Every cloud database migration eventually runs into the same question: which provider actually charges less for the same PostgreSQL or MySQL workload once reserved capacity, storage, backups, and multi-region replicas are added up? AWS RDS, Azure Database for PostgreSQL/MySQL, and Google Cloud SQL all publish list prices that look similar on the surface, but the gap widens fast once you factor in instance families, high-availability tiers, and serverless billing. This comparison breaks down real 2026 pricing across small, medium, and large workloads, benchmarks throughput and latency from independent sources, and gives a migration path for teams weighing a move.
The short version: Google Cloud SQL tends to win on raw compute and mid-tier PostgreSQL pricing, Azure Database frequently undercuts both on small-to-medium managed Postgres and MySQL instances, and AWS RDS leads on engine breadth and ecosystem maturity, often at a premium enterprises pay for anyway. None of the three wins across every dimension, which is exactly why this decision keeps resurfacing on engineering teams’ roadmaps every budget cycle.
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AWS RDS vs Azure Database vs Google Cloud SQL: Quick Verdict
For teams that need an answer before reading 6,000 words: pick Google Cloud SQL if your workload is a standard 4-16 vCPU PostgreSQL production instance and cost-per-vCPU is the deciding factor. Pick Azure Database for PostgreSQL/MySQL if you’re running small-to-medium instances (1-4 vCPU) or you’re already inside the Microsoft ecosystem with .NET services and Azure AD. Pick AWS RDS if you need the widest selection of database engines (15+, including Aurora, and legacy engines like Oracle and SQL Server under one console) or you’re already deep into the AWS ecosystem for compute and networking.
The pricing gap is not trivial. Depending on instance size and region, the difference between the cheapest and most expensive provider for an equivalent managed PostgreSQL instance runs from roughly 10% at the small tier to over 20% at high-end 8 vCPU HA configurations. That compounds fast across a fleet of databases running 24/7.
What Are AWS RDS, Azure Database, and Google Cloud SQL?
Amazon RDS (Relational Database Service) is AWS’s managed database offering, supporting PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, and the AWS-native Aurora engine (PostgreSQL- and MySQL-compatible). RDS handles patching, backups, and failover, and pairs with Aurora Serverless v2 for workloads with variable traffic. AWS has run RDS since 2009, giving it the longest production track record of the three.
Azure Database for PostgreSQL and Azure Database for MySQL run on Microsoft’s Flexible Server architecture, which replaced the older Single Server tier and now supports zone-redundant high availability, read replicas, and a serverless-style burstable compute tier. Azure’s managed database line integrates tightly with Azure Active Directory, Azure Monitor, and Azure Arc for hybrid deployments.
Google Cloud SQL supports PostgreSQL, MySQL, and SQL Server, and sits alongside Google’s newer AlloyDB service, a PostgreSQL-compatible engine built for heavier analytical and transactional mixed workloads with built-in HA at the storage layer. Cloud SQL is the default choice for teams that want a straightforward managed Postgres/MySQL instance without adopting AlloyDB’s more specialized architecture.
