Cassandra vs DynamoDB (2025): Complete Guide with Pricing, Performance, Usecase & Migration Tips

Table of Contents

Introduction

In 2025, the rise of real-time applications, global-scale services, and AI-driven workloads has made NoSQL databases indispensable. Traditional relational systems often struggle with elastic scalability, horizontal distribution, and the need for millisecond responses at petabyte scale.

Two leaders stand out in this space: Apache Cassandra and Amazon DynamoDB. Both are designed for distributed, fault-tolerant, high-throughput use cases, but their philosophies couldn’t be more different:

• Cassandra is an open-source, peer-to-peer distributed database that gives organizations control, flexibility, and multi-cloud freedom.
  
• DynamoDB is a fully managed, serverless AWS service, ideal for teams that want simplicity, automation, and deep cloud-native integration.

This guide is your complete head-to-head comparison of Cassandra vs DynamoDB in 2025. We’ll dive into architecture, performance benchmarks, pricing models, ecosystem fit, migration considerations, disaster recovery, and monitoring, so architects, developers, and decision-makers can make an informed choice.

TL;DR: Cassandra or DynamoDB?

Choose Cassandra if you need:

• Multi-cloud or hybrid flexibility → You don’t want to be locked into AWS and need to run across AWS, Azure, GCP, or on-prem simultaneously.
  
• Open-source control → Ability to tune consistency levels, configure replication, and run clusters your way without vendor restrictions.
  
• Massive global scale → Proven at handling trillions of writes per day for workloads like Netflix, Instagram, and large banks.
  
• Write-heavy workloads → Ideal for IoT telemetry, time-series data, fraud detection, and real-time event logging, where sustained throughput matters more than ultra-low read latency.
  
• Long-term cost predictability → Infrastructure-based pricing may be cheaper at very high, steady workloads vs. DynamoDB’s per-request model.
  

Choose DynamoDB if you need:

• AWS-native simplicity → You’re already building in AWS and want seamless integration with Lambda, API Gateway, S3, Step Functions, etc.
  
• Serverless workloads → DynamoDB scales up and down automatically, making it perfect for apps with bursty or unpredictable traffic patterns.
  
• E-commerce applications → Ideal for shopping carts, product catalogs, and inventory systems where low-latency reads and global replication are critical.
  
• Gaming and real-time apps → Great for leaderboards, player states, and session tracking, thanks to predictable millisecond response times.
  
• Event-driven architectures → DynamoDB Streams + Lambda enable event sourcing, microservices orchestration, and AI-driven triggers without managing queues or ETL pipelines.
  

 In practice:

• Cassandra is often chosen by enterprises that prioritize control, multi-cloud resilience, and predictable performance at extreme scale.
  
• DynamoDB is chosen by teams that want speed to market, hands-off scaling, and deep AWS ecosystem integration.
  

Overview of Each Database

What is Apache Cassandra?

Apache Cassandra is an open-source, distributed NoSQL database designed for linear scalability, fault tolerance, and global availability.

• History: Developed at Facebook (2008) by Avinash Lakshman (co-creator of Amazon Dynamo) and Prashant Malik to power Inbox Search. Open-sourced in July 2008; became a top-level Apache project in 2010.
  
• Architecture: Masterless, peer-to-peer ring structure

• Decentralized design → No master node; all nodes are peers with equal responsibility.
  
• Consistent hashing → Distributes data evenly across nodes to avoid hotspots.
  
• Gossip protocol → Nodes share state and health info continuously to keep the cluster synchronized.
  
• No single point of failure → Any node can accept reads and writes; requests reroute automatically if one fails.
  
• Replication → Data is replicated across multiple nodes (and data centers) for fault tolerance and high availability.
  
• Linear scalability → Adding nodes increases capacity proportionally; Cassandra rebalances data automatically.
  
• Global resilience → Designed for multi-region deployments, ensuring data is always close to users and protected against regional outages.

• Adoption: Used by Netflix (1+ trillion requests/day), Instagram (social feeds), Uber, and large banks for fraud detection and compliance.
  
• 2025 Strengths:
  
  • Multi-region resilience with tunable consistency.
    
  • Kubernetes-native operators simplify deployments.
    
  • Vector search support in Cassandra 5.0 expands AI/ML use cases.
    

What is Amazon DynamoDB?

Amazon DynamoDB is a fully managed, serverless NoSQL database service from AWS, optimized for low-latency performance and near-infinite scaling.

• History: Launched in 2012, inspired by Amazon’s internal Dynamo system (2004).
  
