Last quarter, an e-commerce platform at Series C came to JusDB after spending three months trying to pick a managed database provider. Their infrastructure was already split: the backend ran on AWS, the ML pipeline ran on Google Cloud, and a new European subsidiary was being onboarded under an enterprise Azure agreement. The platform engineering team had a deadline of six weeks to standardize their MySQL 8.0 deployment across all three environments — and each cloud vendor had a sales rep insisting their service was the right answer.
Choosing a managed database service is not just a feature checklist exercise. The differences between Amazon RDS, Google Cloud SQL, and Azure Database for MySQL/PostgreSQL show up in production failover behavior, storage scaling under unexpected load, connection handling at high concurrency, and how badly you get locked in to a single vendor's feature set. Getting it wrong costs six to eighteen months of migration pain later.
This comparison covers all three services as they stand in 2026 — including the features that matter most in production, a detailed pricing breakdown for equivalent instance sizes, and a direct recommendation framework based on your cloud footprint and workload type.
- Amazon RDS leads on ecosystem depth — Multi-AZ, read replicas, RDS Proxy, Performance Insights, and Aurora compatibility make it the most operationally complete managed database service available today.
- Google Cloud SQL is the cleanest option for teams already on GCP: private IP by default, automatic storage increases without manual intervention, and a Cloud SQL Auth Proxy that eliminates exposed database endpoints.
- Azure Database for MySQL/PostgreSQL Flexible Server is the correct Azure choice in 2026 — Single Server is deprecated and should not be used for any new workload.
- Aurora (Amazon) is not RDS — it is a separate product with a cloud-native storage architecture, parallel query, global databases, and ~3x the price for equivalent compute. Lock-in is real.
- For equivalent compute (4 vCPU / 32 GB RAM), monthly pricing runs approximately: RDS db.r6g.xlarge $440–480, Cloud SQL db-n2-highmem-4 $370–420, Azure Flexible Server General Purpose D4ds_v5 $390–450 (all multi-AZ/HA, us-east/us-central, reserved pricing excluded).
- None of the three services provides a universal best answer — the right choice depends on which cloud your application already runs on, your HA requirements, and whether you need Aurora-specific capabilities.
Background
All three managed database services launched between 2009 and 2014 and have been converging on similar feature sets ever since. The surface-level pitch from each cloud vendor is nearly identical: fully managed, automated backups, point-in-time recovery, automatic minor version patching, and high availability. The differences that matter in production are in the implementation details — how failover actually works, how storage behaves under load, how connections are pooled, and what happens when you want to leave.
Understanding these differences requires going one layer deeper than the marketing documentation. The three platforms have fundamentally different architectural philosophies. RDS is built on EC2 with EBS block storage — the same infrastructure primitives available to any AWS customer. Cloud SQL runs on Google's internal Compute Engine with Persistent Disk. Aurora replaced EBS with a distributed shared storage system built by AWS specifically for database workloads. Azure Flexible Server runs on Azure VMs with Premium SSD v2 attached storage. Each of these choices has downstream implications for failover speed, IOPS behavior, and storage scaling.
Amazon RDS Overview
Amazon RDS supports MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server. For MySQL and PostgreSQL workloads, the key architectural features are:
Multi-AZ Deployments
RDS Multi-AZ provisions a synchronous standby replica in a different Availability Zone. Replication is synchronous — every write is committed to the primary and acknowledged by the standby before the application receives a commit confirmation. Failover is automated and typically completes in 60–120 seconds, involving a DNS CNAME flip from the failed primary to the standby.
The 60–120 second Multi-AZ failover window is often underestimated in RTO planning. Applications with aggressive TCP timeouts or connection pool settings that do not retry on connection failure will surface user-visible errors during this window. Always test failover behavior explicitly and configure your connection pool's testOnBorrow / validationQuery settings.
Read Replicas
RDS read replicas use asynchronous replication and can be promoted to standalone primary instances. Up to 5 read replicas are supported per primary for MySQL and PostgreSQL. Cross-region read replicas are available, which is the primary mechanism for disaster recovery across AWS regions. Read replicas can also be used as the source for backups, offloading backup I/O from the primary instance.
RDS Proxy
RDS Proxy is a fully managed connection pooler that sits between your application and the database. It maintains a pool of persistent connections to the database and multiplexes application requests over them. This is particularly valuable for Lambda-heavy architectures where each function invocation opens a new database connection, and for applications with bursty connection patterns. RDS Proxy also improves failover recovery time — it continues accepting connections during a Multi-AZ failover event and reconnects to the new primary transparently, reducing application-visible failover time from ~120s to under 30s.