Full Specs Comparison Table
| Feature | AWS RDS | Azure Database (PostgreSQL/MySQL) | Google Cloud SQL |
|---|---|---|---|
| Supported engines | PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, Aurora (PostgreSQL/MySQL-compatible) | PostgreSQL, MySQL | PostgreSQL, MySQL, SQL Server |
| Serverless option | Aurora Serverless v2 (0.5 ACU minimum) | Flexible Server Burstable tier | Cloud SQL serverless-style autoscaling (per-second billing) |
| Max storage per instance | Up to 128 TB (Aurora), 64 TB (standard RDS) | Up to 32 TB | Up to 64 TB |
| High-availability model | Multi-AZ synchronous standby | Zone-redundant HA (synchronous) | Regional HA with automatic failover |
| Read replica limit | Up to 15 (5 for standard RDS, more for Aurora) | Up to 10 | Up to 10 (cross-region supported) |
| Automated backups retention | Up to 35 days | Up to 35 days | Up to 365 days (with configuration) |
| Point-in-time recovery | Yes | Yes | Yes |
| Encryption at rest | AWS KMS (default on for new instances) | Azure-managed keys or customer-managed keys | Google-managed or customer-managed encryption keys |
| IAM integration | AWS IAM database authentication | Azure Active Directory authentication | Cloud IAM database authentication |
| Minimum instance size (smallest tier) | db.t3.micro (1 vCPU, 1 GB) | B1ms Burstable (1 vCPU, 2 GB) | db-f1-micro (shared vCPU, 0.6 GB) |
| Reserved capacity discount | Up to ~40-60% (1-3 year Reserved Instances) | Up to ~35-55% (1-3 year Reserved Capacity) | Up to ~50-57% (Committed Use Discounts) |
| Free tier | 750 hours/month db.t3.micro (12 months) | 750 hours B1ms (12 months) | No standing free tier for Cloud SQL (credits only) |
| Multi-cloud/hybrid support | AWS Outposts, RDS Custom | Azure Arc-enabled data services | Google Distributed Cloud, BigQuery federation |
Pricing Comparison: Small, Medium, and Large Instances
List prices shift by region and by which instance family a provider uses to represent a “comparable” configuration, so treat the numbers below as directional rather than exact for your account. All figures are on-demand, us-east/us-east-1-class regions, and pulled from provider pricing pages and independent 2026 cost-benchmark write-ups.
| Instance tier | AWS RDS (PostgreSQL) | Azure Database (PostgreSQL) | Google Cloud SQL (PostgreSQL) |
|---|---|---|---|
| Small (1-2 vCPU, ~2 GB RAM) | ~$0.034/hr (~$24-25/mo, db.t3.small) | ~$0.068/hr (~$50-60/mo, B2s) | ~$0.015-0.078/hr depending on tier (~$11-68/mo) |
| Medium (4 vCPU, 16 GB RAM, ~100-250 GB storage) | ~$186-420/mo depending on RI/On-Demand mix | ~$179-390/mo | ~$173-370/mo |
| Large (8 vCPU, 32-64 GB RAM) | ~$0.478-0.544/hr (~$349-397/mo On-Demand) | ~$0.532/hr (~$388/mo) | ~$0.4576/hr (~$334/mo) |
| Multi-AZ/HA large (8 vCPU, 64 GB, 1 TB) | ~$1,154/mo (RDS Multi-AZ) or ~$1,285/mo (Aurora) | ~$980/mo (zone-redundant HA) | ~$975/mo (regional HA) |
| Serverless (per vCPU-hour) | ~$0.12/vCPU-hr (Aurora Serverless v2, 0.5 ACU minimum) | ~$0.14/vCPU-hr (Flexible Server, 5-min billing granularity) | ~$0.10/vCPU-hr (per-second billing) |
| 1-year reserved/committed discount tier (4 vCPU class) | ~$342/mo (1-yr RI) | ~$367/mo (reserved capacity) | ~$298/mo (committed-use discount) |
The pattern that holds across most published 2026 benchmarks: Google Cloud SQL is consistently cheapest or near-cheapest at the mid-to-large PostgreSQL tiers, often 10-16% below AWS RDS at 8 vCPU-class instances. Azure Database wins at the small tier for both PostgreSQL and MySQL, sometimes by 15-25% versus AWS. AWS RDS is rarely the cheapest option on a pure hourly-rate basis, but it wins back ground once you factor in Aurora’s performance-per-dollar at high concurrency and the value of running one fewer cloud vendor relationship if you’re already committed to AWS for compute and storage.
