DynamoDB vs Cassandra: Differences and Tradeoffs

Choose DynamoDB when you want AWS to operate a durable, scalable database and your application's access patterns fit keys and indexes. Choose Apache Cassandra when deployment control, CQL, tunable consistency, and a distributed wide-column model are priorities. Cassandra can run across your own infrastructure or a managed provider; Amazon Keyspaces offers a managed Cassandra-compatible option on AWS with its own compatibility boundaries.

DynamoDB and Apache Cassandra: an overview

DynamoDB is a managed key-value and document database. You create tables, configure capacity and recovery, and use AWS APIs. AWS manages the underlying servers and partitions. You cannot tune its storage engine or install the production service on your own cluster.

Apache Cassandra is an open-source distributed database, not inherently a cloud service. Its peer-to-peer architecture distributes data across replicas, and an application chooses consistency levels for operations. With self-managed Cassandra, your team controls deployment, upgrades, repair, compaction, capacity, and recovery.

Comparison at a glance

Shared attributes for DynamoDB and Cassandra

Both distribute data using partition keys, and both reward query-driven modeling. You usually design tables around the reads the application needs, rather than expecting arbitrary joins to remain cheap at scale. Denormalization is common in both systems.

A partition key selects a logical group of data. DynamoDB's optional sort key orders items within that group; Cassandra's clustering columns order rows within a Cassandra partition. That similarity helps when moving between the products, but their physical partition management is different.

Neither is a relational database just because it has tables. Cassandra CQL looks like SQL, and DynamoDB PartiQL provides SQL-compatible syntax, but syntax does not add relational joins or remove the need to understand the access path.

Data model: DynamoDB vs Cassandra

DynamoDB items can have different non-key attributes, including nested maps and lists. A base-table key has one partition-key attribute and an optional sort-key attribute. Current global secondary indexes also support multi-attribute keys, so the old claim that every DynamoDB index key must use one attribute is outdated.

Cassandra's CQL schema declares column types. Partition keys and clustering keys can contain multiple columns. Rows need not populate every column, but that does not make Cassandra schema-free. Its type system and key definitions remain part of the table contract.

For a device-event workload, a possible design groups records by device and time bucket, then orders them by event time. A bucket limits the growth of one logical partition. Pick the bucket using the actual event rate and query window; a busy device and an occasional sensor need different treatment.

A DynamoDB physical partition is a service-managed storage unit, not the same thing as a Cassandra logical partition. Avoid transferring a Cassandra partition-size recommendation directly into a DynamoDB design. DynamoDB's hard item-size constraint is 400 KB, including attribute names and values. Store larger payloads separately or split them into items with explicit retrieval rules.

Performance and indexing

DynamoDB

DynamoDB is designed for predictable key-based access. Table size is less useful than access distribution when diagnosing performance: one busy key or an overloaded index can throttle a table that otherwise has spare capacity. Use DynamoDB Query for a known key range; a Scan reads through data even when a filter discards most results.

DAX can cache compatible reads, but it adds another service and its own consistency considerations. Measure whether a cache helps the application's actual read pattern before treating it as a default requirement.

Cassandra

Cassandra's write path and distributed architecture suit sustained writes, provided partitions and replica placement fit the workload. Read latency also depends on compaction, tombstones, consistency level, storage, and how many partitions a query touches. “Cassandra is faster” is not a useful conclusion without those settings.

The older blanket criticism of Cassandra indexing misses Storage-Attached Indexing (SAI), introduced in Cassandra 5.0. SAI supports indexed filtering on supported column types. Cassandra 5.0 also has a vector type and vector similarity search. The official download page lists 5.0 as the latest GA line at this article's October 2026 update.

Indexes broaden the available queries; they do not make every query equally efficient. Test selectivity, result limits, and resource usage on realistic data. DynamoDB also has native vector search on supported on-demand tables, so vector capability alone is no longer a deciding difference.

Consistency and transactions

DynamoDB offers strongly consistent reads on tables and local secondary indexes; global secondary indexes use eventual consistency. Regional transactions can update multiple items atomically. For cross-Region data, choose global tables in MREC or MRSC mode. MREC transactions are not replicated as one atomic unit, and MRSC does not support transaction APIs.