Performance Insights
Performance Insights provides a time-series view of database load broken down by wait events, SQL statements, users, and hosts. It is available on most RDS instance classes at no additional cost for a 7-day retention window (longer retention costs extra). For MySQL workloads, it exposes InnoDB wait events directly — row lock waits, log waits, buffer pool reads — which makes it significantly more actionable than generic CloudWatch metrics for query optimization work.
Aurora Compatibility
Aurora MySQL and Aurora PostgreSQL are API-compatible with standard MySQL and PostgreSQL but use a completely different storage engine. The Aurora storage layer is a distributed, fault-tolerant system that stripes data across 6 copies in 3 Availability Zones. This architecture eliminates the Multi-AZ replication lag and allows up to 15 Aurora Replicas with sub-10ms replica lag. Aurora also supports parallel query (for analytics workloads), global database (cross-region active-active with ~1 second replication lag), and Aurora Serverless v2 for variable-load workloads.
Aurora lock-in is significant. Features like parallel query, global database, Aurora ML integrations, and Aurora Serverless v2 autoscaling do not exist in standard MySQL or PostgreSQL. Once your application code takes a dependency on these features — especially Global Database for cross-region writes — migrating to another provider requires substantial re-architecture. Evaluate Aurora's premium carefully against the migration cost if you ever change cloud providers.
Google Cloud SQL Overview
Cloud SQL supports MySQL, PostgreSQL, and SQL Server. It is the managed relational database service in Google Cloud and is the recommended option for GCP-native applications that do not require AlloyDB (Google's Aurora equivalent, PostgreSQL-only).
Private IP Support
Cloud SQL instances can be configured with a private IP address inside a VPC network, eliminating the need to expose the database to the public internet entirely. Private IP is the recommended configuration for all production Cloud SQL instances. Unlike RDS, where VPC configuration is the default but requires explicit security group management, Cloud SQL's private IP setup uses VPC peering with the Service Networking API and is enabled during instance creation.
When using Cloud SQL with private IP, configure your VPC peering with a /16 or larger allocated range for Service Networking. Undersized ranges cause IP exhaustion errors as you create additional Cloud SQL instances or other Google-managed services in the same project.
Automatic Storage Increases
Cloud SQL supports automatic storage increase: when the available storage falls below a threshold (typically 25 GB or 25% of provisioned storage, whichever is larger), Cloud SQL automatically increases storage capacity. This is enabled by a checkbox during instance configuration. Unlike RDS, which also supports storage autoscaling, Cloud SQL's implementation has a lower minimum threshold and triggers earlier, making it less likely to surprise you with a full-disk incident at 2 AM. Storage cannot be reduced after it is increased on either platform.
High Availability Instances
Cloud SQL HA uses a primary instance and a standby instance in a different zone, with synchronous replication over a shared Persistent Disk volume. Failover is triggered automatically on instance failure and typically completes in 60–120 seconds — comparable to RDS Multi-AZ. One key difference: Cloud SQL HA is a zonal standby (within the same region), while RDS Multi-AZ can span Availability Zones within the same region with more infrastructure isolation between them.
Cloud SQL Auth Proxy
Cloud SQL Auth Proxy is an open-source proxy (available on GitHub) that establishes an encrypted tunnel to a Cloud SQL instance using IAM-based authentication. It eliminates the need to manage SSL certificates for database connections and allows you to use a private IP-only Cloud SQL instance from environments outside the VPC (such as Cloud Run, Cloud Functions, or local development) without exposing the database publicly. The Auth Proxy handles token refresh automatically and works with Workload Identity Federation, making it the standard connection method for Cloud SQL on GKE.
Azure Database Overview
Azure Database supports MySQL Flexible Server and PostgreSQL Flexible Server. The legacy Single Server deployment model is deprecated as of September 2025 and is no longer recommended for new deployments. All production workloads should use Flexible Server.
Flexible Server vs Single Server
The architectural difference is significant. Single Server was a first-generation managed database product with limited customization, a rigid maintenance window approach, and no zone-redundant HA. Flexible Server introduced configurable compute tiers, zone-redundant standby replicas, a custom maintenance window, stop/start capability (useful for dev environments), and support for a larger set of MySQL and PostgreSQL configuration parameters. If you are inheriting a Single Server deployment, plan your migration to Flexible Server now — Microsoft has set hard end-of-life dates.
Zone-Redundant High Availability
Azure Flexible Server HA can be configured in two modes: Same-Zone HA (standby in the same availability zone, lower cost) or Zone-Redundant HA (standby in a different availability zone, higher resilience). Zone-Redundant HA uses synchronous replication and automated failover. Failover time is typically 60–120 seconds, consistent with RDS and Cloud SQL. Zone-Redundant HA is available in all Azure regions that have multiple Availability Zones.
