Enterprise Cloud Storage Showdown: AWS vs Azure vs GCP (High-Level Comparison)
Get a high-level comparison of AWS S3, Google Cloud Storage, and Azure Blob Storage. Key metrics, strengths, and decision guidance for enterprise leaders evaluating multi-cloud storage options (Part 1 of 3).
This is Part 1 of a 3-part series comparing Enterprise Multi-Cloud Object Storage. Continue to Part 2: Performance, Pricing, and Operations.
Executive Summary
This report provides an executive-level comparison of three leading cloud object storage platforms for enterprises:
- Amazon Web Services S3 (Standard) – AWS’s flagship object storage service
- Microsoft Azure Blob Storage (Hot tier) – Azure’s general-purpose object storage
- Google Cloud Storage (Standard class) – Google Cloud’s object storage offering
We evaluate their performance, cost structure, and operational characteristics in a multi-cloud context. The goal is to help CTOs, CIOs, and infrastructure leaders (with $100K+ storage budgets) make informed decisions on multi-cloud object storage strategies.
💡 Key Decision Factors: Read/write performance, data durability, pricing (including hidden costs), integration with analytics/ML and CDN services, operational factors like scalability, availability SLAs, and total cost of ownership (TCO).
At-a-Glance Provider Strengths
| Provider | Core Strengths | Ideal For |
|---|---|---|
| AWS S3 | Maturity, ecosystem depth, proven reliability | Organizations heavily invested in AWS; workloads requiring fine-grained control |
| Google Cloud Storage | Performance, simplicity, global accessibility | Big data/ML workloads; multi-regional deployments; operational simplicity |
| Azure Blob Storage | Enterprise integration, cost flexibility, Microsoft ecosystem | Organizations with Microsoft footprint; budget-conscious enterprises able to commit |
Performance & Cost Metrics
| Metric | AWS S3 (Standard) | Google Cloud Storage (Standard) | Azure Blob Storage (Hot Tier) |
|---|---|---|---|
| Base storage cost (per GB-month, US East) | ~$0.023/GB (first 50 TB) Tiered volume discounts beyond | ~$0.020–$0.026/GB No volume discounts (flat rate by region) | $0.0184/GB (LRS*) $0.023/GB for ZRS (multi-AZ) |
| API GET request cost (per 1M) | $0.40 ($0.0004 per 1k GET requests) | ~$5.00 ($0.05 per 1k Class A ops – higher API cost) | $0.40 ($0.0004 per 1k read ops, similar to AWS) |
| Data egress to internet (per GB, US East) | ~$0.09/GB (after 100GB/mo free) Tiered down to ~$0.05 at PB scale | ~$0.12/GB (after 100GB/mo free) Tiered down with large volumes | ~$0.087/GB (after 100GB free) Tiered down to ~$0.07 at >50 TB |
| Data durability (annual) | 11 nines (99.999999999%) Multi-AZ redundancy by default | 11 nines (99.999999999%) Multi-zone redundancy in region | 11 nines (99.999999999%) LRS=single site; ZRS=multi-AZ |
| Availability SLA (regional) | 99.9% SLA (Standard) Designed for 99.99% uptime | 99.9% (regional) / 99.95% (multi-regional) SLO varies by class | 99.9% SLA (hot LRS) 99.99% read SLA with RA-GRS (dual-region) |
*LRS = Locally Redundant Storage (3 copies in one region, but potentially in one data center); ZRS = Zone-Redundant (3 copies across multiple AZs in region). GCS Standard is regional by default (multi-zone within region); a Multi-Regional class stores across multiple regions.
