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.
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 assistant. 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 modelling 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 modelled. 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 remodelling 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.
Last verified 2026-07-12 against the official AWS DynamoDB Developer Guide and MongoDB documentation. MongoDB is a trademark of its respective owner; referenced here for identification only.