DynamoDB vs MongoDB: Queries, Scaling, and Cost

Choose DynamoDB when your application has well-defined key-based access patterns and you want AWS to manage database capacity and infrastructure. Choose MongoDB when document queries, aggregation, geospatial operations, or deployment flexibility are central to the application. MongoDB Atlas is a managed hosting platform for MongoDB, but its tiers and operating model are different from DynamoDB on-demand.

DynamoDB and MongoDB both store flexible documents, support indexes and transactions, and can power production applications. The decision is less about which is “more NoSQL” and more about the queries, transaction boundaries, and operational work you need.

Current MongoDB and Atlas options

The MongoDB release index lists the 9.0 series as the current stable release at this article's October 2026 update. Existing deployments can use older supported versions; check the selected cluster's version and driver compatibility before relying on a feature.

Atlas no longer offers the old M2, M5, or Serverless instance types. Its migration documentation records their retirement and migration to Free, Flex, or Dedicated clusters. An old tutorial describing Atlas Serverless as a preview is no longer a useful description of the product.

The current pricing page lists Atlas Core Free, Flex, and Dedicated options, plus Atlas Infinite in public preview. Treat the preview as a separate deployment choice with documented restrictions, not a renamed version of the retired Serverless offering.

Comparison at a glance

Similarities

Backups

DynamoDB provides on-demand backups and point-in-time recovery. The recovery window is configurable from 1 to 35 days, and restoration creates a new table. Rehearse restoring permissions and integrations as well as data.

Atlas backup capabilities depend on tier and deployment. Flex uses daily snapshots; dedicated deployments support broader cloud-backup features, including continuous backup where configured. Do not assume a Free or Flex cluster provides the same recovery options as a dedicated production deployment.

For either product, replication is not backup. It improves availability but also propagates erroneous application writes. A recovery test should demonstrate that the application can use the restored dataset within its downtime budget.

Security

DynamoDB encrypts data at rest, uses IAM and resource-based policies for access control, and supports VPC endpoints. The default AWS-owned encryption key differs from AWS-managed and customer-managed KMS keys in control and cost.

Atlas requires TLS and provides encryption at rest. You configure database users and allowed network access. Private endpoints and other controls depend on the selected tier. Self-hosted MongoDB requires you to configure and operate the corresponding protections yourself; Atlas defaults are not automatically self-hosted defaults.

In both systems, database access and administrative access need separate attention. Use application-specific identities, avoid broad credentials, and account for access to backups and exported data.

Provisioning and scaling

DynamoDB on-demand charges for requests and manages capacity, subject to key distribution, warm throughput, quotas, and optional throughput caps. Provisioned mode lets you set read/write capacity and use auto scaling. Auto scaling is not a promise that any instantaneous traffic jump will succeed without throttling.

MongoDB scales through replica-set capacity and sharding; Atlas offers automation for supported cluster configurations. An appropriate shard key matters just as an appropriate DynamoDB partition key matters. Adding capacity cannot fix every inefficient query or concentrated write pattern.

Differences

Data access patterns

DynamoDB starts with the questions your application must answer: get an order by ID, list a customer's orders by date, or find jobs in a status bucket. Keys and secondary indexes should make those operations explicit. Single-table design can group related entities, but it is a design option, not a requirement for every DynamoDB application.

MongoDB offers more flexible document filtering and aggregation pipelines. Pipelines can filter, group, transform, and combine data, including lookup operations. This can reduce application-side work when query needs evolve, but query plans, indexes, and dataset size still determine performance.

For example, “orders for customer 42 during October” is a natural key-range query in either system. “Group all orders by changing combinations of nested product attributes” needs a different plan: a MongoDB aggregation, a precomputed DynamoDB view, or a separate analytics system. Do not turn that requirement into a repeated DynamoDB Scan without estimating its cost.

Data model and document size

DynamoDB has no dedicated date or timestamp data type. Store timestamps as numbers or consistently formatted strings, and choose units explicitly. MongoDB BSON has a native date type as well as types such as ObjectId and Decimal128, which require deliberate conversion during migration.

