Amazon DynamoDB Complete Guide 2025: Architecture, Pricing, Use Cases & Alternatives

TL;R: DynamoDB at a Glance

• Amazon DynamoDB is a serverless, fully managed NoSQL database that blends key-value and document models. It delivers single-digit millisecond latency, scales automatically, and integrates tightly with the AWS ecosystem.
  
• Key features:
  
  • Serverless, auto-scaling, multi-AZ replication
    
  • Supports 400 KB item size, key-value & document storage
    
  • Flexible consistency modes (eventual vs. strong)
    
  • Secondary indexes (LSI & GSI) for additional query patterns
    
  • Features like TTL (auto-expire items), Streams (event triggers), global tables, and DAX caching
    
• Alternatives:
  
  • Managed: Azure Cosmos DB, MongoDB Atlas, Google Firestore, Couchbase, AWS Keyspaces, ScyllaDB Cloud
    
  • Open source: MongoDB, MySQL, MariaDB, PostgreSQL, Firebase, Apache Cassandra, ScyllaDB
    

DynamoDB is perfect for apps needing speed, scale, and simplicity in the AWS cloud. But for complex queries, heavy analytics, or multi-cloud flexibility, alternatives like Cosmos DB, MongoDB Atlas, PostgreSQL, or Cassandra may be better fits.

DynamoDB’s data model is simple but powerful. Everything revolves around tables, items, and keys:

Table of Contents

Amazon DynamoDB: An Overview

Additional features:

• Auto Scaling: Adjusts capacity automatically.
  
• Global Tables: Active-active replication across regions.
  
• TTL: Auto-expire items based on timestamps.
  
• Streams: Change data capture for real-time triggers.
  
• DAX: In-memory cache for microsecond read latency.

Common use cases

DynamoDB’s architecture makes it ideal for workloads requiring low‑latency access to data at scale. Some of the most common use cases include:

These use cases share a common theme: high concurrency, rapid elasticity and simple access patterns. DynamoDB is less suitable for complex analytics or multi‑table joins; such workloads are typically offloaded to services like Amazon Redshift, Athena or Aurora.

Is DynamoDB Expensive? Pricing and Cost Breakdown

Amazon DynamoDB pricing consists of several components. The two principal capacity modes are on‑demand and provisioned:

1. On‑demand capacity mode – You pay per request. Each write request unit (WRU) corresponds to a 1 KB write and is priced at $0.25 per million writes. Each read request unit (RRU) corresponds to a 4 KB strongly consistent read (or two eventually consistent reads) and is also $0.25 per million reads. On‑demand mode is suitable for unpredictable workloads because you don’t provision capacity. However, it can be more expensive for consistently high traffic.
   
2. Provisioned capacity mode – You specify the number of RCUs and WCUs needed. In the us‑east‑1 region, each WCU costs $0.00065 per hour and each RCU costs $0.00013 per hour. You pay for the allocated capacity whether or not you use it; unused capacity can be scaled down automatically. Reserved capacity (1–3‑year commitments) offers up to 75 % savings.
   

Example Scenarios

• On-Demand Startup: 3.55M reads + 3.55M writes + 27 GB storage → ≈ $2.28/month
• Steady SaaS App: 100 WCUs + 200 RCUs + 50 GB storage → ≈ $79/month

Cost optimization tips

• Choose the right capacity mode – On‑demand is ideal for unpredictable workloads; provisioned with auto scaling is cheaper for steady traffic.
  
• Use reserved capacity – Commit to a 1‑ or 3‑year term to save up to 75 % on provisioned capacity.
  
• Optimize storage – Use the Standard‑IA table class for infrequently accessed data, archive cold items to S3 and design schemas to minimize item size.
  
• Reduce read/write costs – Use eventually consistent reads (half the cost of strong reads), batch operations (BatchGetItem/BatchWriteItem), avoid full table scans and keep items small.
  
• Leverage caching – Deploy DAX for read‑heavy workloads, ensuring the savings from reduced RCUs outweigh the cost of DAX nodes.
  
• Monitor usage – Use CloudWatch and Cost Explorer to track consumption and set alerts; remove unused backups and manage TTL to delete outdated items.
  