MySQL Pricing: A Different Winner
MySQL pricing doesn’t track the same pattern as PostgreSQL. At the small db.t3.small-class tier, Azure Database for MySQL comes in around $0.052/hour (roughly $456/year), compared to $0.063/hour on Google Cloud SQL (~$552/year) and $0.080/hour on AWS RDS (~$701/year) — Azure undercutting AWS by close to 35% annually at that size. But at a 2 vCPU medium configuration, AWS RDS db.t3.medium MySQL flips to the cheapest at roughly $0.068/hour (~$49/month), ahead of Azure’s General Purpose tier at ~$0.089/hour (~$64/month) and GCP’s n1-standard-1 at ~$0.0965/hour (~$70/month). The takeaway: don’t assume one provider wins MySQL across the board — always price your exact instance size before committing to reserved capacity.
Performance Benchmarks: Throughput and Latency
Pricing only tells half the story. Independent 2026 database review sites that ran comparable pgbench and sysbench-style workloads across all three providers found meaningful differences in sustained throughput, not just sticker price.
| Benchmark dimension | AWS RDS / Aurora | Azure Database Flexible Server | Google Cloud SQL |
|---|---|---|---|
| Storage I/O architecture | Decoupled compute/storage on Aurora; EBS-backed on standard RDS | Local NVMe-backed Flexible Server storage tiers | Persistent SSD with configurable IOPS |
| Failover time (typical) | ~30-60 seconds (Multi-AZ), faster on Aurora (sub-30s) | ~60-120 seconds (zone-redundant HA) | ~60 seconds typical (regional HA) |
| Read replica lag under load | Low with Aurora’s shared storage layer; higher on standard RDS | Moderate, async replication | Moderate, async replication with cross-region option |
| Connection scaling | RDS Proxy add-on for pooled connections | PgBouncer built into Flexible Server | Built-in connection pooling via Cloud SQL Auth Proxy |
| Vertical scaling downtime | Requires restart on standard RDS; near-zero on Aurora Serverless v2 | Requires restart for compute tier changes | Requires restart for machine type changes |
Aurora’s decoupled storage architecture is the standout differentiator for AWS: because compute and storage scale independently and replicate across three availability zones by design, Aurora PostgreSQL and Aurora MySQL typically post lower replica lag and faster failover than standard RDS instances running the same engine. That’s why AWS’s own comparisons and third-party 2026 TCO write-ups treat Aurora, not vanilla RDS, as the fair performance comparison point against Azure Flexible Server and Cloud SQL — vanilla RDS is priced lower but performs closer to the other two providers’ baseline tier.
Architecture Differences That Affect Your Decision
The three providers didn’t converge on identical architectures, and the differences matter more than the pricing table once you’re running production traffic.
AWS’s storage-compute split (Aurora) vs instance-attached storage (RDS). Aurora separates compute from a shared, auto-scaling storage layer replicated six ways across three AZs. Standard RDS instances, by contrast, attach EBS volumes directly to the compute instance, which is architecturally closer to what Azure Flexible Server and Cloud SQL do. If you need Aurora-class resilience, you’re paying an Aurora premium — the standard RDS price in the tables above understates what full parity with Azure/GCP HA actually costs.
Azure’s Burstable tier. Azure’s B-series Burstable compute tier accrues CPU credits during idle periods and spends them during traffic spikes, similar in spirit to AWS’s T-series burstable EC2 instances. This makes Azure notably cheap for dev/test databases and low-traffic production workloads that spike occasionally, but it can throttle hard under sustained high CPU, which is a common source of unexpected latency spikes teams report after migrating a “small” database to Azure without reading the credit-exhaustion documentation.
Google’s AlloyDB alternative. Cloud SQL is Google’s general-purpose managed database, but Google has been steering heavier PostgreSQL workloads toward AlloyDB, which bakes HA into the storage layer (similar conceptually to Aurora) and adds a columnar engine for faster analytical queries on transactional data. If your workload looks more like Aurora’s target use case — high concurrency, need for built-in resilience — AlloyDB, not Cloud SQL, is the more accurate Google comparison point, and it’s priced above standard Cloud SQL (roughly $262/month for a 4 vCPU instance with built-in HA in recent 2026 cost guides).
