A gaming company was spending $45K/month on DynamoDB for a user-profile workload with highly variable access patterns. After analyzing their query patterns with us, they migrated to MongoDB Atlas — and their bill dropped to $8K/month while gaining the flexible querying they needed for analytics. DynamoDB had been the wrong choice from day one, but it took 18 months and a painful migration to discover it.
DynamoDB and MongoDB solve the same problem differently. Understanding where each excels prevents costly migrations later.
- DynamoDB excels at predictable, high-throughput key-value and single-table access patterns at any scale.
- MongoDB is better for flexible schemas, ad-hoc queries, and workloads where access patterns evolve.
- DynamoDB costs are predictable at known throughput; MongoDB costs scale with data volume and compute.
- Neither is universally better — the decision hinges entirely on your access patterns.
Architecture Comparison
| Dimension | DynamoDB | MongoDB |
|---|---|---|
| Data model | Key-value / document (limited) | Rich document (BSON) |
| Query model | Primary key + GSI only | Any field, aggregation pipelines |
| Transactions | ACID (single-region, up to 25 items) | ACID (multi-document, distributed) |
| Scaling | Automatic, serverless | Manual sharding or Atlas auto-scaling |
| Managed options | AWS DynamoDB only | MongoDB Atlas, self-hosted |
| Secondary indexes | GSI + LSI (limited) | Any field, compound, text, geo |
When DynamoDB Wins
Predictable High-Throughput Key-Value Access
DynamoDB is the right choice when your access patterns are known, consistent, and key-value-oriented. E-commerce session stores, gaming leaderboards, and IoT device state are ideal workloads:
import boto3
dynamodb = boto3.resource('dynamodb')
table = dynamodb.Table('Sessions')
# Single-item get — always fast, always consistent cost
response = table.get_item(Key={'user_id': 'u123', 'session_id': 's456'})
# Batch get — up to 100 items in one request
response = dynamodb.batch_get_item(RequestItems={
'Sessions': {'Keys': [{'user_id': 'u123', 'session_id': f's{i}'} for i in range(100)]}
})On-Demand Scaling Without Ops Work
DynamoDB on-demand mode scales instantly with zero configuration. For traffic spikes from Black Friday to near-zero off-season, DynamoDB handles it automatically. MongoDB requires capacity planning.
When MongoDB Wins
Flexible Access Patterns and Ad-Hoc Queries
// MongoDB: query any field, any depth
db.orders.find({
"customer.country": "US",
"items.sku": { $in: ["SKU123", "SKU456"] },
created_at: { $gte: new Date("2025-01-01") }
}).sort({ total: -1 }).limit(100)
// DynamoDB equivalent requires a GSI on every query field — and you may hit GSI limitsComplex Aggregation
// MongoDB aggregation pipeline — run directly in the database
db.orders.aggregate([
{ $match: { status: "completed", year: 2025 } },
{ $group: { _id: "$product_id", revenue: { $sum: "$amount" }, count: { $sum: 1 } } },
{ $sort: { revenue: -1 } }, { $limit: 20 }
])DynamoDB's 25-item transaction limit is a hard constraint. If your use case requires transactional updates across more than 25 items in a single operation, DynamoDB is the wrong database.
Cost Comparison
| Scenario | DynamoDB (on-demand) | MongoDB Atlas (M30) |
|---|---|---|
| 10M reads/day, 1M writes/day | ~$350/month | ~$190/month (flat) |
| 100M reads/day, 10M writes/day | ~$3,500/month | ~$380/month (same instance) |
| Variable traffic (10x spikes) | Scales automatically | Requires over-provisioning |
| 100GB data, 10M reads/day | ~$380/month | ~$190/month |
DynamoDB on-demand is expensive at high sustained throughput. If your throughput is predictable, DynamoDB provisioned mode with auto-scaling is 50-80% cheaper than on-demand at the same throughput.
- Choose DynamoDB for known, key-value access patterns, serverless scaling, and AWS-native integration.
- Choose MongoDB when access patterns are evolving, queries are complex, or you need aggregation pipelines.
- DynamoDB on-demand is expensive at high sustained throughput — evaluate provisioned mode at scale.
- Single-table design in DynamoDB requires upfront access pattern design that MongoDB does not.
Working with JusDB on NoSQL Strategy
JusDB helps teams choose between DynamoDB and MongoDB, design data models for either, and migrate between them with minimal downtime. We have migrated workloads in both directions — DynamoDB to MongoDB and MongoDB to DynamoDB — depending on evolving access patterns.