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.
External reviews are not included in the AWS star rating for the product.
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.
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.
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.
I have been using this product for the past 5 years.
I find the installation process easy to deploy as it wasn't difficult to implement.
The stability of the product is very high, and I would rate it a nine out of ten for stability.
It's very much scalable, and I would rate scalability a nine.
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.
Positive
I personally took part in the installation process.
I can deploy MongoDB Atlas in 2-3 hours.
When we make changes, responsibilities are always distributed. It will be a team whenever a production deployment comes.
My company has seen financial benefits from using MongoDB Atlas through savings because we are using open source.
Postgres is another option that is available for us. I have considered alternatives for MongoDB Atlas.
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.
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.
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.
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.
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.
I have been using MongoDB Atlas since 2017 and Google Cloud Platform since 2018.
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.
MongoDB Atlas scales well and supports data growth effectively.
The technical support is very good. I have used them sometimes, even recently, and found the feedback to be spot on our needs.
Neutral
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.
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.
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.
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.
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.
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.