DynamoDB items have a 400 KB limit; MongoDB's BSON document limit is 16 MiB. Larger MongoDB documents cannot be copied into one DynamoDB item. Common alternatives are splitting a document into related items or storing the large payload in object storage with metadata in DynamoDB.

MongoDB offers GridFS for files exceeding its document limit. That does not mean every binary asset belongs in MongoDB: choose storage based on how the application retrieves, updates, and serves the data.

Transactions and consistency

MongoDB supports multi-document transactions on replica sets and sharded clusters. Their cost and behavior depend on deployment and read/write concerns. Single-document atomic updates remain useful even when transactions are available.

MongoDB consistency controls include read concern, write concern, read preference, and causally consistent sessions. Reading from a secondary and reading from the primary with stronger guarantees are not equivalent workloads.

DynamoDB supports conditional operations and regional transactions. Its global tables use either MREC or MRSC; MRSC does not support transactions. Specify the business guarantee first, such as preventing two successful reservations of the last unit, then choose an operation and retry strategy that maintains it.

Search, vectors, and geospatial data

MongoDB has dedicated geospatial indexes and operators for location-based queries. DynamoDB can store coordinates and support application-designed spatial access patterns, but it does not provide MongoDB's geospatial query interface.

MongoDB Vector Search supports approximate and exact nearest-neighbor search, with deployment and index requirements. MongoDB Search also supports full-text search use cases. DynamoDB now offers native vector search on supported on-demand tables, so a comparison that says it always needs a separate vector database is outdated.

Evaluate filtering, embedding dimensions, index freshness, and operating cost against representative queries. A demonstration using a handful of vectors will not establish production recall or tail latency.

Cloud platforms and local development

The production DynamoDB service runs on AWS. DynamoDB Local supports local development and testing, with differences from the managed service.

Atlas supports AWS, Azure, and Google Cloud, subject to feature and region availability. MongoDB can also be self-hosted. That gives you more deployment choices, although moving a deployment still requires planning for networking, data transfer, operational differences, and any Atlas-specific features the application uses.

Connections from application code

DynamoDB clients use an HTTPS service API. MongoDB drivers manage database connections and connection pools. Both benefit from client reuse; creating a new client for every request increases overhead.

Atlas connection limits depend on the tier. In Lambda or another environment that rapidly creates workers, budget total connections across all workers and configure pool sizes accordingly. The retired M2/M5 tiers are not a valid basis for new capacity planning.

AWS native integrations

DynamoDB integrates with Lambda, Step Functions, IAM, S3 exports, and analytics services. DynamoDB Streams provides item changes for event processing. These integrations can remove application code and operational components.

MongoDB can also be accessed from Lambda and other AWS workloads through its drivers. Hosting Atlas on AWS can keep the database close to the application. The tradeoff is the specific connection, identity, and integration work your design needs, not whether MongoDB can run with serverless application code.

Pricing

Compare DynamoDB pricing with current Atlas pricing using equivalent workload and recovery requirements. DynamoDB costs depend on item sizes, reads, writes, indexes, consistency, storage, replication, and backups. Atlas costs depend on the deployment, resources, provider, region, traffic, and enabled services.

Flex is a shared-resource option intended for development and testing; Dedicated provides a different resource and feature profile. Neither should be priced as the retired per-operation Serverless tier. Preview offerings need their own evaluation.

There is no reliable rule that MongoDB always becomes cheaper at scale or DynamoDB is always cheaper for small applications. A well-utilized cluster and a sporadically accessed table have different economics. The DynamoDB pricing calculator helps quantify one side; measure MongoDB resource requirements and add its backup and transfer charges for the other.

When to use which

Choose DynamoDB when predictable access paths, AWS integrations, and managed capacity fit the application. Choose MongoDB when its query language, aggregation, geospatial features, or deployment choices save meaningful application complexity.

Using both can work when services have separate data ownership, or one system holds an asynchronously updated read model. Define the source of truth, deletion handling, lag, and reconciliation. If you are replacing MongoDB, use the migration guide to redesign access patterns before moving records.