Google Cloud

Spanner vs DynamoDB vs Cosmos DB: Global NoSQL / NewSQL Comparison

2026-05-24
NicheeLab Editorial Team

The four major options for a globally distributed database — Spanner (GCP), DynamoDB (AWS), Cosmos DB (Azure), and Aurora DSQL (AWS, GA in 2024) — compared head to head. As the line between NewSQL and NoSQL keeps blurring, this guide lays out how to pick the right one for each workload.

Headline Comparison

ItemSpannerDynamoDBCosmos DBAurora DSQL
ModelNewSQL (SQL)NoSQL KV/DocMulti-modelNewSQL (PG-compatible)
SQLGoogleSQL / PostgreSQLPartiQLSQL API + 4 othersPostgreSQL
ConsistencyExternal ConsistencyEventual / Strong5 levels (Strong / BS / Session / CP / Eventual)Serializable
ACIDExcellent (global)Excellent (per table)Excellent (per container)Excellent (per region)
Multi-regionExcellent (5 continents)Global TablesExcellent (60+ regions)3 regions
SLA99.999%99.999% (Global Tables)99.999%99.99%
Billing unitProcessing UnitRCU/WCU or on-demandRU/secvCPU-hours
ServerlessExcellent (On-demand)Excellent (Serverless)Excellent
Track record10+ years (Google internal)10+ years10+ years2024-

Consistency Level Comparison

LevelSpannerDynamoDBCosmos DB
Strong / LinearizableExcellent (default)Yes (per request)Yes
Bounded StalenessYes
SessionYes (default)
Consistent PrefixYes
EventualExcellent (default, cheapest)Yes

Pricing Example (1,000 reads/sec + 1,000 writes/sec + 100 GB)

ServiceApprox. monthly cost
Spanner (1000 PU = 1 node)~$200 (regional)
DynamoDB On-demand~$400
DynamoDB Provisioned~$300
Cosmos DB Provisioned (1000 RU/s)~$60 + data transfer
Cosmos DB Serverless~$280

Key Feature Comparison

FeatureSpannerDynamoDBCosmos DB
Secondary indexesExcellentGSI / LSIAuto + Composite
Stream / CDCChange StreamsStreamsChange Feed
PITR1 hour to 7 days35 days30 days
Time Travel
Vector SearchExcellent (since 2024)
GraphSpanner Graph (2024)Cosmos for Apache Gremlin

Recommendations by Use Case

Use caseRecommendedWhy
Financial transactions / bankingSpannerStrong consistency + global + ACID
Serverless web / APIDynamoDBOn-demand + simple KV model
Mobile + web, multi-cloudCosmos DBMulti-model + 60+ regions
E-commerce order managementSpannerTransactions + global
IoT event logsDynamoDBServerless + high write throughput
AWS PG-compatible + globalAurora DSQLNewly integrated into AWS
Knowledge graphSpanner Graph / Cosmos GremlinProperty Graph
Vector Search + documentCosmos DB (2024)Native vector support

How do Spanner, DynamoDB, and Cosmos DB differ?

Spanner = NewSQL (SQL + ACID + global distribution), DynamoDB = serverless NoSQL KV/Document, Cosmos DB = multi-model NoSQL + SQL API. They are completely different beasts.

Is Aurora DSQL in the same category?

Yes (GA in 2024). It's AWS's Spanner-like NewSQL. The track record is still short, but it's already integrated into the Aurora ecosystem, bridging Spanner-style features into AWS.

Which offers strong consistency in a global setup?

Spanner = External Consistency (TrueTime-based, the strongest in the industry); Cosmos DB = 5 consistency levels (Strong, Bounded Staleness, etc.); DynamoDB = Eventual + Strong (within a single region).

What do the pricing models look like?

Spanner = Processing Units (100 PU = $0.90/h) + storage; DynamoDB = RCU/WCU or on-demand; Cosmos = RU/sec (Provisioned or Serverless).

How serverless are they?

DynamoDB On-demand bills only per request; Cosmos DB Serverless bills only per RU consumed; Spanner starts at a minimum 100 PU (~$70/month). DynamoDB is the most serverless of the three.

Can they handle multi-region writes?

Spanner: in multi-region setups every region can accept writes. Cosmos: native multi-region writes. DynamoDB: handled via Global Tables (last-writer-wins).

Which one for which workload?

Financial transactions → Spanner; serverless web/API → DynamoDB; multi-cloud + multi-model → Cosmos DB; AWS Aurora compatibility → DSQL.

What does a cost comparison look like?

For 1,000 reads/sec + 1,000 writes/sec + 100 GB of data: Spanner ~$200/month, DynamoDB On-demand ~$400/month, Cosmos Provisioned ~$300/month. The ranking can flip depending on the workload.

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NicheeLab Editorial Team

NicheeLab editorial team focused on data engineering and cloud certification learning. Content is structured around practical study needs and official exam domains.


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