Version and patching cadence. All three providers support current major PostgreSQL and MySQL releases, but the timeline for making a new major version generally available differs. AWS and Google Cloud have historically been the fastest to certify a new PostgreSQL major version for their managed service, with Azure typically following within a few months. For teams that want to run the newest PostgreSQL release on day one, that lag is worth checking against your own upgrade timeline rather than assuming parity across providers.
Real-World Use Cases and Recommendations
1. Startup running a single-region SaaS product on a tight budget. Azure Database for PostgreSQL Burstable tier or Google Cloud SQL’s smallest shared-core tier both undercut AWS RDS at the 1-2 vCPU level. If you’re not yet locked into an AWS-heavy stack, Cloud SQL’s f1-micro-class pricing (as low as ~$0.015/hour) gives the most headroom for a bootstrapped team.
2. Enterprise migrating a legacy Oracle or SQL Server estate. AWS RDS is the only one of the three that natively hosts Oracle and SQL Server alongside PostgreSQL and MySQL in the same console and billing relationship, which matters for teams doing a phased migration off legacy engines rather than a clean rewrite.
3. High-concurrency fintech or e-commerce platform needing sub-second failover. Aurora PostgreSQL/MySQL’s shared storage architecture and fast failover make it the safer default here, even at a price premium over standard RDS, Azure, or Cloud SQL — the cost of a failed transaction during a failover event usually dwarfs the monthly instance bill difference.
4. Microsoft-shop enterprise running .NET services and Azure AD. Azure Database for PostgreSQL/MySQL’s native Azure AD authentication and Azure Monitor integration removes a chunk of custom IAM and observability glue code that AWS or GCP migrations would require, even if the raw instance price were identical.
5. Data team already standardized on BigQuery for analytics. Google Cloud SQL’s federation with BigQuery (query federation across operational and analytical stores without a separate ETL pipeline) is a meaningful operational simplification for teams already committed to Google’s data stack.
6. Multi-cloud or hybrid deployment with on-prem components. Azure Arc-enabled data services extends Azure’s managed database experience to on-prem or other-cloud infrastructure, which is a more mature hybrid story right now than AWS Outposts’ database support or Google’s Distributed Cloud offering for this specific use case.
7. Gaming or ad-tech platform with unpredictable traffic spikes. Aurora Serverless v2’s sub-second scale-up, combined with its low replica lag, makes it the strongest fit for workloads where a launch event, a viral moment, or a live sports tie-in can multiply query volume in minutes rather than hours — the kind of spike that a Burstable-tier or fixed-provisioned instance on the other two providers is more likely to struggle with.
8. Regulated industry (healthcare, finance) needing long backup retention. Google Cloud SQL’s support for backup retention up to 365 days, well beyond the 35-day ceiling on AWS RDS and Azure Database, is a meaningful advantage for organizations with regulatory requirements around long-term point-in-time recovery, and it avoids the extra engineering work of exporting older backups to a separate object-storage archive.
Migration Guide: Moving Between Providers
Migrating a production PostgreSQL or MySQL database between AWS, Azure, and GCP follows a similar shape regardless of direction, but a few provider-specific gotchas are worth planning around before you schedule a cutover window.
- Audit engine-specific extensions and versions. Confirm your PostgreSQL extensions (PostGIS, pg_cron, pg_stat_statements, etc.) are supported on the destination provider — extension support lists differ between RDS, Azure Flexible Server, and Cloud SQL.
- Provision the destination instance at a matching or slightly larger tier than production, not the cheapest tier, to avoid throttling during the initial full load.
- Use native replication tooling for the initial sync: AWS Database Migration Service (DMS) for moving into or out of RDS, Azure Database Migration Service for Azure targets, or Google’s Database Migration Service for Cloud SQL targets. All three support continuous replication with minimal downtime cutover.