Cassandra's replication and consistency model lets you trade latency and failure tolerance against the number of replica acknowledgments required. For example, local quorum settings focus coordination within a datacenter. Configure the replication factor, read consistency, and write consistency together; a label such as “eventually consistent database” hides those choices.

Cassandra lightweight transactions and batches have specific semantics. Conditional writes use coordination to protect an update. Logged batches ensure their mutations are eventually completed, but do not provide general cross-partition transaction isolation. Do not translate an application transaction into a large batch without checking those guarantees.

Security

DynamoDB uses IAM and resource-based policies for authorization, and encryption at rest is always enabled. AWS-owned keys are the default; AWS-managed and customer-managed KMS keys offer different control and billing arrangements. VPC endpoints can restrict the network path. See the DynamoDB security guide for application-facing controls.

Cassandra provides configurable authentication, role-based authorization, and client-to-node and inter-node TLS. Its security documentation describes these as deployment settings. Do not assume they are enabled just because Cassandra supports them, or describe its standard table permissions as row-level authorization. Verify disk encryption and backup protection in the infrastructure or managed service you select.

Scalability, availability, and durability

DynamoDB automatically manages storage partitions. On-demand capacity suits variable demand; provisioned capacity gives explicit throughput settings and supports auto scaling. Both still require good keys and appropriate quotas. There is no basis for promising that scaling never affects latency or that a bad access pattern has unlimited throughput.

Cassandra scales by adding capacity and distributing replicas across nodes and datacenters. Its decentralized design avoids a single database leader for ordinary operations. Capacity planning must leave room for repairs, compaction, node replacement, and data movement, not only normal traffic.

Replication in either product is different from backup. A replicated accidental delete is still a delete. Configure recovery and rehearse it. With Cassandra, monitor repair and tombstone behavior as part of durability operations; with DynamoDB, verify point-in-time recovery and the application changes needed to use a restored table.

Time to Live

DynamoDB TTL uses an item attribute containing Unix epoch seconds. Deletion is asynchronous, typically occurring within a few days, and expired records can still appear in reads until removed. The application must enforce expiration when correctness depends on it.

Cassandra CQL accepts a TTL duration in seconds for written values and supports a table default TTL. Expiration produces tombstones that storage maintenance later removes. This is a different model from waiting for DynamoDB to delete a whole expired item. Include expiration and deletion volume in Cassandra's compaction and repair planning.

When to use each database

DynamoDB fits account state, application records, game state, and device data when known key-based queries and AWS integration matter more than storage-engine control. Cassandra fits workloads that need CQL, deployment flexibility, or a distributed operational model the team already knows how to run.

Both can support IoT, messaging, personalization, and event-oriented systems. Those industry labels do not select a database by themselves. Compare the actual query list, failure recovery, regional writes, and staffing requirements.

Running both can make sense when different services already own different datasets. Define one source of truth for each record and exchange events or derived views. Introducing two authoritative copies of the same dataset adds conflict handling and reconciliation work; it is not a free availability upgrade.

Pricing and Amazon Keyspaces

DynamoDB pricing charges for requests or provisioned capacity, storage, and optional features. On-demand unit prices do not dynamically rise and fall with traffic; your bill changes with consumed units. Item size, indexes, consistency, and replication affect that usage. Compare capacity choices with the DynamoDB pricing calculator.

Apache Cassandra has no database subscription fee, but running it costs compute, storage, networking, backup capacity, and engineering time. Managed Cassandra vendors set their own prices. There is no universal Cassandra “Developer” or “Production” per-node tariff, so those old price tiers do not belong in a product-wide comparison.

Amazon Keyspaces pricing offers on-demand and provisioned modes plus storage and other applicable charges. Keyspaces removes node and compaction management while retaining a Cassandra-compatible interface. Read its functional differences before migrating; support for a Cassandra driver does not imply support for every upstream feature, index, or administrative operation.

Community and ecosystem

DynamoDB integrates directly with AWS identity, event processing, monitoring, and infrastructure tooling. Cassandra offers Apache project governance, CQL tooling, drivers, and multiple deployment providers. Choose the ecosystem that reduces the work your team must own, including upgrades and incident response, rather than choosing from customer-logo lists.