Amazon DynamoDB vs Google Cloud Spanner

Data model and query power

Amazon DynamoDB is a key-value and document database. Developers design tables around the queries the application will run, often using single-table design with partition and sort keys, secondary indexes, and the PartiQL query layer. This produces excellent performance for known access patterns but does not support relational joins or ad-hoc analytical queries the way a SQL engine does.

Google Cloud Spanner is relational. It supports SQL, joins, secondary indexes, and foreign keys while distributing data across nodes. For teams whose applications rely on relational integrity and flexible querying, Spanner removes the modelling constraints that DynamoDB imposes, at the cost of a heavier and more expensive engine.

Scaling and consistency

DynamoDB scales effectively without bound for workloads that fit its model, delivering consistent single-digit-millisecond latency. Global Tables provide multi-region, multi-active replication, though that model is eventually consistent across regions rather than synchronously consistent. DynamoDB suits shopping carts, session stores, event ingestion, and similar patterns where throughput matters more than relational querying.

Spanner scales writes horizontally while maintaining external consistency through TrueTime, and it offers a 99.999 percent availability commitment for multi-region instances. Where an application needs strong consistency and relational semantics across regions at the same time, Spanner addresses a requirement DynamoDB does not, since DynamoDB trades relational features for raw scale and operational simplicity.

Pricing

DynamoDB bills by throughput and storage. On-demand mode charges per read and write request, which is cost-effective for spiky or unpredictable traffic, while provisioned capacity with auto-scaling lowers cost for steady high-volume workloads. Storage and optional features such as global tables and backups add to the bill. At low to moderate scale DynamoDB is inexpensive and requires no capacity planning.

Spanner bills by compute capacity in nodes or processing units, plus storage and egress, with multi-region configurations costing more. Its minimum spend is higher than a small DynamoDB workload, so DynamoDB usually wins on cost for simple high-throughput access patterns, while Spanner earns its cost when relational power and global consistency are both required.

Operations and fit

Both are fully managed and remove most administration. DynamoDB is effectively serverless, so there is no capacity to provision in on-demand mode and scaling is automatic, which appeals to teams that want minimal database operations. Spanner is managed but expects relational schema design, query tuning, and capacity decisions expressed in nodes. The clearest way to choose is to examine the workload: if access patterns are simple and throughput is the priority, DynamoDB fits; if the application is relational and must scale globally with strong consistency, Spanner fits. They are frequently used for different tiers of the same architecture rather than as direct substitutes.

User-sentiment summary

Buyers frequently report that Amazon DynamoDB delivers predictable latency at enormous scale with almost no operational overhead, and that on-demand pricing makes it easy to start, while cautioning that the access-pattern-first data model is unforgiving and that costs and complexity rise for query patterns it was not designed around. Reviewers of Google Cloud Spanner praise the combination of relational SQL with horizontal scaling and strong consistency, describing it as a fit for systems that outgrew single-node databases, but they note a higher cost floor and a smaller ecosystem than mainstream relational engines. Across both, evaluators stress that the data model, not raw performance, should drive the choice, and that the two often serve different layers of the same system.

Recommendation

Choose Amazon DynamoDB when your access patterns are well understood, throughput and latency are the priorities, and you want a serverless store that scales without capacity planning. Choose Google Cloud Spanner when the application is relational, needs joins and SQL, and must scale globally with strong consistency. Teams building event ingestion, session, or cart stores usually reach for DynamoDB, while teams running transactional systems of record that must grow beyond a single node usually reach for Spanner. Many architectures use both at different layers.

Alternatives to both

Related comparisons

Continue your research with related independent comparisons: MongoDB vs DynamoDB, DynamoDB vs Cassandra. For the full category overview, see Database Management.