- Run a schema and extension compatibility check before the full load — pg_dump/pg_restore or mysqldump work across all three providers, but managed-service-specific roles and permissions need re-mapping.
- Benchmark the destination instance under a synthetic load test (pgbench or sysbench) before cutover, matching your production peak QPS, not average QPS.
- Set up parallel monitoring on both source and destination (CloudWatch, Azure Monitor, or Cloud Monitoring) for at least one full business cycle before decommissioning the source.
- Cut over application connection strings during a low-traffic window, with a rollback plan that keeps the source database in sync via reverse replication for 24-72 hours post-cutover.
- Re-validate IAM/auth integration on the new provider — AWS IAM database auth, Azure AD auth, and Cloud IAM database auth are not drop-in replacements for each other and require separate configuration.
- Decommission the source instance only after a full backup cycle has completed successfully on the new provider.
Pros and Cons
AWS RDS
Pros: widest engine support (PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, Aurora); Aurora’s decoupled storage delivers strong failover and replica-lag performance; deepest third-party tooling ecosystem; longest production track record since 2009.
Cons: rarely the cheapest option on raw hourly pricing; Aurora’s performance advantages come at a price premium over standard RDS; reserved instance planning adds complexity for teams with variable workloads.
Azure Database for PostgreSQL/MySQL
Pros: cheapest at small-to-medium instance tiers for both PostgreSQL and MySQL in most 2026 pricing comparisons; native Azure AD integration; Burstable tier is excellent value for spiky, low-average-load workloads; Azure Arc extends the managed experience to hybrid/on-prem.
Cons: only supports PostgreSQL and MySQL (no Oracle/SQL Server-class breadth on the managed-database side); Burstable tier can throttle unpredictably under sustained CPU load; fewer read replica slots than AWS.
Google Cloud SQL
Pros: consistently competitive to cheapest at mid-to-large PostgreSQL tiers; per-second billing granularity on serverless-style scaling; strong committed-use discounts (up to ~57%); tight BigQuery federation for analytics-heavy teams.
Cons: no standing free tier (credit-based trials only); heavier workloads are steered toward the pricier AlloyDB rather than staying on Cloud SQL; fewer database engines supported than AWS.
Serverless and Autoscaling: Which Provider Handles Bursty Traffic Best
Not every workload runs at a flat, predictable rate. A ticketing platform that sees 90% of its monthly traffic in a two-hour flash sale, or a B2B SaaS tool that’s idle overnight and hammered during business hours, is a poor fit for a fixed provisioned instance sized for peak load. All three providers now offer a serverless or autoscaling path, but the billing models and scale-down behavior differ enough to matter.
Aurora Serverless v2 scales in fractional Aurora Capacity Units (ACUs) with a floor of 0.5 ACU, and it can scale up within seconds during a traffic spike without a connection-dropping restart, which is the feature most teams cite as the reason to pick it over standard RDS for spiky workloads. The tradeoff is that Aurora Serverless v2 doesn’t scale all the way to zero, so idle-but-not-off periods still generate a bill. Azure’s Flexible Server Burstable tier takes a different approach: it’s not serverless in the strict sense, but the B-series credit system means a mostly idle database accrues CPU credit and can absorb short bursts without upgrading tiers, at the cost of throttling once credits run out under sustained load. Google Cloud SQL’s autoscaling, billed per second, is the most granular of the three for cost tracking, though Google steers genuinely bursty, high-concurrency workloads toward AlloyDB rather than positioning Cloud SQL itself as the serverless-first option.
For a workload that’s truly idle for large stretches of the day — a staging environment, an internal admin tool, a low-traffic side project — none of the three “managed database” services scale to zero the way a fully serverless database product like Neon or PlanetScale does. If near-zero idle cost is the actual requirement rather than burst-handling, it’s worth evaluating those specialized serverless-native providers alongside the big three rather than assuming Aurora Serverless, Azure Burstable, or Cloud SQL autoscaling will get you there.