Limitations and challenges

While DynamoDB provides excellent scalability and performance, it has several limitations:

1. Query flexibility – The query model is limited to primary keys and secondary indexes. Complex joins, aggregations and full‑text search require other services (e.g., Elasticsearch or Redshift). Secondary indexes increase cost and require careful design.
   
2. Item size limit – A single item cannot exceed 400 KB, which is much smaller than document databases like MongoDB (16 MB) or Cassandra (2 GB). Large objects should be stored in S3 and referenced from DynamoDB.
   
3. Partition throughput limits – Hot partitions can occur if the partition key isn’t sufficiently distributed, leading to throttling and increased latency.
   
4. Vendor lock‑in – DynamoDB runs exclusively on AWS. Migrating to other clouds or on‑premises systems requires rewriting applications or using compatible services.
   
5. Cost complexity – Pricing depends on many factors (capacity, storage, DAX, streams, data transfer) and can result in unexpected bills for spiky workloads.
   

DynamoDB vs MongoDB vs Cassandra vs others: Which Should You Choose?

Because of these limitations, teams may consider other databases. The best choice depends on data model, query requirements, cost and vendor preference. Alternatives can be divided into managed cloud services and open‑source/self‑managed solutions.

Managed cloud alternatives

• Azure Cosmos DB (Microsoft) – A globally distributed, multi‑model database with support for key‑value, document, graph and column‑family models. It offers high availability, strong consistency options and is ideal for geographically distributed applications.
  
• MongoDB Atlas – A cloud‑hosted document database with a rich query language, flexible schema and large developer community. It supports ad‑hoc queries and aggregation pipelines but can be expensive at very large scale.
  
• Google Cloud Firestore – A NoSQL document database with offline support and real‑time synchronization, optimized for mobile/web applications.
  
• Couchbase – A distributed NoSQL database that offers strong consistency, flexible indexing and can run both on‑premises and across multiple clouds.
  
• Amazon Keyspaces (managed Cassandra) – An AWS service compatible with Cassandra Query Language (CQL) but still inherits some inefficiencies of Cassandra’s Java architecture.
  
• ScyllaDB Cloud – A managed service compatible with Cassandra and DynamoDB protocols. Written in C++ with a shard‑per‑core architecture, ScyllaDB provides high throughput, low latency and more predictable pricing.
  

Open‑source and self‑hosted alternatives

• MongoDB (self‑hosted) – Offers limitless scalability, stores structured/unstructured data in JSON documents and provides powerful query capabilities. It is highly flexible, compatible with multiple languages and offers a strong community.
  
• MySQL – A relational database using SQL. It is open source, user‑friendly, partially ACID‑compliant and well suited for structured data and large volumes. It offers high availability, can combine with NoSQL options and has a vibrant community.
  
• MariaDB – A fork of MySQL that adds features such as columnar storage engines and hybrid workloads. It is open source, fully SQL compliant and popular among users who value independence and long‑term reliability.
  
• PostgreSQL – An object‑relational database known for flexibility. It supports both relational and some non‑relational types, integrates easily with other databases and is valued by companies like Apple and Spotify.
  
• Firebase (Cloud Firestore / Realtime DB) – Google’s mobile‑focused database with low latency, offline mode and integration with other Google services.
  
• Apache Cassandra – An open‑source column‑oriented NoSQL database designed for big data. It provides horizontal scalability, fault tolerance and an SQL‑like query language (CQL). Major companies (JPMorgan, Netflix, Apple) run Cassandra for large‑scale data workloads.
  
• ScyllaDB – A source‑available database written in C++ that is compatible with Cassandra and DynamoDB. It offers high throughput, predictable pricing and a DynamoDB‑compatible API (Alternator) to ease migration.
  

When evaluating alternatives, consider cost, scalability, query flexibility, vendor lock‑in and technical expertise required. Self‑hosting solutions provide more control but require maintenance and operational expertise; managed services offer convenience at the expense of higher cost or vendor dependence.

Comparative Analysis of alternative databases

Managed cloud alternatives

Open‑source and self‑hosted alternatives

When NOT to Use DynamoDB

Amazon DynamoDB is powerful, but it’s the wrong choice for certain workloads. Below are clear scenarios where you should consider alternatives.

