
Overview
MongoDB Atlas for Government has achieved FedRAMP Moderate Authorization and is a separate environment of MongoDB Atlas, dedicated to meeting the demanding security and privacy needs of the US Government.
It is a fully managed MongoDB service engineered and run by the same team that builds the database and MongoDB Atlas. It incorporates operational best practices we've learned from optimizing thousands of deployments across startups and the Fortune 100. Build on MongoDB Atlas for Government with confidence, knowing that you no longer need to worry about database management, setup, and configuration, software patching, monitoring, backups, or operating a reliable, distributed database cluster.
MongoDB Atlas for Government is available in AWS GovCloud and AWS US FedRAMP Moderate regions.MongoDB Atlas for Government for AWS Marketplace includes: 24,000 MongoDB Atlas for Government Credits valid for 12 months for USD 24,000. Credits are consumed based on the chosen cluster configuration, backup settings, and network transfer costs
MongoDB Atlas for Government Pro Support with 2 hr response time SLA. The MongoDB Atlas for Government Pro plan provides access to proactive, consultative support. The same team that builds the database helps you throughout your entire application lifecycle. You can ask MongoDB experts unlimited questions, 24 x 365, globally.
Highlights
- Highly available: Clusters are geo-distributed fault-tolerant and self-healing. Deploy across multiple regions (e.g.: spanning GovCloud east and west regions) for even better guarantees and local reads.
- Protect your data: Strong security defaults with authentication, network isolation, encryption, and role-based access controls keep your data protected.
- Build semantic search and AI-powered applications: Integrate the operational database and vector search in a single, unified, and fully managed developer data platform with a MongoDB native interface that leverages large language models (LLMs) through popular frameworks
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Customer reviews
Open-source tool improves network monitoring and reporting efficiency
What is our primary use case?
MongoDB does well in being able to access our network devices and keep logs and reporting—that's about it.
I would recommend MongoDB as part of a template if anyone is considering free and open-source templating services such as LibreNMS , but as a standalone, I couldn't advise.
What is most valuable?
MongoDB has definitely helped us improve our network monitoring and reporting dashboard, so I would say it has impacted our operations positively overall.
What needs improvement?
I'm not sure about the documentation or the knowledge bases available for MongoDB because I don't interact with it at that level, but I would say it's minimal and could be improved.
I am not experienced with MongoDB enough to know any pain points or areas they could improve.
Nothing else comes to mind at this time that could be improved.
For how long have I used the solution?
We deployed MongoDB about five years ago and it has been in operation since then.
What was my experience with deployment of the solution?
I was not a part of the initial setup or deployment of MongoDB.
One person was involved with the setup team, and it took just a few days to deploy it.
What do I think about the scalability of the solution?
Overall, on a scale of one to ten, I would rate MongoDB an eight; it's mostly because we're still running a monolithic environment on old hardware, so there are some limitations with read-write access.
Which solution did I use previously and why did I switch?
At this time, I'm only looking into Cisco or Linux or other solutions out of curiosity about possibly switching to it, but currently all that we use are LibreNMS and Splynx.
How was the initial setup?
From what I know, I would say the initial setup of MongoDB was pretty straightforward.
On LibreNMS, they have a template for setting up the environment that includes all the services, so MongoDB is just part of that template, meaning they weren't really too hands-on with setting up MongoDB itself.
What about the implementation team?
One person was involved with the setup team, and their job title was Network Operations Engineer.
Which other solutions did I evaluate?
I'm familiar with open-source databases such as MongoDB, and I don't think it's Grafana , but it's similar to Grafana , though I'm trying to think of what it's called.
I'm not entirely sure about the main differences between MongoDB and other open-source databases that I've used.
We haven't really delved too much into looking at comparisons for databases.
What other advice do I have?
MongoDB is not currently supporting our AI-driven projects nor do we use it along with AI at all.
I don't know how MongoDB's document-oriented model has benefited our management processes; that's beyond my expertise.
I don't have experience with QRadar or Auvik or similar products.
I'm familiar with some Linux tools, just things such as smokeping, which we use implemented in our LibreNMS environment.
I'm only an operator, so I don't actually spend a lot of time developing MongoDB, thus I'm not sure what the best features are.
I would rate MongoDB an eight out of ten.
Which deployment model are you using for this solution?
Offers reliable engine for legacy needs but requires enhanced cost management and AI features
What is our primary use case?
I am not a partner of MongoDB; I am just a customer.
I do not use MongoDB in AI projects; only CosmoDB is used for AI projects, as MongoDB is an old pattern for us, and the new workload in AI is for a new pattern, which is CosmoDB for AI apps.
I would recommend MongoDB because it is a good pattern and a good product for legacy; for us, MongoDB is for legacy databases and legacy apps, and in this scope, it is a good pattern and a stable database engine; however, for new deployments and new applications, CosmoDB is a better engine.
What is most valuable?
My experience as a partner with Microsoft is very good because we have been a partner for three or four years, and it has been a very good experience.
MongoDB may have advantages over Cosmos DB perhaps in metrics because you can make some dashboards with database metrics, and there are many tools in MongoDB for dashboarding that are perhaps better than CosmoDB.
The dashboards in MongoDB have more functionalities; for example, you can create a dashboard with MongoDB database data, and it is simple to create, such as some sales dashboards, while I do not see this functionality to rapidly create such dashboards in CosmoDB.
