DynamoDB vs MongoDB

Should you use DynamoDB or MongoDB?

Choose DynamoDB for a fully-managed, serverless key-value and document store on AWS with predictable, key-based access patterns and hands-off scaling. Choose MongoDB for a document database with flexible ad-hoc queries, a rich aggregation pipeline, and the option to self-host or run managed on any major cloud. Your access patterns decide it.

DynamoDB vs MongoDB at a glance

Characteristic	DynamoDB	MongoDB
Data model	NoSQL key-value and document; items grouped in tables, each item up to 400 KB	Document database; BSON (JSON-like) documents up to 16 MB, grouped in collections
Query language / API	Native API (GetItem, Query, Scan, PutItem, …) plus PartiQL, a SQL-compatible query language	MongoDB Query API (MQL) plus the aggregation pipeline for multi-stage transforms, joins ($lookup) and grouping
Primary access pattern	Key-based lookups on the partition key (and optional sort key); schema is designed around known access patterns up front	Flexible queries on any field; ad-hoc filters and aggregations, backed by indexes
Secondary indexes	Global secondary indexes (own throughput, any key) and local secondary indexes (same partition key, 10 GB per-partition cap)	Secondary indexes on any field, including compound, multikey, text, geospatial and wildcard indexes
Consistency model	Eventually consistent reads by default; strongly consistent reads available per-request (within a single AWS Region)	Tunable read/write concerns; reads from the primary are strongly consistent by default
Scaling model	Automatic partitioning managed by AWS; scales throughput and storage without provisioning servers	Horizontal scaling via sharding across a cluster; replica sets provide high availability and failover
Transactions	ACID transactions across multiple items, scoped to a single AWS Region	Multi-document ACID transactions (since 4.0), including across shards (since 4.2)
Pricing model	Pay-per-request (on-demand) or provisioned capacity, plus storage; no servers to run	Atlas is cluster-based (instance size/hours) with a low-cost Flex tier; self-hosted Community edition is infrastructure cost only
Managed / serverless options	Fully serverless and fully managed; available only on AWS	Atlas managed service on AWS, Google Cloud or Azure; also self-hostable. Atlas Serverless instances were retired and replaced by the Flex tier
Best-fit workloads	High-scale apps with predictable key access needing consistent low latency and minimal operations	Apps with evolving schemas, ad-hoc queries and rich aggregations, or that need multi-cloud / on-prem portability

When DynamoDB is the better choice

• You are on AWS and want zero database operations. DynamoDB is serverless — there are no instances, clusters, or capacity to patch. On-demand mode scales to traffic automatically (capacity modes).
• Your access patterns are known and key-based. When you can design your keys around how you read data, DynamoDB delivers consistent low-latency lookups at effectively any scale. This is where single-table design shines.
• You want pay-per-request pricing. With on-demand capacity you pay only for the reads and writes you make, with no idle cluster cost.
• You need tight AWS integration. Native ties to IAM, Lambda, streams and the wider AWS ecosystem reduce glue code.

When MongoDB is the better choice

• Your queries are ad-hoc or exploratory. MongoDB lets you filter and index on any field and run rich server-side aggregations — including joins across collections — without pre-planning every access pattern.
• You need portability across clouds or on-prem. MongoDB runs self-hosted or on Atlas across AWS, Google Cloud and Azure. DynamoDB is AWS-only.
• Your documents are large or deeply nested. MongoDB's 16 MB document ceiling is far larger than DynamoDB's 400 KB item limit (DynamoDB constraints).
• Your schema evolves quickly. Flexible documents plus first-class secondary indexing suit fast-changing models without a rigid up-front key design.

What a MongoDB aggregation becomes in DynamoDB

The comparison tables above tell you the models differ. This is what that actually
costs you on a real query. Take a revenue-by-country rollup, which in MongoDB is one
pipeline:

db.orders.aggregate([
  {$match: {status: 'shipped'}},
  {
    $lookup: {
      from: 'customers',
      localField: 'customerId',
      foreignField: '_id',
      as: 'customer'
    }
  },
  {$unwind: '$customer'},
  {$group: {_id: '$customer.country', revenue: {$sum: '$amount'}}}
]);

DynamoDB has no server-side equivalent of any of the three stages that matter.
$lookup has no counterpart — there are no joins. $group has none either, and
PartiQL does not add one: its SELECT supports no JOIN, no GROUP BY and no
aggregate functions, so rewriting the pipeline in PartiQL does not get you there.

