DynamoDB vs Google Cloud Spanner: Which should you choose?

Choose DynamoDB for key-value and document workloads with known access patterns and AWS-managed throughput scaling. Choose Google Cloud Spanner when you need relational SQL, joins, and distributed transactions with strong consistency across a configured database. Both are managed distributed databases; Spanner's global relational transaction model is the main distinction, not a claim that DynamoDB lacks transactions or strong reads.

DynamoDB and Google Cloud Spanner: an overview

DynamoDB

DynamoDB stores items in tables organized by partition and optional sort keys. It fits sessions, carts, device state, and similar workloads where requests can find data through a table or index key. AWS manages storage distribution, replication, and infrastructure maintenance.

You still own data modeling, permissions, capacity choices, retry behavior, and recovery. A service that scales its partitions does not automatically make an arbitrary scan efficient or eliminate hot keys.

Google Cloud Spanner

Spanner is Google's managed distributed database with a relational foundation. Its schema model includes tables, primary keys, secondary indexes, and relationships. It offers GoogleSQL and a PostgreSQL dialect; the latter is a compatibility surface, not a hosted instance of the standard PostgreSQL server.

A regional or multi-region configuration determines replica placement. Spanner is relevant when maintaining relationships and transaction guarantees across a distributed deployment would otherwise require application-level coordination.

DynamoDB vs Spanner at a glance

The transaction ceilings are documented in DynamoDB constraints and Spanner quotas. Spanner mutations are not equivalent to DynamoDB items: index maintenance can count toward a commit's work.

Shared attributes and meaningful differences

Both systems distribute data, replicate it, and support transactions. Both require deliberate key design and monitoring. Both can support substantial workloads, but neither promises a fixed latency for every operation regardless of query complexity or geography.

The practical difference appears when an access pattern changes. DynamoDB may require a new secondary index or denormalized read model. Spanner may express the request as a new SQL query, although its indexes, execution plan, and distributed data access still determine cost and latency. Start with your application's hardest transaction and most expensive query rather than a generic SQL-versus-NoSQL checklist.

Consistency and transactions

DynamoDB supports strongly consistent reads on tables and local secondary indexes; global secondary indexes remain eventually consistent. Its transaction APIs support atomic operations within one account and Region. MREC global tables replicate changes asynchronously and do not replicate a transaction as an atomic group. MRSC global tables support strong multi-Region reads but do not support transactions.

Spanner documents external consistency, which orders serializable transactions consistently with real time. This is useful when a reservation, account transfer, or inventory update must preserve an invariant across related records in a distributed database.

Check the selected isolation mode: Spanner's transaction documentation also describes repeatable-read isolation, which does not provide the same guarantees as the default serializable mode. Transaction retries can rerun application callbacks, so avoid irreversible side effects inside them.

For a global financial workflow, ask whether you need one transaction spanning related rows, or only a strongly consistent read of one record. Those are different requirements and can lead to different choices.

Performance

DynamoDB

DynamoDB is designed for low-latency key lookups and updates. A well-distributed workload can scale without an application-managed shard map. Latency still includes network distance, item size, retries, throttling, and any additional reads needed to assemble a response.

DAX can cache suitable read-heavy workloads, but it adds cost and operational considerations. A cache-hit latency should not be presented as a guarantee for every DynamoDB operation. Likewise, a service-wide traffic statistic is not the throughput of your table.

Google Cloud Spanner

Spanner distributes queries and transactions across its storage and compute resources. Performance depends on schema, indexes, locality, contention, compute capacity, and replica placement. A multi-region transaction can pay a coordination cost that a single-region key lookup does not.

Benchmark representative joins and transactions at the intended topology. Measure tail latency and abort/retry rates as well as throughput. There is no useful universal figure such as a fixed number of mixed reads and writes per node that applies to every schema and query.

Spanner editions, graph and vector search

Spanner's edition matrix separates three choices:

• Standard: regional deployments with the core relational capabilities.
• Enterprise: adds capabilities including Spanner Graph, full-text and vector search, managed autoscaling, and incremental backups.
• Enterprise Plus: includes Enterprise features and supports dual-region and multi-region configurations and geo-partitioning.

Spanner Graph adds a property-graph view and graph queries over data. That can help with connected-data use cases such as relationship exploration without making every application adopt a separate graph database.