• Architecture: Partitioned, SSD-backed storage with synchronous replication across multiple AZs

• Partitioned data model → Tables are automatically split into partitions (shards) based on partition keys and workload volume, ensuring elastic scalability.
  
• Leader–follower replication → Each partition has a leader node that handles writes and synchronously replicates to multiple follower nodes for durability.
  
• SSD-backed storage → Optimized for fast, predictable single-digit millisecond performance at scale.
  
• Multi-AZ durability → Data is synchronously replicated across at least three Availability Zones (AZs) within a region to withstand infrastructure failures.
  
• Automatic partition management → DynamoDB splits or merges partitions automatically as datasets grow or workloads spike, requiring no manual sharding.
  
• Global Tables for multi-region → Optional feature that extends replication across AWS regions, enabling active-active deployments with 99.999% availability.
  
• Seamless scaling → Applications don’t need to manage nodes; DynamoDB abstracts the underlying infrastructure while maintaining performance guarantees.

• Adoption: Used by Amazon.com, Airbnb, Disney+, Zoom, and Fidelity.
  
• 2025 Strengths:
  
  • Automatic partitioning & scaling (no ops required).
    
  • Deep integration with AWS ecosystem (Lambda, API Gateway, S3, OpenSearch).
    
  • Global Tables for active-active replication across regions.
    
  • Ideal for event-driven AI workloads via DynamoDB Streams.
    

DynamoDB or Cassandra: Which Database Fits Your Architecture?

Architecture & Scalability

Cassandra

• Peer-to-peer architecture; nodes communicate via gossip protocol.
  
• Data is replicated across nodes using consistent hashing.
  
• Scaling = add more nodes manually.
  
• Multi-datacenter replication built-in.
  

DynamoDB

• Partitioned, leader-follower replication.
  
• Data automatically sharded into partitions.
  
• Scaling is automatic and elastic, handled by AWS.
  
• Global Tables enable multi-region, multi-active replication.
  

Cassandra = control & multi-cloud flexibility. 

DynamoDB = hands-off scalability inside AWS.

Performance & Querying

Cassandra Performance

• Write latency: ~1–2ms.
  
• Proven scale: Netflix runs 1 trillion requests/day on Cassandra.
  
• Linear throughput: Add nodes → capacity grows proportionally.
  
• Optimized for high-write workloads (logs, time-series, IoT).
  

Sample Query (CQL):

CREATE TABLE users (
    user_id UUID PRIMARY KEY,
    name TEXT,
    email TEXT,
    created_at TIMESTAMP
);
SELECT * FROM users WHERE user_id=12345;

DynamoDB Performance

• Latency: 4–6ms reads, 5–10ms writes (sub-ms with DAX cache).
  
• Predictable performance with provisioned capacity or on-demand mode.
  
• Transactions supported (ACID compliance).
  
• Best for consistent low-latency reads and event-driven apps.
  

Sample Query (PartiQL):

CREATE TABLE Users (
    user_id STRING,
    name STRING,
    email STRING,
    created_at STRING,
    PRIMARY KEY(user_id)
);
SELECT * FROM Users WHERE user_id='12345';

Cassandra = best for high-throughput writes. DynamoDB = best for low-latency reads & serverless apps.

Ecosystem & Integrations

• Cassandra:
  
  • Integrates with Spark (analytics), Kafka (streaming), Kubernetes (deployment).
    
  • Managed offerings: DataStax Astra, Instaclustr.
    
• DynamoDB:
  
  • Native AWS integration: Lambda, API Gateway, S3, OpenSearch, Step Functions.
    
  • DAX (DynamoDB Accelerator) for in-memory caching.
    
  • Streams for event-driven architectures.
    

Tools like Knowi connect natively to Cassandra, DynamoDB, and 30+ data sources. This enables unified analytics without ETL, blending NoSQL, SQL, APIs, and even documents.

How Much Does Cassandra Cost vs DynamoDB?

Example: 100M requests/month, 500GB storage

• DynamoDB = cheaper for small/medium workloads.
  
• Cassandra = more cost-effective at very large scale, where infra amortizes.
  

Security & Compliance

• Cassandra: TLS encryption, pluggable auth, role-based access control. Compliance depends on hosting (AWS, Azure, on-prem).
  
• DynamoDB: Native AWS IAM integration, encryption at rest/in transit, and compliance with PCI DSS, HIPAA, FedRAMP, GDPR.
  