Operational Considerations Summary
| Aspect | AWS S3 (Standard) | Google Cloud Storage (Standard) | Azure Blob Storage (Hot Tier) |
|---|---|---|---|
| Setup & Ecosystem | Straightforward bucket creation (global namespace). Most mature ecosystem – many tools/apps natively support S3 API. Strong IAM policy model (AWS IAM & bucket policies). | Bucket creation with global naming (DNS). Dual APIs (JSON and S3-compatible XML) – broad tool support but slightly less than S3. Unified Google IAM controls for access. | Create via storage account (must be unique name). Uses Azure AD/RBAC or SAS tokens for access. Windows/Azure integration (Active Directory, Azure Portal) beneficial for MS-centric environments. |
| Scalability & Performance | Auto-scales with workload: ≥3,500 writes/sec & 5,500 reads/sec per prefix (no hard limit on prefixes). Low latency (~100ms first-byte in-region) for small objects. Strong consistency for reads/listings since 2020. | Initial cap ~1k writes & 5k reads/sec per bucket (auto-scales in minutes as load grows). Excellent throughput for large streams (high single-flow MB/s), but median latency can be higher without CDN. Strong consistency (immediate read-after-write, consistent listings). | 20,000 requests/sec per storage account (soft limit) – scale further by using multiple accounts. High throughput with Azure’s network (400 Gbps+ possible per VM scale-set). Strong consistency (writes replicated to 3 copies synchronously). |
| Managed integration | Deep integration with AWS services: e.g. Athena and Redshift Spectrum query data on S3, Event triggers via S3 -> Lambda. Dozens of native integrations (Backup, Big Data, ML, CDN CloudFront). | Integrated with GCP services: BigQuery can directly load from GCS, DataProc and Spark use GCS for data lakes. Cloud Functions and EventArc can trigger from bucket events. Strong synergy if analytics stack is on Google Cloud. | Tight integration in Azure ecosystem: Azure Data Lake Storage is essentially Blob with hierarchical namespace (for Hadoop/Synapse analytics). Azure Functions and Event Grid trigger on Blob events. Seamless use with Azure Backup, Azure CDN, and Azure ML pipelines. |
| Hidden Costs | Additional charges for data egress (out of AWS) and inter-region replication. Charges for API calls (large numbers of small files can add cost). Lifecycle transitions to Glacier classes incur retrieval fees if accessed early. | Higher charges for operations – API calls are expensive (e.g. ~$5 per 1M writes). Egress fees out of Google Cloud (no free intra-cloud egress to GCP compute). Lifecycle class transitions (Nearline/Coldline) have minimum storage days (30–90 days) and retrieval fees. | Redundancy costs: Multi-AZ (ZRS) or geo-redundant (GRS) storage costs more. Early deletion fees for Cool/Archive tier (e.g. 30 days cool, 180 days archive). Egress fees out of Azure apply (first 100GB free, then tiered). |
| Support & SLAs | Enterprise support available (AWS Support plans) and a vast third-party community. SLA: 99.9% availability (credits if below). Longest track record (S3 launched 2006) with proven reliability, though notable past outage (2017) highlighted need for multi-region DR. | Google premium support offers assistance; growing community but smaller than AWS’s. SLA: 99.95% (multi-regional) or 99.9% (regional) availability. High reliability; Google’s own services (e.g. YouTube) rely on GCS tech, demonstrating confidence. | Strong Microsoft enterprise support network. SLA: 99.9% for read/write (hot tier); 99.99% read availability with RA-GRS. Highly reliable, used in many Azure services. Note: LRS (no zone redundancy) can suffer outages if a datacenter fails – consider ZRS/GRS for critical data. |
| Geo-Replication & DR | Cross-Region Replication (CRR) available (user-configured, pay egress+storage for second copy). Multi-Region Access Points can simplify multi-region reads but still require duplicates. 11×9 durability in one region; for disaster recovery, must maintain a backup in another region or use third-party solutions. | Dual- and Multi-Region buckets: offer automatic geo-replication within continent (e.g. “US” bucket replicates data in multiple US regions). Egress between regions in multi-regional class is handled by Google’s network. For custom DR, can use Bucket migration or Transfer Service to copy data to another cloud/region (egress costs apply). | Geo-Redundant Storage (GRS) replicates to secondary region (asynchronous). Read Access Geo (RA-GRS) allows reads from the secondary. These provide automated DR at storage layer (with eventual consistency). Alternatively, customers implement custom replication or use Azure Backup for certain data types (not generic blob backup). |
Quick Decision Guide
When to Choose AWS S3
✅ Best for:
- Organizations already heavily invested in AWS ecosystem
- Workloads with extremely high transaction volumes (lowest API costs)
- Applications that leverage the S3 API’s ubiquity
- When integration with AWS’s analytics stack (Athena, EMR, Redshift) is crucial
⚠️ Consider alternatives when:
- Looking for predictable, commitment-based pricing discounts
- Multi-region replication needs to be automatic
- Your organization is primarily Microsoft-centric
When to Choose Google Cloud Storage
✅ Best for:
- Workloads requiring high-throughput for large objects
- When global accessibility is needed (multi-regional buckets)
- Data science and ML workflows on Google Cloud
- Simplicity in management and operations
⚠️ Consider alternatives when:
- Workloads involve extremely high API operation counts
- Integration with non-Google tools is a priority
- Reserved capacity pricing would benefit your budget
When to Choose Azure Blob Storage
✅ Best for:
- Organizations with existing Microsoft investments
- When reserved capacity discounts (1-3yr) would benefit your budget
- Integration with Azure AD/Microsoft security ecosystem
- Workloads requiring hierarchical namespace (ADLS Gen2)
⚠️ Consider alternatives when:
- Looking for simpler object namespace (vs. storage accounts/containers)
- When multi-AZ redundancy is needed (ZRS costs extra vs. S3/GCS defaults)
- Ecosystem compatibility with non-Microsoft tools is crucial