Independent comparison for enterprise buyers. Updated May 2026.
Quick verdict: Choose Amazon DynamoDB when the workload runs on AWS, predictable single-digit-millisecond latency at any scale is essential, and the team prefers a fully managed serverless data store over operating clusters. Choose Apache Cassandra when multi-cloud or on-premise portability matters, when workloads require multi-data-centre active-active writes with tunable consistency, or when open-source licensing avoids cloud lock-in. The key differentiator is operating model: DynamoDB is a managed AWS service; Cassandra is portable infrastructure with substantial operational depth.
| Criteria | Amazon DynamoDB | Apache Cassandra |
|---|---|---|
| Editorial score | 4.4 / 5.0 | 4.2 / 5.0 |
| Deployment | AWS-only managed service; DAX in-memory accelerator | Self-managed, DataStax Astra DB, Amazon Keyspaces (Cassandra API) |
| Pricing Model | On-demand or provisioned capacity; storage and replication per region | Open source; commercial via DataStax or AWS Keyspaces pricing |
| Target Buyer | AWS-native applications, serverless backends, predictable latency at scale | Multi-cloud and on-premise workloads, write-heavy at extreme scale, multi-DC |
| Implementation | Approximately 2–6 weeks for a typical workload | Approximately 3–9 months self-managed; 1–3 months on Astra DB |
| Customisation | Partition key and sort key design; Global Secondary Indexes | CQL, wide-row data model, tunable consistency per query |
| Ecosystem | Deep AWS integration: Lambda, AppSync, Kinesis, Streams, IAM | DataStax, Apache community, broad open-source tooling |
| Key Strength | Zero operations, predictable latency, AWS integration depth | Multi-cloud portability, multi-DC active-active, linear write scale |
DynamoDB is a fully managed NoSQL key-value and document store native to AWS. The service is genuinely serverless: capacity is provisioned by table either as on-demand (pay-per-request) or provisioned (reserved capacity with auto-scaling). Single-digit-millisecond latency at any scale is a service-level commitment, achieved through automatic partitioning across SSD-backed nodes invisible to the user. Global Tables provide multi-region active-active replication with eventual consistency. Streams expose change data capture to Lambda, Kinesis, or external pipelines. DynamoDB Accelerator (DAX) adds an in-memory cache for read-heavy workloads.
Apache Cassandra is an open-source wide-column store designed for write-heavy workloads at extreme scale. The architecture is masterless: every node is equal, data partitions across nodes via consistent hashing, and replication is configurable per keyspace with tunable consistency per query. Cassandra excels at workloads writing millions of events per second across multiple data centres with active-active replication. Production deployments at Netflix, Apple, Uber, and Discord run thousands of nodes serving petabytes.
The data model is similar in principle: both reward careful primary key design and discourage ad-hoc joins. DynamoDB uses partition key plus optional sort key with up to 20 Global Secondary Indexes per table. Cassandra uses partition key plus clustering columns with secondary indexes available but generally discouraged for performance reasons. Both require thinking about query patterns at design time.
Consistency models differ. DynamoDB offers eventually consistent reads by default with optional strongly consistent reads at higher cost. Cassandra offers tunable consistency from ONE to ALL on a per-query basis. Neither supports multi-row ACID transactions natively in the way relational databases do; DynamoDB supports up to 100 items per transaction with full ACID semantics, and Cassandra provides lightweight transactions via Paxos with significant performance cost.
For operational characteristics, DynamoDB removes essentially all operational burden in exchange for AWS lock-in. Cassandra demands operational expertise: repair scheduling, compaction tuning, node replacement, cross-DC consistency, and JVM tuning all require specialist skills. DataStax Astra DB and Amazon Keyspaces provide managed Cassandra-compatible services that narrow the operational gap considerably.
DynamoDB on-demand prices at approximately $1.25 per million writes and $0.25 per million reads (US regions, eventually consistent) with storage at $0.25 per GB-month and replicated writes for Global Tables charged per region. Provisioned capacity prices lower per request but requires capacity planning. Apache Cassandra itself is free under the Apache 2.0 licence. DataStax Enterprise lists at approximately $5,000–$15,000 per node per year depending on support tier; DataStax Astra DB prices by read/write units and storage similar to DynamoDB. Amazon Keyspaces prices nearly identically to DynamoDB on-demand.
Five-year cost of ownership for a 50,000 write-per-second steady workload: DynamoDB on-demand $4M–$10M, DynamoDB provisioned $2M–$5M, self-managed Cassandra $1M–$3M (largely staffing and infrastructure), DataStax Astra DB $2M–$5M. The primary buying-side caveat for DynamoDB is request charges at sustained high throughput, where provisioned capacity is essential to control cost — many teams discover this after on-demand bills arrive. For self-managed Cassandra, the caveat is staffing: production Cassandra requires deep specialist expertise that many enterprises underestimate, frequently leading to migration to managed offerings after operational incidents. Pricing as of May 2026.
Choose DynamoDB when the application runs in AWS and predictable single-digit-millisecond latency at any scale is a hard requirement, when the team prefers serverless data without operating clusters, when integration with Lambda, AppSync, and Kinesis is valuable, when workloads have well-understood key access patterns, or when small teams need to ship without dedicating headcount to database operations. DynamoDB suits gaming leaderboards, session stores, IoT ingestion on AWS, serverless application backends, and microservices that can model queries around partition keys.
Choose Apache Cassandra for write-heavy workloads at extreme scale where linear write throughput matters, for time-series and telemetry workloads where wide-row modelling fits naturally, for multi-cloud or on-premise deployment where AWS lock-in is unacceptable, for multi-data-centre active-active deployments with tunable consistency, or when the organisation has Cassandra operational expertise already. Self-managed Cassandra requires specialist skills; DataStax Astra DB or Amazon Keyspaces provide managed Cassandra-compatible alternatives that reduce operational burden while retaining the data model.
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