What DynamoDB gives you server-side is the filter, and only if you designed a key for
it. With status on a GSI, the $match stage becomes:

{
  "TableName": "orders",
  "IndexName": "status-index",
  "KeyConditionExpression": "#s = :status",
  "ExpressionAttributeNames": {"#s": "status"},
  "ExpressionAttributeValues": {":status": {"S": "shipped"}}
}

Everything after that — fetching each order's customer, and summing by country — is
work your application has to do. That is the real migration cost, and it is the part
the feature tables omit: not "DynamoDB lacks joins" as a bullet, but that one pipeline
becomes a query plus a fan-out plus a client-side rollup you now own and maintain.

DynoTable's SQL Workbench does that plan for you rather than making you write it. The
same rollup is one statement:

SELECT c.country, SUM(o.amount) AS revenue
FROM orders o
JOIN customers c ON o.customerId = c.customerId
WHERE o.status = 'shipped'
GROUP BY c.country

It compiles to DynamoDB's native Query and Scan operations, planned against your
real keys and indexes, and performs the join and the aggregation on the client. It
supports INNER JOIN and LEFT JOIN, GROUP BY, and COUNT/SUM — the operations
PartiQL does not have. It does not add a server-side join to DynamoDB, because nothing
can; it removes the glue code you would otherwise write around the gap.

Working with DynamoDB

Once you have chosen DynamoDB, you still need a fast way to browse tables, run queries and edit items day to day. DynoTable is a native desktop GUI for DynamoDB: browse and inline-edit items, build key conditions and filters, and query with a SQL Workbench and an AI agent. If you are coming from MongoDB's query-anything mental model, the SQL workbench helps you express relational-shaped queries while staying within DynamoDB's access-pattern rules.

For building expressions without leaving the docs, the free DynamoDB Expression Builder generates key-condition, filter and update expressions with ready-to-paste SDK, CLI and PartiQL output. And if you are new to modeling for a key-value store, the how to model data in DynamoDB guide walks through translating entities and access patterns into keys.

FAQ

Is DynamoDB faster than MongoDB?

Neither is universally faster — it depends on the workload and how the data is modeled. DynamoDB is designed to deliver consistent low-latency performance at large scale for key-based access, while MongoDB can be very fast for indexed and ad-hoc queries. The right data model matters more than the engine for real-world latency.

Can DynamoDB replace MongoDB?

Sometimes. If your access patterns are predictable and key-based, DynamoDB can replace MongoDB and remove most operational overhead. If you rely on ad-hoc queries or server-side aggregations across many fields, a migration means remodeling your data around DynamoDB's keys first — see how to model data in DynamoDB.

Is DynamoDB based on MongoDB?

No. They are unrelated products from different companies. DynamoDB is an AWS-managed database whose design lineage traces to Amazon's internal Dynamo work, while MongoDB is a separate open-source-rooted document database. They share the "NoSQL" label but no codebase.

Related

• Learn single-table design and how to model data in DynamoDB.
• Build expressions with the free DynamoDB Expression Builder.
• Download DynoTable to browse, query and edit DynamoDB with a SQL workbench and AI.

References

• What is Amazon DynamoDB? — AWS DynamoDB Developer Guide
• Improving data access with secondary indexes in DynamoDB
• PartiQL — a SQL-compatible query language for DynamoDB
• DynamoDB service quotas (400 KB item size limit)
• MongoDB Query API
• MongoDB aggregation pipeline
• MongoDB limits and thresholds (16 MB BSON document size)
• MongoDB multi-document ACID transactions
• MongoDB Atlas Serverless instance limitations (migration to Flex)

Last verified 2026-07-13 against the official AWS DynamoDB Developer Guide and MongoDB documentation. MongoDB is a trademark of its respective owner; referenced here for identification only.