MongoDB—both the original product and several other databases compatible with it—have become a popular choice for developers because they offer considerable flexibility in a user-friendly package.

• Flexible schema for agile development: With MongoDB, you can tailor the level of schema structure and validation. You can start with a relaxed structure for rapid prototyping, then introduce strict validation rules and evolve your data model as your application matures. If you want to build quickly without defining the entire data structure upfront, MongoDB’s flexibility can be extremely helpful.
• Horizontal scaling for adaptability: MongoDB is designed for horizontal scaling, allowing data to be distributed across multiple nodes or clusters—a process known as “sharding.” By sharing the load across multiple servers, you can quickly save or recall data and adjust to shrinking or expanding demand.
• High availability: MongoDB offers built-in data replication for high availability and fault tolerance. In case of a hardware failure or during maintenance, multiple copies of data on different database servers allow the database to provide automatic failover.

MongoDB's Query Language (MQL) is designed to be immediately familiar to developers, especially those who know JavaScript. This can make it easier to jump in and write powerful queries to retrieve, update, or delete data.

You have the flexibility to find the exact data you need with different types of queries, including:

• Field query: Find an exact match to a specific value across documents, like all users whose occupation field is exactly “Developer.”
• Range query: Find data that falls within a certain range, such as all users whose age is greater than 35.
• Regular expression (regex) query: Find text that matches a specific pattern. For example, you can find all users whose name starts with the letter “B.”

Overall, MongoDB excels at retrieving information on whole objects. For example, if you want to gather all the information on one user—their name, order history, and preferences—it's likely all in one document. A single query can fetch everything you need quickly.

However, MongoDB is less efficient for queries that require complex JOINs across many different, highly structured collections—which is where a relational database excels.

MongoDB can be the right database choice for a number of applications, particularly those that rely on semi-structured or unstructured data—or applications where the data model is likely to keep evolving. However, it’s worth weighing other database options to ensure you make the right call.

Both MongoDB, Apache Cassandra, and SQL databases offer distinct benefits depending on your specific use case. So let’s unpack some of their differences to help you choose the right option for your application.

SQL databases (like PostgreSQL) and NoSQL document databases (like MongoDB) represent two fundamental approaches to data management.

Unlike MongoDB, SQL databases, also called relational databases, organize data into tables. Each table has a predefined format with rows and columns, so SQL databases are often referred to as “rigid” when compared to the document format of MongoDB.

The appeal of SQL databases like PostgreSQL is the ability to run complex queries with JOINs, combining information from multiple tables with high efficiency. This could include a query like, “Show me customers in Chicago, over the age of 30, who bought a blue shirt last month.”

MongoDB and Apache Cassandra are both powerful NoSQL databases, but they’re built on fundamentally different architectures and are optimized for different use cases.

Apache Cassandra is a wide-column store, meaning it organizes data into rows and columns, but with a flexible schema that allows different rows to have different columns within the same column family.

It's also known for handling massive amounts of data across many servers, providing high availability and high write performance across distributed systems.

If you want to build with MongoDB, you have a few paths to choose from. You can get the database directly from its creators, MongoDB Inc., or use a MongoDB-compatible service from a different provider. Compatibility means it supports the MongoDB API and query language (MQL), allowing you to use the same code, drivers, and tools with minimal changes, while enjoying additional features or performance benefits.

Here are some of the best-known MongoDB-compatible options:

• MongoDB Community and Enterprise editions: These are the original, downloadable versions of MongoDB. You can run them on your own infrastructure, giving you complete control over your deployment.
• MongoDB Atlas: A database-as-a-service (DBaaS) option from the creators of MongoDB. Atlas is a fully managed cloud service that handles all the infrastructure, scaling, backups, and security. It’s offered on several cloud platforms, including Google Cloud.
• Firestore: Another fully managed option for developers who want to focus on building applications without worrying about operations. Firestore is a NoSQL document database that offers a suite of unique features, which we’ll cover in more detail below.
• Amazon DocumentDB: An Amazon Web Services (AWS) managed database service that’s compatible with the MongoDB API. It's designed as a drop-in replacement for applications that are already using MongoDB and want to run on AWS infrastructure.
• Azure Cosmos DB: Microsoft's globally distributed, multi-model database service also provides an API for MongoDB. This allows you to use familiar MongoDB tools and skills to build applications on the Azure cloud platform.

Firestore is a fully managed document database that offers the simplicity of cloud data management with seamless integration to Firebase—a platform designed for creating AI apps and agents.

Firestore is fully compatible with MongoDB drivers, tools, and code, making it easier to start a new project or migrate an existing application while maintaining a familiar query language and functionality.

• Fully managed by Google Cloud: All operational administration, such as scaling, updating, backups, and security, is handled by Google.
• Real-time updates: Keep your users connected with live data. Firestore excels in real-time data synchronization, making it ideal for applications that require live updates, such as chat applications, collaborative tools, or mobile apps with instant data refresh needs.
• Global scalability: With Firestore’s automatic scaling and multi-region data replication, you can handle growth effortlessly and ensure low latency for users worldwide—without the need to configure capacity, sharding, or provision storage.
• Industry-leading availability and performance: Firestore boasts industry-leading high availability of up to 99.999% SLA and single-digit millisecond read latency performance.
• Vector database: You can store vector embeddings and run vector search across your data in Firestore. This enables you to perform the fast similarity searches that are the backbone of modern AI applications and agents.
• Built-in Google Cloud integration: As a part of Google Cloud and Firebase, Firestore offers seamless integrations with Firebase Auth, BigQuery, and many other powerful services.
• Free tier for low usage: Firestore offers a free tier, making it ideal for startups or smaller projects that want to scale as they grow without upfront costs.