AWS DocumentDB vs. MongoDB

Why API Compatibility Is Not a Database Strategy

AWS DocumentDB and MongoDB are regularly equated. The reason is quickly stated: Both are supposed to speak the same API. For many decision-making processes, this statement is enough to check a box. “Compatible” sounds like interchangeable, low risk, flexible. This assumption is exactly the core of the problem.

A database is not a protocol and not an interface. It is complex system behavior. Those who focus solely on an API ignore architecture, performance characteristics, feature development, and long-term dependencies. In the case of AWS DocumentDB and MongoDB, this regularly leads to poor decisions that only become visible in production.

This article classifies where both systems actually differ – technically, strategically, and economically – and why these differences are not academic but operationally relevant.

What AWS DocumentDB Technically Is – and What It Is Not

AWS DocumentDB is not a MongoDB instance. It is a fully AWS-developed managed service that emulates the MongoDB API. Under the hood, no MongoDB engine runs, but a proprietary implementation whose goal is to replicate part of MongoDB’s behavior.

AWS handles operations, scaling, backups, replication, and high availability. For many teams, this is attractive, especially when the entire infrastructure is already operated in AWS and operational simplicity is weighted higher than technical control.

The problem begins where “API-compatible” implicitly becomes “functionally equivalent.” This equation is technically untenable.

API Compatibility: A Limited Promise

The MongoDB API defines how queries are formulated – not how they are executed internally. This is exactly where the dividing line between both systems lies.

DocumentDB supports a defined portion of the MongoDB API, however:

• not every MongoDB version
• not every aggregation stage
• not every index or query behavior
• not every performance optimization

Feature parity is selective and delayed. New MongoDB features appear in DocumentDB only when AWS implements them – and only to the extent that AWS considers appropriate. For development teams, this means: Documentation alone is not enough. Every assumption about behavior must be validated.

This becomes particularly problematic with more complex aggregation pipelines, specific index strategies, or edge cases in the query planner. Debugging is harder because familiar MongoDB tools and explanation models don’t fully apply. After all, no MongoDB runs internally.

MongoDB as the Reference System

MongoDB itself is the reference system for its own ecosystem. The engine is openly documented, the behavior consistent across operating models. Whether self-operated, in Kubernetes, or as a managed service like MongoDB Atlas – it is technically the same database.

This is not a philosophical difference but an operational advantage:

• reproducible behavior across environments
• consistent performance characteristics
• predictable feature development along a clear roadmap
• familiar debugging and monitoring tools

For teams running production-like tests, load simulations, or complex data models, this consistency is crucial. Deviations between development, test, and production environments are one of the most common causes of operational problems – MongoDB structurally reduces this risk.

Strategic Implications: Vendor Lock-in Through Behavior

The actual difference between DocumentDB and MongoDB lies not in the functional scope of individual features, but in strategic lock-in.

DocumentDB binds applications not only infrastructurally to AWS, but functionally. Query behavior, performance characteristics, and scaling mechanisms follow AWS-specific logic. Applications are implicitly optimized for these peculiarities – often unconsciously.

A later switch to “real” MongoDB is therefore not a drop-in replacement. It is a migration project that regularly includes the following points:

• Review and adaptation of aggregation pipelines
• Revalidation of index strategies
• Adjustment of performance assumptions
• Tests for deviating query behavior

What is considered compatible today can become a structural risk with growing complexity.

Costs and Scaling: Two Different Mindsets

Both approaches also differ fundamentally economically.

DocumentDB primarily scales instance-driven. Performance problems are often solved by larger or additional instances. Architecture optimization is only possible to a limited extent, as central levers are not exposed.

MongoDB follows an architecture-driven approach. Scaling happens purposefully through sharding, adapted topologies, and precise index strategies. Performance optimization does not necessarily mean more resources, but better utilization of existing ones.

This difference affects the Total Cost of Ownership in the long term. Systems that can be scaled through architecture remain more controllable – technically and financially.

Condensed Comparison

When Each System Makes Sense

AWS DocumentDB can make sense for:

• simple, stable MongoDB-like use cases
• applications without complex aggregations
• systems that deliberately remain in AWS permanently
• low requirements for feature currency

MongoDB makes sense for:

• growing or changing data models
• platforms with high requirements for predictability
• complex query and aggregation logic
• scenarios where portability matters

Conclusion

A database is not an API promise. It is system behavior – over years.

Those who weight short-term convenience higher than long-term control can work with DocumentDB. However, those who understand data models as a strategic asset should not rely on emulated compatibility.

Control over data means control over behavior. And this control cannot be abstracted through an API.