Amazon DynamoDB
NoSQL Database
A fully managed, serverless, key-value NoSQL database designed to run high-performance applications at any scale.
✅ Key Advantages
- •Single-digit millisecond performance at any scale
- •Fully serverless (no mandatory provisioning)
- •Built-in multi-region active-active replication (Global Tables)
- •Deeply integrated with AWS IAM and Lambda
⚠️ Trade-offs
- •Complex query patterns require extensive secondary index planning
- •Vendor lock-in to AWS
- •Item size limit of 400KB
- •Unexpected costs if partition keys are poorly designed
MongoDB
NoSQL Database
A source-available cross-platform document-oriented database program classified as a NoSQL database program.
✅ Key Advantages
- •Flexible, schemaless JSON document structure
- •Rich query language supporting aggregations and geospatial queries
- •Horizontal scalability via native sharding
- •Cloud-agnostic (can run on AWS, GCP, Azure, or on-premise)
⚠️ Trade-offs
- •High memory consumption (loads working set into RAM)
- •Lacks traditional ACID compliance across multiple documents (historically, though improved in recent versions)
- •Sharding configuration can be operationally complex
- •Data duplication is required for fast reads due to lack of JOINs
Engineering Metrics
Expected Latency
Amazon DynamoDB~5-10ms (reads/writes, un-cached)
MongoDB~1-5ms (reads from RAM), ~10-20ms (disk writes)
Cost Scaling
Amazon DynamoDB
Pay-per-request or Provisioned Capacity. Can be extremely cheap for bursty workloads, but expensive for sustained, highly predictable heavy reads.
MongoDB
Usually provisioned instance based (e.g., MongoDB Atlas). Cost scales linearly with RAM and vCPU requirements.
Don't guess the costs in an interview. Prove it.
Simulate high-traffic workloads against both Amazon DynamoDB and MongoDB instantly using the MockArch canvas. Let AI analyze your system.
Start Architecting Now