What I like most about MongoDB in general is the flexibility of its data model and how easy it is to use for developers and data engineers. MongoDB Atlas brings these strengths to the infrastructure layer as well, which I really appreciate as a developer.

With the option to store rich documents, along with built-in full-text and vector search (even with automatic embedding), it feels like a natural fit for AI applications.

Setting up a cluster on MongoDB Atlas is straightforward and largely hassle-free. It also integrates with all major hyperscalers, so you can keep your data close to where your applications run. In the paid tiers, there are different cluster sizes you can fine-tune to match your memory and compute needs. This way, you pay only for the performance you actually require, which is an important aspect for cloud databases.

As a developer, you can start with a free cluster that’s ready within minutes, or use a Flex cluster that adapts to the needs of your MVP.

One feature I don’t want to overlook is the Performance Advisor, which monitors your database performance and provides suggestions on how to improve it. Review collected by and hosted on G2.com.

Cloud databases in general are an expensive part of infrastructure. This also holds true for MongoDB Atlas, so you need to carefully weigh whether to host the MongoDB on your hardware and assign resources to manage it or go the cloud route. Review collected by and hosted on G2.com.