An in-depth comparison of storage and database services across GCP and AWS. Covers object storage, data warehouses, relational databases, NoSQL, and caching, with the latest 2026 pricing and a complete service matrix.
| Category | GCP | AWS |
|---|---|---|
| Object storage | Cloud Storage | S3 |
| Block storage | Persistent Disk / Hyperdisk | EBS |
| File system | Filestore | EFS / FSx |
| RDB (MySQL/PG/MSSQL) | Cloud SQL | RDS |
| RDB (high-performance PG-compatible) | AlloyDB | Aurora |
| Global NewSQL | Spanner | Aurora DSQL (2024) |
| DWH | BigQuery | Redshift |
| NoSQL KV/Doc | Firestore / Bigtable | DynamoDB |
| Wide-column NoSQL | Bigtable | Keyspaces (Cassandra) |
| Cache | Memorystore | ElastiCache |
| Graph DB | Spanner Graph | Neptune |
| Time-series DB | Bigtable | Timestream |
| Item | Cloud Storage | S3 |
|---|---|---|
| Price / GB / month | $0.020 | $0.023 |
| Durability | 99.999999999% (11 nines) | 99.999999999% |
| Consistency | Strong (from day one) | Strong (since 2020) |
| Classes | 4 (Std/Nearline/Coldline/Archive) | 6 (Std/IA/Onezone/Glacier/Deep Archive) |
| Free tier | 5 GB | 5 GB (12 months) |
| Item | BigQuery | Redshift |
|---|---|---|
| Architecture | Serverless | Cluster-managed |
| Pricing unit | Scan volume $6.25/TB or Slot | Cluster hours |
| Serverless | Yes | Redshift Serverless |
| ML integration | BigQuery ML + Gemini | Redshift ML |
| Multi-cloud | BigQuery Omni | — |
| Lakehouse | BigLake | Lake Formation + Athena |
| Item | Cloud SQL | RDS | AlloyDB | Aurora |
|---|---|---|---|---|
| Supported engines | MySQL/PG/MSSQL | MySQL/PG/MSSQL/Oracle/MariaDB | PG-compatible | MySQL/PG-compatible |
| HA SLA | 99.95% | 99.95% | 99.99% | 99.99% |
| Performance | Standard | Standard | 4x PG, 100x analytics | 3x PG |
| ML integration | — | — | AlloyDB AI | — |
| Item | Spanner | Aurora DSQL |
|---|---|---|
| Release | 2017 (in use at Google since 2007) | 2024 GA |
| Consistency | External Consistency | Serializable |
| SQL | GoogleSQL / PostgreSQL | PostgreSQL |
| SLA (multi-region) | 99.999% | 99.99% |
| Track record | Google AdWords / Gmail / YouTube | New |
| Item | Firestore | Bigtable | DynamoDB |
|---|---|---|---|
| Model | Document | Wide-column | KV + Document |
| Primary use case | Mobile / Web | Time-series / IoT / analytics | Serverless APIs |
| Real-time sync | Yes | — | Streams |
| Scaling | Automatic | Add nodes | On-demand / provisioned |
| Pricing unit | Operation count | Node hours | RCU/WCU |
| Item | Memorystore | ElastiCache |
|---|---|---|
| Redis | Yes | Yes |
| Memcached | Yes | Yes |
| Valkey | Yes (2024) | Yes (2024) |
| Serverless | — | ElastiCache Serverless |
| SLA | 99.9-99.99% | 99.9% |
Which is cheaper, Cloud Storage or S3?
They are roughly on par. Standard-class GCS is $0.02/GB and S3 is $0.023/GB, so GCS is marginally cheaper. Egress is also slightly more favorable on GCS.
Should I use BigQuery or Redshift?
Pick BigQuery if you're in the GCP ecosystem (serverless + ML integration), and Redshift if you're in the AWS ecosystem. Both are leading DWH analytics products with very similar capabilities.
How do I choose between Spanner and DynamoDB?
Spanner = SQL + ACID + global. DynamoDB = NoSQL + serverless + AWS integration. Choose Spanner when you need transactions, and DynamoDB for massive scale with a simple KV model.
Which is easier to use, Cloud SQL or RDS?
Both are managed MySQL / PostgreSQL / SQL Server offerings. RDS is powerful when paired with Aurora, while Cloud SQL has the unique strength of AlloyDB alongside it.
What is the difference between Firestore and DynamoDB?
Firestore = document NoSQL with real-time sync. DynamoDB = KV with strong GSI support. Firestore is common for mobile/web, while DynamoDB is the go-to for serverless APIs.
What is the difference between Bigtable and DynamoDB?
Bigtable = wide-column, HBase-compatible. DynamoDB = KV + Document. Bigtable suits time-series, IoT, and analytics; DynamoDB suits general-purpose KV workloads.
How do Cloud Spanner and Aurora DSQL compare?
Aurora DSQL (GA in 2024) is Spanner-like. Spanner has a 10+ year track record, while DSQL is new. Spanner is often chosen for its proven track record and SLA.
How do AlloyDB and Aurora compare?
Both are high-performance PostgreSQL-compatible databases. AlloyDB delivers up to 100x faster analytical queries plus ML integration (AlloyDB AI), while Aurora's strength is its deep AWS integration.
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Google Cloud and AWS are trademarks of their respective owners. Please refer to each provider's official site for the latest pricing.
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