Spanner also documents approximate nearest-neighbor vector search. Verify SQL dialect and feature support for the exact design. DynamoDB now supports native vector indexes on on-demand tables too, so the relevant comparison is query composition, filtering, recall, and operational fit, not simply whether a vector feature exists.

Availability and durability

DynamoDB replicates across Availability Zones within a Region. Global tables add regional replicas with a chosen consistency mode. Application routing, failover procedures, and regional dependencies still matter.

Spanner's replica placement follows its instance configuration. A regional deployment and a multi-region deployment are different failure-domain choices, even though both use replication. Its edition matrix lists 99.99% availability SLAs for Standard and Enterprise and up to 99.999% for Enterprise Plus, subject to configuration and service terms.

Replicas protect against infrastructure failures. They can also replicate an erroneous update, so use backups and recovery procedures for logical corruption. Do not assume that a database SLA covers the availability of your entire application.

Security

DynamoDB uses encryption at rest, IAM authorization, and configurable key management. Resource-based policies can grant scoped access to tables and streams. VPC endpoints provide private connectivity to the service; they do not create a private database server in your subnet.

Spanner encrypts stored data by default and supports customer-managed keys and VPC Service Controls, as described in its security overview.

Spanner uses Google Cloud IAM for access control. Define service identities and scoped roles for applications and administration instead of sharing broad project permissions.

Spanner audit logging distinguishes administrative and data-access events. Configure the log categories, retention, and access required by your organization. Authentication identifies the caller; authorization determines what that caller can do. An audit facility is not proof that every desired event is enabled or retained by default.

Backups

DynamoDB supports on-demand backups and PITR with a configurable retention window of 1 to 35 days. AWS Backup can add centrally managed backup policies. A restore creates a new table, so recovery includes reconnecting consumers and checking settings. The DynamoDB backup guide covers these steps.

Spanner supports backups, scheduled backups, and edition-dependent incremental backups. Its PITR documentation describes version retention from one hour to seven days. Full-database recovery uses a backup or export at a past timestamp followed by restoration or import into a new database.

Choose recovery windows from business requirements, then test the complete restoration path. Neither service's replication should be your only protection against an application bug.

Scalability

DynamoDB on-demand mode adjusts throughput automatically, while provisioned mode allocates read and write capacity that can be managed with auto scaling. Warm throughput and quotas determine what a sudden spike can immediately consume. Partition-key distribution remains important in either mode.

Spanner compute is expressed in processing units or nodes. Its autoscaling options include a managed autoscaler and an open-source autoscaler, with different operational responsibilities and edition eligibility. Configure targets and capacity boundaries; do not assume that autoscaling reacts instantaneously or repairs a poor schema.

In both systems, test a skewed workload as well as a uniform benchmark. One frequently updated account or a concentrated write range can behave differently from evenly distributed synthetic traffic.

Pricing

DynamoDB pricing separates on-demand requests from provisioned capacity. Storage, secondary indexes, replicated writes, backup retention, and optional features contribute to the bill. Variable workloads can benefit from request-based billing; provisioned capacity can suit steady utilization. Use the pricing calculator with realistic item sizes and read consistency.

Spanner pricing depends on edition, region/configuration, compute capacity, database storage, backup storage, and network usage. Granular instances allow less than one node of compute, but provisioned compute still has a baseline cost. Optional features such as Data Boost have their own billing dimensions.

Compare the complete topology and workload. A single-region DynamoDB estimate and a multi-region Spanner estimate do not buy equivalent failure tolerance or transaction semantics. Include cross-cloud transfer and processing if the application lives outside the database's cloud.

Use cases and choosing between them

DynamoDB fits high-throughput sessions, carts, IoT state, game state, and content metadata with predictable key-based reads. It can serve a small serverless application too; there is no minimum daily request count that makes it appropriate. Some leaderboards and content queries require explicit secondary-index or aggregate designs.

Spanner fits distributed order management, financial records, inventory, and supply-chain applications whose relational transactions must remain consistent across a configured database. It is particularly relevant when a conventional relational deployment would otherwise need complicated application-managed sharding.

Use both only when each has a clear responsibility. For example, an organization might keep transactional records in Spanner and serve AWS-local session state or derived data in DynamoDB. This is an architectural option, not a built-in atomic integration: plan event delivery, idempotency, reconciliation, cross-cloud latency, and transfer cost. Prefer one source of truth for each business entity.