DynamoDB vs Amazon Keyspaces: Which Model Fits?

Choose DynamoDB when you want AWS-native item APIs, secondary indexes and a key-value/document model. Choose Amazon Keyspaces when Cassandra Query Language (CQL), Cassandra drivers and a wide-column data model fit the application or an existing migration. Both are managed serverless databases; the useful distinction is their API and data model, not a claim that only one can scale.

DynamoDB and Amazon Keyspaces: an overview

DynamoDB stores items identified by a partition key and an optional sort key. Items can carry different attributes and nested documents. Applications use AWS SDK calls such as GetItem, Query and UpdateItem, with indexes designed for additional access patterns.

Amazon Keyspaces is a managed Apache Cassandra-compatible database. Tables use a declared column schema, partition keys and clustering columns. Applications can use compatible Cassandra drivers and CQL without operating Cassandra nodes or maintaining the underlying cluster.

Keyspaces is useful for more than migration. A new service can choose it because its team, libraries and data model already fit CQL. Conversely, an existing Cassandra application still needs a compatibility review: the service does not implement every Cassandra operation or expose the same cluster administration model.

DynamoDB vs Keyspaces at a glance

Queries and Cassandra compatibility

CQL looks like SQL, but it does not make Keyspaces a relational database with arbitrary joins. Partition and clustering keys still determine efficient queries. Likewise, DynamoDB PartiQL provides SQL-compatible syntax over DynamoDB operations, not a relational optimizer for joining arbitrary tables.

The current Keyspaces API support matrix lists supported CQL operations. In particular, CREATE INDEX and DROP INDEX are not supported. If an application depends on a Cassandra secondary-index feature, redesign that access path or choose a different destination rather than assuming it migrates unchanged.

DynamoDB offers global secondary indexes for alternate access paths, but they add write and storage cost and have eventual read consistency. An index is useful when a real application query needs it; it is not a reason to copy every attribute into every index.

For a device-events table, either database can organize records by device and time. The design still needs a policy for very active devices and large time ranges. Bucketing by time can bound reads, while additional query-specific tables or indexes support other access patterns.

Performance and latency

Both services are designed for low-latency operational workloads. Neither guarantees that all requests complete at the same latency regardless of query size, hot keys or traffic bursts. Remove service-wide marketing throughput totals from capacity planning and measure the actual workload instead.

DynamoDB performance depends on item sizes, key distribution and whether queries use the intended index. DAX can help repeated eligible reads but does not fix a poor write distribution. The query guide explains why filters do not substitute for key conditions.

Keyspaces also requires sensible partition design and client configuration. Its functional differences guide explains connection handling, load balancing and pagination. Client connection limits can become a bottleneck even when the table has sufficient capacity. Follow the driver's Keyspaces configuration guidance rather than reproducing a self-managed Cassandra topology assumption.

Benchmark successful requests, throttled requests and retries together. A retry-heavy benchmark can appear to sustain throughput while hiding unacceptable tail latency or increased cost.

Consistency and transactions

Keyspaces supports compare-and-set conditions through lightweight transactions on INSERT, UPDATE and DELETE. These conditional operations are useful for optimistic concurrency, such as changing a status only if its previous value matches. They should not be described as equivalent to an arbitrary multi-table SQL transaction.

Keyspaces also supports logged batches that atomically apply writes across tables in the same account and Region. Unlogged batches can partially succeed. Logged batches consume twice the write capacity of ordinary writes, and their atomic behavior in the source Region does not imply isolated application in asynchronously replicated Regions. Include conditions, conflicts and retries in migration testing.

DynamoDB supports conditional writes and multi-item transactions across tables in one account and Region. The transaction APIs are limited to 100 unique items and 4 MB in aggregate, and are unavailable on MRSC global tables. Choose the atomic boundary needed by the application rather than assuming either database can transact over an unlimited dataset.

For reads, choose consistency deliberately. A cheaper or local read can have different freshness guarantees from the read that confirms an update. That distinction becomes especially important once the application spans Regions.

Availability and multi-Region replication

Both services replicate data across Availability Zones within a Region. Regional replication protects against infrastructure failures but does not replace backup for accidental changes.

Keyspaces multi-Region replication is active-active and asynchronous. Each Region can accept local reads and writes, while cell-level timestamps and last-writer-wins reconciliation resolve conflicting updates. A successful local write does not mean that a reader in another Region already sees it.

DynamoDB global tables offer multi-Region eventual consistency and multi-Region strong consistency. MRSC has different restrictions, including no transaction API support or TTL. Do not compare Keyspaces asynchronous replication with DynamoDB strong replication without accounting for those differences.

In either design, specify which Regions can write the same business record and how the application routes traffic during a regional incident. Infrastructure replication does not resolve conflicting business intentions by itself.

Security

Both services integrate with IAM and encrypt data at rest. DynamoDB offers AWS owned, AWS managed and customer managed key options. Keyspaces also protects data in transit and supports its documented encryption and authentication mechanisms.

The Keyspaces encryption guide describes its key choices. Do not copy DynamoDB's key-option list into a Keyspaces configuration: use the service's supported AWS owned or customer managed KMS key settings.

For a Cassandra migration, test driver authentication and TLS as part of the proof of concept. The application data-plane connection and permissions to create or modify tables are distinct operational concerns.

Scalability and limits

Both services offer on-demand and provisioned capacity. On-demand is useful for variable traffic; provisioned mode can fit predictable utilization. Service quotas and uneven traffic still apply, so test sudden peaks rather than treating serverless as unlimited instantaneous capacity.

DynamoDB items are limited to 400 KB. Keyspaces rows are limited to 1 MB, but the Keyspaces quotas include key, column, connection and throughput constraints as well. Row-size accounting includes service metadata, so the size of a serialized application object is not the final measure.

For the earlier team-based example, one very large, active team can dominate a partition-oriented access pattern. Consider whether requests can be split by a time bucket or another useful dimension. Keep the resulting read fan-out manageable and test the busiest tenant, not only an average one.

Backups and change data capture

DynamoDB supports on-demand backups and PITR with a configurable 1-to-35-day recovery window. Restoring creates a new table. Keyspaces supports point-in-time recovery for restoring table data within its recovery window. Enable and test recovery before relying on it; a backup setting is not retroactive protection for older changes.

Keyspaces now provides CDC streams for row-level changes, with up to 24 hours of retention. This supports event-driven processing and derived search or analytical views.

DynamoDB Streams provides item-level changes. For either source, consumers need retries, idempotency and monitoring for lag. An archived stream can complement backups, but a consumer that misses retained records needs a resynchronization strategy.

Pricing and choosing a service

DynamoDB pricing and Keyspaces pricing both distinguish request-based on-demand throughput from provisioned capacity. Storage, consistency choices, row/item sizes, replication and recovery influence the total. Keyspaces TTL and CDC have pricing dimensions that should not be assumed to match DynamoDB.

Compare a representative workload in the actual Region. Include idle capacity and the cost of maintaining duplicate query tables or indexes.

Choose DynamoDB when its APIs and indexes simplify the application. Choose Keyspaces when CQL, compatible clients and wide-column modeling do that job better. If different services use both, keep clear data ownership and asynchronous contracts rather than trying to maintain two interchangeable authoritative copies.