities through sharding, which makes it suitable for handling large datasets and high-throughput applications. Features like indexing, aggregation pipelines, and replica sets for high availability are excellent for both performance and reliability.
For developers, tools like MongoDB Atlas (its cloud platform) simplify deployment and monitoring, saving tons of time.
I also appreciate its horizontal scaling capabilities through sharding, which makes it suitable for handling large datasets and high-throughput applications. Features like indexing, aggregation pipelines, and replica sets for high availability are excellent for both performance and reliability.
For developers, tools like MongoDB Atlas (its cloud platform) simplify deployment and monitoring, saving tons of time.
What do you dislike about the product?
write amplification and potential performance issues under high write loads if not tuned properly. Its flexibility can also be a double-edged sword; having no enforced schema can lead to messy data structures if developers aren't disciplined.
What problems is the product solving and how is that benefiting you?
storing and querying time series data
Supportive features enable effective data management and growth
What is our primary use case?
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
Amazing product!
I recently got a chance to to work with MongoDB Atlas on AWS.
It's a great option to bring these two power houses together and leverage the best of both of them.
I cannot recommend this product more!
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.
Powerful and Scalable Database Solution with MongoDB Atlas
As a developer, I’ve had the opportunity to work with various database solutions, and MongoDB Atlas stands out as one of the best managed database services available today. Here are my thoughts on why I highly recommend MongoDB Atlas, especially for users in the AWS ecosystem:
- Ease of Use and Quick Setup: Setting up MongoDB Atlas was a breeze. The integration with AWS was seamless, allowing me to deploy clusters in just a few clicks. The user-friendly web interface is intuitive, making it easy to manage databases without a steep learning curve.
- Scalability and Performance: One of the most impressive features of MongoDB Atlas is its ability to scale effortlessly. Whether you’re dealing with moderate traffic or a sudden spike in user requests, Atlas can automatically adjust resources to ensure optimal performance. The built-in auto-scaling feature is a game-changer for applications that experience fluctuating workloads.
- Global Distribution and High Availability: With MongoDB Atlas, I can deploy clusters across multiple regions, ensuring low-latency access for users around the globe. The built-in replication and failover mechanisms provide high availability, which is critical for mission-critical applications.
- Cost-Effective: For a managed service, MongoDB Atlas offers competitive pricing. The pay-as-you-go model allows us to only pay for what we use, making it suitable for startups and large enterprises alike.
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.
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