MongoDB vs DynamoDB — sound familiar?
- ▸ Inheriting one, considering switching — Your team standardised on MongoDB years ago and now the AWS bill (Atlas + AWS infra) is twice what DynamoDB would cost. Or vice-versa: stuck on DynamoDB and the query limitations are forcing application-layer joins.
- ▸ Cost predictability question — DynamoDB on-demand vs provisioned vs MongoDB Atlas M-tier — three pricing models, none directly comparable. Decision blocking the platform choice.
- ▸ Query complexity outgrew the schema — DynamoDB requires you to design partition keys for every query upfront. New access patterns mean schema work + GSI cost. MongoDB's ad-hoc query flexibility is starting to look appealing.
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MongoDB vs DynamoDB
MongoDB and DynamoDB are both NoSQL document stores, but their philosophies diverge sharply. MongoDB is a feature-rich, schema-flexible, query-rich document database that runs anywhere. DynamoDB is a constrained, ops-free, AWS-native key-value store with serverless cost predictability. The right pick depends on your query patterns, cost model preference, multi-cloud requirements, and team familiarity. This guide is the production DBA view of where each wins.
Feature Matrix
MongoDB vs DynamoDB — side-by-side
| Feature | MongoDB | DynamoDB |
|---|---|---|
| Data model | Document (BSON, deep nesting) | Key-value + document (smaller items) |
| Query model | Ad-hoc queries, aggregation pipeline, $lookup | Partition-key + sort-key + GSIs (predefined) |
| Schema flexibility | Fully flexible | Flexible attributes per item, but query patterns must be designed upfront |
| Max document size | 16 MB | 400 KB per item |
| Indexing | Single-field, compound, multi-key, geospatial, text, vector | Primary key + GSI / LSI (limited to 20 GSIs) |
| Transactions | Multi-document ACID (replica set + sharded) | ACID for items in same partition + transactional API |
| Consistency | Configurable (eventual / read-your-write / linearisable) | Strong (read-after-write) or eventual |
| Joins | $lookup aggregation stage | Application-layer only |
| Hosting model | Self-managed, Atlas (any cloud), DocumentDB (AWS) | AWS only (managed) |
| Cost model | Atlas M-tier (provisioned) or Serverless | On-demand (per request) or provisioned (per RCU/WCU) |
| Min cost (cluster) | Atlas M0 (free) → M10 (~$60/mo) | ~$0 (truly on-demand, scale to zero) |
| Scaling | Manual (shard add), Atlas auto-scaling | Auto-scaling (provisioned) or fully on-demand |
| Backup | Continuous (Atlas), snapshot, oplog tailing | Point-in-time recovery (continuous), snapshots |
| Global distribution | Atlas Global Cluster, multi-region replicas | Global Tables (cross-region active-active) |
| Vector search | Atlas Vector Search (knn / hybrid) | No native; use OpenSearch Serverless or external |
| Time-series | Atlas time-series collections | Possible via design but no native support |
| Change streams / triggers | Change Streams (built-in) | DynamoDB Streams + Lambda triggers |
| Migration friction | Atlas migration tools (mongosync, live migration) | AWS DMS, AWS Glue, or custom |
| Multi-cloud / portability | High (Atlas runs on AWS, GCP, Azure) | AWS-only |
| Operations burden | Self-managed: high. Atlas: low. | Lowest — fully managed serverless |
When MongoDB wins
Ad-hoc query flexibility
MongoDB's aggregation pipeline + $lookup let you query data without knowing access patterns in advance. New product features don't require schema redesign.
Multi-cloud portability
Atlas runs identically on AWS, GCP, Azure. If multi-cloud is a strategic requirement, MongoDB Atlas is the only choice.
Larger document size
16 MB vs DynamoDB's 400 KB. Storing PDF metadata, large nested objects, or product catalogs with deep variation is feasible.
Richer indexing options
Geospatial, text, vector (Atlas Vector Search), wildcard, partial — all native. DynamoDB's GSI model handles fewer access patterns elegantly.
Aggregation pipeline
Server-side data transformation with $project, $group, $unwind, $lookup. DynamoDB requires client-side or external (Athena, Glue).
When DynamoDB wins
Operational burden
DynamoDB is fully managed, scales to zero, requires zero capacity planning. MongoDB self-managed or Atlas both involve more ops decisions.
Cost predictability at scale
DynamoDB on-demand: pay per request. Provisioned with auto-scaling: cap-and-burn budgets. MongoDB Atlas M-tier sizing requires capacity planning.
AWS-native integration
Lambda, AppSync, EventBridge, IAM — DynamoDB integrates with the AWS event-driven stack natively. MongoDB needs API Gateway shims.
Single-digit-ms global tables
Cross-region active-active with sub-10ms latency anywhere. MongoDB Atlas Global Clusters can do this but with more setup.
Serverless cost floor
DynamoDB on-demand truly scales to zero — pay nothing when idle. Atlas M-tier has a floor (M0 free has limits; M10+ has minimums).
Migration
Migration paths between MongoDB and DynamoDB
MongoDB → DynamoDB
Most complex migration in NoSQL. Requires re-designing access patterns into partition keys + GSIs. Use AWS DMS or custom scripts; expect application code changes.
DynamoDB → MongoDB
Easier — DynamoDB items map naturally to MongoDB documents. AWS DMS or dump-to-JSON + mongoimport. Application code changes for query syntax.
Cross-cloud
MongoDB Atlas → AWS DynamoDB: full re-architecture. DynamoDB → MongoDB Atlas (any cloud): simpler re-mapping.
FAQ
Common questions
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