Azure Flexible Server supports the stop/start feature for non-HA instances — you can stop a dev or staging database to avoid compute costs when it is not in use. Storage costs continue while stopped. This feature is not available when HA is enabled, and it is not available on RDS or Cloud SQL.
Read Replicas
Azure Database for MySQL Flexible Server supports up to 10 read replicas. PostgreSQL Flexible Server supports up to 5. Read replicas use asynchronous replication and can be promoted to standalone servers. Cross-region read replicas are supported for disaster recovery scenarios.
Side-by-Side Comparison
| Feature | Amazon RDS | Google Cloud SQL | Azure Flexible Server |
|---|---|---|---|
| Supported engines | MySQL, PostgreSQL, MariaDB, Oracle, SQL Server | MySQL, PostgreSQL, SQL Server | MySQL, PostgreSQL |
| Max storage | 64 TB (MySQL/PostgreSQL) | 64 TB | 32 TB (MySQL), 64 TB (PostgreSQL) |
| Max IOPS | 256,000 (io2 Block Express on gp3-equivalent large instances) | 160,000 (Hyperdisk Balanced, regional) | 80,000 (Premium SSD v2, general purpose tier) |
| HA approach | Synchronous Multi-AZ standby, automated DNS failover, 60–120s | Synchronous zonal standby, automated failover, 60–120s | Synchronous zone-redundant or same-zone standby, 60–120s |
| Connection pooling | RDS Proxy (managed, PgBouncer-equivalent for PostgreSQL) | Cloud SQL Auth Proxy + manual PgBouncer/ProxySQL on GCE | PgBouncer built into Flexible Server (PostgreSQL only) |
| Automated backups | Daily automated backups, transaction log backups every 5 min, PITR to 35 days | Daily automated backups, transaction log PITR up to 35 days | Automated backups, PITR up to 35 days |
| Backup window | Configurable 30-min window (backup I/O can affect performance on gp2) | Configurable window, minimal I/O impact on SSD-backed instances | Configurable maintenance + backup window, geo-redundant backup option |
| Storage autoscaling | Yes — threshold configurable, minimum increase 5 GB | Yes — automatic, triggers at 25% free storage | Yes — automatic, configurable thresholds on Flexible Server |
| Read replicas | Up to 5, cross-region available | Up to 10 (MySQL), cascading replicas supported on PostgreSQL | Up to 10 (MySQL), up to 5 (PostgreSQL), cross-region available |
| pgvector support | Yes (PostgreSQL 15+) | Yes (PostgreSQL 14+) | Yes (PostgreSQL Flexible Server 13+) |
| IAM authentication | Yes (IAM DB authentication for MySQL and PostgreSQL) | Yes (Cloud SQL IAM database authentication) | Yes (Azure AD authentication) |
Pricing Comparison
The following table compares on-demand monthly pricing for a configuration roughly equivalent to a 4 vCPU / 32 GB RAM instance with Multi-AZ or HA enabled, 500 GB SSD storage, in the primary US east region of each cloud. Prices reflect on-demand rates as of early 2026 and do not include reserved instance discounts, which typically reduce compute costs by 30–40% for 1-year commitments.
| Component | RDS db.r6g.xlarge (MySQL, Multi-AZ) | Cloud SQL db-n2-highmem-4 (MySQL, HA) | Azure D4ds_v5 Flexible (MySQL, Zone-Redundant HA) |
|---|---|---|---|
| Instance compute (on-demand) | ~$0.600/hr → ~$438/mo | ~$0.510/hr → ~$372/mo | ~$0.530/hr → ~$387/mo |
| Storage (500 GB gp3 / SSD) | ~$57.50/mo (Multi-AZ doubles storage billing) | ~$51/mo | ~$55/mo |
| I/O costs | $0 on gp3 (included in storage provisioning) | $0.10/million I/O requests on SSD (low-impact at moderate load) | $0 on Premium SSD v2 (IOPS provisioned separately) |
| Backup storage (100 GB) | ~$2.30/mo over free tier | ~$2.60/mo | ~$2.40/mo |
| Approximate monthly total | ~$498/mo | ~$426/mo | ~$445/mo |
| Aurora equivalent (db.r6g.xlarge) | ~$890–1,100/mo (storage I/O billed per GB/request) | N/A | N/A |
Aurora pricing is not directly comparable to RDS. Aurora charges separately for storage ($0.10/GB/month), I/O ($0.20 per million requests on Aurora Standard), and compute. For a write-heavy workload that generates high I/O, Aurora Standard costs can be 2–3x higher than an equivalent RDS Multi-AZ instance. Aurora I/O-Optimized (launched 2023) eliminates the per-I/O charge at a 30% higher instance price — evaluate which mode is cheaper for your actual I/O-to-compute ratio before committing.