What needs improvement?
While MongoDB is a good product, it is also an expensive product for support, and its scalability is acceptable, but the big problem with MongoDB is the cost.
For security in MongoDB, we work with encrypted databases by default, but we have not contracted the security options in our contract because it is too expensive, so we only implement encrypted databases without the security pack, which is very expensive for us; in security, we are at the first steps, just using encrypted databases.
I think additional features needed in MongoDB include perhaps vector databases, as I think they are not supported right now.
For how long have I used the solution?
I have been working with MongoDB for five years.
What do I think about the scalability of the solution?
The scalability in MongoDB is limited because we only work with ReplicaSet with two servers, and in comparison, the scalability in CosmoDB is much better than the MongoDB ReplicaSet models; although you can set the auto-provisioning of a node in ReplicaSet, it is very expensive, and we have to work with manual scalability in MongoDB.
The performance of MongoDB is good, especially in a ReplicaSet model, but if you want to pass on to another model, for example, Sharding models, it is very complicated; in ReplicaSet, it is acceptable, but if your workload needs more performance, and you must pass to a Sharding model, it is complicated in MongoDB, whereas in CosmoDB, it is simple.
What was our ROI?
We have seen a little ROI, and we want to target CosmoDB for this return on investment because it is the better model for this feature; however, with MongoDB, it is difficult to calculate the return on investment, as it is too expensive for our use.
What's my experience with pricing, setup cost, and licensing?
We pay approximately 2,000 euros per month for MongoDB.
What other advice do I have?
This solution receives a rating of 7 out of 10.
Friendly to use of collections
Transforms data flow with adaptable schema and smooth public cloud deployment
What is our primary use case?
What is most valuable?
What needs improvement?
For how long have I used the solution?
What was my experience with deployment of the solution?
What do I think about the stability of the solution?
What do I think about the scalability of the solution?
How are customer service and support?
How would you rate customer service and support?
Neutral
Which solution did I use previously and why did I switch?
How was the initial setup?
What's my experience with pricing, setup cost, and licensing?
What other advice do I have?
Which deployment model are you using for this solution?
If public cloud, private cloud, or hybrid cloud, which cloud provider do you use?
Overpriced, Poor performance and some of the worse support I have ever had to deal with
Compared to our other infrastructure bills, Mongo was significantly higher for the amount of compute and storage we used ($3K per month). This is a managed service, so you would expect to pay a premium. Ok, sure, but then I expect great functionality, performance, and support.
The main problem began with Mongo when we needed to delete some data because they tie the CPU and memory tiers to storage size, so we were overpaying. Our application would run fine off an M10 dedicated cluster (the smallest tier), but it had automatically scaled to an M50 because of storage. This is already a bit disappointing because they are forcing customers to pay for more compute and memory than they need.
So we started deleting some data, but then we ran into problems. The data deletion process was really slow and also slowed our entire cluster down, causing lag and performance issues for our end users. But hang on, this makes no sense because we are paying for more CPU and RAM than we need, so why would we have this issue?
It took us three months to delete 500GB of data. In the meantime, our bill remained the same because you can't claim the space back without compacting the database. Ok, fine. So we ran compact(), but we only freed ~100GB on the secondary clusters.
Support gave us a script to run that can see how much storage can be freed.
In the end, we had to activate an expensive additional support plan costing us $500 USD per month to get support to run a re-sync command. This should have taken their support people 10 minutes, but instead, they mucked us around going back and forth on the ticket, taking three weeks to resolve.
A year later, we needed to delete some more data. We spent another five months deleting 800GB of data. Then we ran compact() and freed 300GB. Where is our other 500GB? We contacted some humans at Mongo, who really couldn't do much other than suggest we get funding to cover the $500 support for one month. Yes, we got the $500 credit, but when I went to reactivate support, it was going to charge us for three months for one month because Mongo retroactively bills you for three months when you reactivate. Wow, we started in a bad place, now I'm beyond frustrated; this is daylight robbery.
To this day, I am still fighting to reclaim some storage, but at this point, I'm going to recommend to our CEO that our dev team put some effort into moving away completely from Mongo.
I also need to mention that Mongo recommended we use their online archive features, but when we crunched the numbers, it was still quite expensive, and we would have to do significant work to make our application work between the regular clusters and online archive. So it was significantly more logical to just put the data in AWS S3, then delete it in Mongo.
If I can summarize my experience with Mongo, and I acknowledge mine is probably quite different to most, here it is:
Overpriced for the performance you get
Sneaky billing model where they tie CPU and memory to storage
Terrible and expensive support
Sneaky extra charges on reactivating support
Bad support escalation solutions - they couldn't just turn on free 'support'
Poor database performance
Slow delete operations
Ecosystem lock-in
Forced upgrades - no LTS releases
Let me sum it up this way: if your compact() command does not free up the space that is available on your cluster, then provide the customer with free support to do so.
I hate dealing with Mongo. Nothing is simple, everything is expensive, and the performance sucks.
If you are considering using Mongo, find something else. Even if you have to take a bit more time to learn AWS Dynamo, S3, or Aurora, you should do it; you will save time and money in the long run.
Mongo, you deserve this negative review. I have given you plenty of opportunities to resolve things and have escalated issues, but you just don't care.
We wanted to move away from Mongo before; now I can't get rid of it fast enough.