Complex Queries and Analytics

Avoid DynamoDB if you need:

• SQL JOINs across multiple tables
  
• Ad-hoc queries on non-indexed attributes
  
• Aggregations (SUM, COUNT, GROUP BY, AVG)
  
• Full-text search or fuzzy matching
  
• Complex WHERE clauses with multiple conditions
  

Example: An analytics dashboard that queries:
“Show me all orders from California customers who bought electronics in the last 30 days, grouped by city with average order value.”

Better choice: PostgreSQL, Snowflake, or BigQuery

Large Object Storage

DynamoDB’s 400KB item limit makes it unsuitable for:

• Images, videos, or documents
  
• Large JSON logs or documents
  
• Binary data like PDFs or media files
  
• Long-form text content
  

Example: A CMS storing blog posts with embedded images.

Better choice: MongoDB (16MB limit), or store large objects in S3 with DynamoDB references.

Relational Data with Complex Relationships

Skip DynamoDB when you have:

• Many-to-many relationships
  
• Strict referential integrity
  
• ACID transactions across multiple tables
  
• Complex parent-child hierarchies
  

Example: An HR system tracking employees, departments, projects, and skills.

Better choice: PostgreSQL, MySQL, or Amazon Aurora

Cost-Sensitive High-Volume Steady Workloads

DynamoDB gets expensive when:

• Sustained traffic exceeds 10M requests/day
  
• Predictable, constant load 24/7
  
• Large datasets with frequent full scans
  
• Multiple GSIs with full projections
  

Example: A logging system writing 100M events daily with steady throughput.

Better choice: Self-hosted Cassandra or ScyllaDB (fixed infrastructure costs).

Multi-Cloud or Hybrid Requirements

DynamoDB won’t work if you need:

• Portability across clouds
  
• On-premises deployment
  
• Avoiding AWS lock-in
  
• Edge databases for local computing
  

Example: An enterprise app that must run on AWS, Azure, and on-prem data centers.

Better choice: Cassandra, MongoDB, or CockroachDB

Development and Testing Constraints

DynamoDB is challenging when:

• Local dev environments without internet
  
• CI/CD pipelines needing embedded DBs
  
• Unit testing with database mocks
  
• Cost-sensitive dev environments
  

Example: Offshore dev team with limited AWS access and budget constraints.

Better choice: MongoDB or PostgreSQL with local Docker containers.

Specific Technical Limitations

Don’t use DynamoDB if you require:

• Stored procedures or triggers
  
• Complex nested data (beyond 32 levels)
  
• Immediate global consistency (global tables have ~1s lag)
  
• Backups older than 35 days
  
• Exactly-once CDC (change data capture)
  

Quick Decision Matrix

The “Red Flags” Checklist

Reconsider DynamoDB if you answer yes to any:

• Need queries by more than 5 attribute combinations?
  
• Items regularly exceed 100KB?
  
• Monthly AWS bill must stay under $100?
  
• Require JOINs across multiple tables?
  
• Must run outside AWS?
  
• Need ACID across tables?
  
• Frequent analytical queries required?
  
• Need full-text search?
  

Migration Complexity Warning

Moving away from DynamoDB later requires:

• Complete rewrite of the data access layer
  
• ETL pipelines for migration
  
• Redesigning data models
  
• Potential downtime or dual-writes
  

Switching cost: Often 3–6 months of engineering effort for production workloads.

Bottom Line

• Use DynamoDB for speed, scale, and simplicity in AWS-centric, key-based applications.
  
• Choose alternatives when you need query flexibility, complex relationships, multi-cloud portability, or predictable costs at high scale.

Conclusion

DynamoDB is a battle-tested AWS-native NoSQL database that excels at speed, scale, and simplicity. It’s the right choice for applications where millisecond response times and elastic scaling matter most.

However, for analytics-heavy workloads, relational joins, or multi-cloud strategies, alternatives like Cosmos DB, MongoDB Atlas, PostgreSQL, or Cassandra may be more appropriate.

For teams that want DynamoDB speed without its query and analytics limitations, platforms like Knowi let you unify DynamoDB data with SQL, NoSQL, and APIs – no ETL required.

Frequently Asked Questions