How to Choose
Choose Amazon RDS if:
- Your application already runs on AWS. The integration with IAM, VPC, CloudWatch, Secrets Manager, and AWS Lambda is seamless — there is no cross-cloud latency and no egress cost for application-to-database traffic.
- You need Oracle or SQL Server managed hosting — only RDS supports these engines across all three platforms.
- You want RDS Proxy for serverless or high-concurrency connection management without running your own PgBouncer or ProxySQL.
- You are evaluating Aurora and want to start with standard RDS MySQL/PostgreSQL and migrate to Aurora later — the two are on the same management plane.
Choose Google Cloud SQL if:
- Your application already runs on GCP. Cloud SQL integrates with Cloud Run, GKE, App Engine, and Cloud Functions via the Auth Proxy and Workload Identity with near-zero configuration.
- You want automatic storage increases that trigger early and reliably — Cloud SQL's autoscale behavior is more conservative and less likely to hit a full-disk condition.
- You are building a new project and want a private-IP-only database as the default posture without complex security group management.
- You need AlloyDB in the future — AlloyDB is PostgreSQL-only and is Cloud SQL's Aurora-equivalent for GCP. Starting on Cloud SQL makes migration to AlloyDB a supported path.
Choose Azure Flexible Server if:
- Your application runs on Azure, or you have an enterprise Azure Agreement that provides committed Azure spend. Azure Database is deeply integrated with Azure Active Directory, Azure Private Endpoint, and Azure Defender for SQL.
- You need stop/start capability for non-production environments to reduce compute costs outside business hours.
- Your organization standardizes on Microsoft's ecosystem and benefits from consolidated support, billing, and compliance tooling through an existing Azure contract.
Multi-cloud managed database deployments — running the same application against RDS in one region and Cloud SQL or Azure in another — are operationally viable but come with a significant hidden cost: you must master the operational model, monitoring tooling, and failure modes of multiple platforms simultaneously. Unless you have a hard requirement for cloud portability, standardizing on a single cloud's managed database service is almost always the correct decision for teams under 50 engineers.
Key Takeaways
- Match your managed database choice to your application's cloud: RDS for AWS, Cloud SQL for GCP, Flexible Server for Azure — cross-cloud latency and egress costs make multi-cloud database deployments expensive unless there is a specific architectural reason for them.
- Treat Aurora as a separate product decision from RDS — the storage architecture and pricing model are fundamentally different, lock-in to Aurora-specific features (parallel query, global database) is real, and the cost advantage only materializes at high I/O workloads using Aurora I/O-Optimized pricing.
- Never deploy Azure Database for MySQL or PostgreSQL on Single Server for any new workload in 2026 — Single Server is deprecated and its end-of-life migration creates unnecessary operational risk.
- Configure Multi-AZ / HA and test your actual failover time before production launch — the advertised 60–120 second failover window requires application-side connection retry logic to be invisible to users, and most teams discover their connection pools are not configured correctly during their first real failover event.
- Use the native connection proxy on each platform (RDS Proxy, Cloud SQL Auth Proxy, or PgBouncer on Azure Flexible Server for PostgreSQL) before reaching for a self-managed ProxySQL or PgBouncer — the managed proxies handle credential rotation, IAM integration, and failover reconnection in ways that are difficult to replicate correctly in self-managed infrastructure.
- Storage autoscaling is not a substitute for capacity planning — on all three platforms, storage cannot be reduced once it has been increased, and runaway autoscale events on a high-write workload can double your storage bill in hours.
Working with JusDB on Cloud Databases
JusDB manages MySQL and PostgreSQL on RDS, Cloud SQL, and Azure Flexible Server for engineering teams who need production-grade reliability without the operational overhead. Our DBAs handle initial setup and sizing, Multi-AZ failover configuration and testing, RDS Proxy and Cloud SQL Auth Proxy integration, query performance optimization using Performance Insights and pg_stat_statements, backup validation, and 24/7 incident response — so your team ships features instead of fighting database fires at 3 AM.
We have migrated teams between all three platforms, including Single Server to Flexible Server migrations on Azure, RDS MySQL to Aurora MySQL evaluations, and Cloud SQL to AlloyDB proof-of-concept projects. If you are trying to choose a managed database provider or are locked into a platform that is not the right fit for your workload, a 90-minute architecture review can clarify your options before you commit.
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