Pros and Cons

Use Cases & Industry Adoption

Cassandra Best Fits:

Telecom & IoT: Cassandra is purpose-built for time-series data at extreme scale. Telecom providers rely on it to monitor call records, network events, and sensor telemetry in real time. In IoT, it’s often used to store billions of sensor readings per day from connected devices, with the ability to query across time buckets for anomaly detection and predictive maintenance.

Banking & Fintech: Cassandra’s tunable consistency and multi-datacenter replication make it a strong choice for financial services. It powers fraud detection systems, customer transaction histories, and ledger applications, where high write throughput and zero downtime are essential. For instance, fraud detection pipelines can ingest transactions in real time and flag anomalies within milliseconds.

Multi-cloud SaaS Platforms: Many SaaS companies adopt Cassandra to avoid vendor lock-in and to provide services across AWS, Azure, and GCP. Its open-source nature allows organizations to deploy globally, ensuring compliance with data residency laws while still guaranteeing performance and resilience.

DynamoDB Best Fits:

E-commerce: DynamoDB is widely used for shopping carts, product catalogs, and inventory systems. Its millisecond latency ensures that customers can browse and checkout seamlessly even during peak traffic surges like Black Friday or Prime Day. Global Tables let e-commerce providers serve users across regions with local responsiveness.

Gaming: Modern gaming platforms require real-time state management. DynamoDB supports leaderboards, player sessions, achievements, and matchmaking systems with consistent performance at scale. Since traffic in gaming can be highly spiky (new releases, in-game events), DynamoDB’s autoscaling ensures smooth user experiences without pre-provisioning.

Serverless Architectures: For teams building AWS-native applications with Lambda, API Gateway, and Step Functions, DynamoDB is a natural fit. It scales transparently, integrates seamlessly with other AWS services, and removes the need for manual infrastructure management. Event-driven patterns like DynamoDB Streams + Lambda power microservices and reactive workflows.

Startups: Startups often choose DynamoDB because it allows them to move fast without ops teams. Its pay-as-you-go pricing is budget-friendly for unpredictable workloads, while the fully managed model lets developers focus on features instead of database administration. As the product grows, DynamoDB automatically scales without re-architecting.

When to Choose Which?

• Choose Cassandra if:
  
  • You need multi-cloud/hybrid flexibility.
    
  • You want control & open-source freedom.
    
  • Your workload = billions of writes/day.
    
• Choose DynamoDB if:
  
  • You are all-in on AWS.
    
  • You want serverless simplicity.
    
  • Your traffic is spiky/unpredictable.
    

Many enterprises use both (Cassandra for multi-cloud, DynamoDB for AWS apps), then unify analytics with Knowi.

Alternatives to Consider

• MongoDB Atlas: Developer-friendly document database.
  
• Couchbase: Hybrid key-value + SQL.
  
• Cosmos DB: Microsoft’s globally distributed DB.
  
• Redis: Real-time caching & session store.
  

Migration Considerations (Cassandra ↔ DynamoDB)

Cassandra → DynamoDB

1. Map access patterns to single-table design (PK/SK + GSIs).
   
2. Estimate item sizes → choose on-demand or provisioned + autoscaling.
   
3. Bulk-load via Glue/DMS; validate counts/checksums.
   
4. Dual-write for safe cutover; flip reads with feature flags.
   
5. Monitor throttling; add DAX for hot read paths.
   

DynamoDB → Cassandra

1. Expand single-table into multiple Cassandra tables per query pattern.
   
2. Size cluster for peak write throughput; pick compaction strategy.
   
3. Backfill with Spark; local quorum reads/writes; schedule repairs.
   
4. Dual-write and phase read cutover; build dashboards for latency & GC.
   

Data-Modeling Differences

• DynamoDB favors single table + secondary indexes; Cassandra favors many query-tuned tables.
  
• Cassandra’s clustering columns give natural time-series ordering; DynamoDB relies on sort keys + careful key design.
  
• Consistency is tunable in Cassandra, opt-in strong in DynamoDB.
  
• Costs in DynamoDB scale per-KB operation; Cassandra costs scale with infra and ops effort.

Conclusion

Both Apache Cassandra and Amazon DynamoDB are leaders in the NoSQL space, but their strengths align with different strategies.

• Cassandra = freedom, scalability, and multi-cloud resilience.
  
• DynamoDB = simplicity, serverless scaling, and AWS-native speed.
  

Looking for analytics across Cassandra, DynamoDB, MongoDB, and beyond? Try Knowi’s unified analytics platform, connect data directly without ETL and analyze everything in one place.

Frequently Asked Questions