MongoDB vs Amazon DynamoDB

Feature comparison

MongoDB delivers a fully-featured document database with secondary indexes, the aggregation framework for complex transformations, change streams for event-driven applications, multi-document ACID transactions across replica sets and sharded clusters, and Atlas Search for native full-text and vector search. The Atlas managed service runs on AWS, Azure, and Google Cloud with auto-sharding, cross-region replication, and online schema migrations. MongoDB's query API supports ad-hoc queries, projections, joins via $lookup, and rich filtering — capabilities that DynamoDB does not match natively.

Amazon DynamoDB takes a different architectural approach. It is a fully managed key-value and document store designed for predictable single-digit-millisecond latency at any throughput. Tables are partitioned by partition key with optional sort keys, and queries must use these keys or global secondary indexes. The simplicity of the model is the source of its scalability: DynamoDB delivers consistent performance from kilobytes to petabytes without the partitioning, sharding, or capacity management overhead of self-managed document databases.

For query patterns, MongoDB suits applications with diverse, evolving, or unpredictable query needs. DynamoDB requires query patterns to be known in advance and modelled into the partition key design — a discipline known as single-table design that AWS Solutions Architects routinely teach. Adding new query patterns later often requires global secondary indexes or table redesign.

For transactional capabilities, both support ACID transactions: MongoDB across replica sets and sharded clusters, DynamoDB across up to 100 items within a region. DynamoDB Streams paired with Lambda provides a mature event-driven trigger model; MongoDB change streams provide similar capability.

For vector search, MongoDB Atlas Vector Search is widely used as a retrieval-augmented generation backend. DynamoDB does not include native vector search; AWS workloads typically pair DynamoDB with OpenSearch or Amazon Aurora PostgreSQL for embedding storage. For mobile and edge synchronisation, neither product leads — Couchbase and AWS AppSync GraphQL serve those scenarios.

Pricing comparison

MongoDB Atlas prices by cluster tier and region. Production tiers start at approximately $60 per month on M10 instances, scaling to $5,000–$30,000 monthly for larger sharded clusters before backup, Atlas Search, Vector Search, and data transfer charges. Amazon DynamoDB prices in two modes: on-demand at approximately $0.25 per million write request units and $0.05 per million read request units, plus storage at $0.25 per GB per month; or provisioned at approximately $0.65 per WCU per month and $0.13 per RCU per month. DynamoDB Streams, Global Tables, and Backup add separate charges.

Five-year cost of ownership comparisons are workload-shaped. For a 50,000 write-per-second steady-state workload with 1TB working set: MongoDB Atlas $1.5M–4M depending on instance class and region, DynamoDB on-demand $3M–8M, DynamoDB provisioned with auto-scaling $1.5M–4M. The primary buying-side caveat for DynamoDB is on-demand cost surprises at scale — workloads moving from prototyping to production should switch to provisioned with auto-scaling. MongoDB Atlas's primary caveat is data egress on multi-cloud architectures and the per-query cost of Atlas Search and Vector Search. Pricing as of May 2026.

When to choose MongoDB

Choose MongoDB when applications need flexible query patterns including secondary indexes, ad-hoc queries, and complex aggregations, when multi-cloud deployment across AWS, Azure, and Google Cloud is a requirement, when the team values rich query capabilities over operational simplicity, when vector search for retrieval-augmented generation is part of the architecture, when the schema is expected to evolve frequently, or when the existing data stack already includes MongoDB Atlas, Realm, or App Services. MongoDB is also the default for new SaaS workloads needing portability between clouds.

When to choose Amazon DynamoDB

Choose Amazon DynamoDB for AWS-native applications where predictable latency at any scale matters, when query patterns are known in advance and can be modelled into partition key design, when serverless operational characteristics align with the team and platform strategy, when applications integrate tightly with Lambda, AppSync, EventBridge, or Glue, when global tables and multi-region active-active replication are needed without operational overhead, or for workloads with extreme throughput requirements where MongoDB sharding management would be a material operational burden.

Alternatives to both