MongoDB Atlas (pay-as-you-go)

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.

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.

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

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.

On-premises

Amazon Web Services  (AWS )

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

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.

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.

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.

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.

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.

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.

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.

The price of MongoDB Atlas is reasonable, which is why many organizations, including mine, are opting for it.

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.