Storage, Backup, and Egress Pricing Compared
The instance-hour price is the number most comparison articles lead with, but storage, backup retention past the free window, and network egress routinely add 15-30% to a real monthly bill. Here’s how the three providers structure those charges.
| Cost category | AWS RDS | Azure Database | Google Cloud SQL |
|---|---|---|---|
| General-purpose SSD storage | ~$0.115/GB-month (gp3) | ~$0.115-0.138/GB-month (Premium SSD v2) | ~$0.17/GB-month (SSD, included IOPS) |
| Provisioned IOPS (above baseline) | Billed separately above 3,000 baseline IOPS on gp3 | Billed separately above tier baseline | Bundled into storage tier selection, less granular billing |
| Backup storage beyond free allotment | ~$0.095/GB-month beyond instance storage size | ~$0.095-0.10/GB-month beyond 100% of provisioned storage | ~$0.08/GB-month beyond included backup allowance |
| Cross-region read replica egress | Standard AWS inter-region data transfer rates apply | Standard Azure bandwidth pricing applies (varies by region pair) | Standard GCP inter-region egress applies |
| Snapshot export to object storage | Supported (export to S3) | Supported (export to Blob Storage) | Supported (export to Cloud Storage) |
The practical impact: a team running a 500 GB PostgreSQL instance with a 30-day backup retention window and a cross-region disaster-recovery replica should budget for storage and backup costs roughly equal to 20-25% of the compute instance bill, on top of whatever egress the DR replica generates during initial sync and ongoing replication. None of the three providers publish this as a single line item in their pricing calculators by default, which is why so many teams get an unpleasant surprise on their first full month’s invoice after a migration.
FinOps: How to Cut Your Managed Database Bill
Regardless of which provider you land on, the same cost-control levers apply, and most teams only pull two or three of them.
- Right-size before you commit to reserved capacity. Run at least two weeks of production traffic through a monitoring tool before locking in a 1- or 3-year reserved instance. Oversized reservations are the single biggest source of wasted cloud database spend across all three providers.
- Separate read-heavy traffic onto read replicas. Offloading reporting queries and read-heavy API traffic to a replica lets you keep the primary instance smaller, which compounds savings since primary instances typically need the HA-tier pricing while replicas can sometimes run on cheaper single-AZ configurations.
- Match backup retention to actual compliance requirements, not defaults. Extending retention from the default 7 days to 35 days “just in case” on every environment, including staging and dev, is a common source of avoidable backup storage cost.
- Use burstable/serverless tiers for non-production environments. Dev and staging databases rarely need a fixed provisioned instance running 24/7 at production-equivalent size — Azure Burstable, Aurora Serverless v2, or Cloud SQL autoscaling all cut idle-time cost substantially compared to a static instance sized for peak.
- Audit orphaned snapshots and unused read replicas quarterly. Snapshots and idle replicas left over from a completed migration or an abandoned feature branch are a recurring, quietly compounding cost across all three clouds.
Common Migration Mistakes to Avoid
Teams that have moved a production database between AWS, Azure, and GCP tend to report the same handful of avoidable mistakes, regardless of which direction the migration went.
Underestimating extension compatibility gaps. A PostgreSQL database relying on a less common extension — TimescaleDB, pgvector at an unsupported version, or a custom C extension — can block a lift-and-shift migration entirely on a target provider that hasn’t approved that extension for its managed service. Always check the destination provider’s supported-extensions list before scheduling a cutover date, not during it.
Sizing the destination instance off the source provider’s instance name instead of actual utilization. A “4 vCPU” instance on one provider is not guaranteed to deliver identical real-world throughput to a “4 vCPU” instance on another, given differences in underlying hardware generations and storage architecture. Size the destination off observed CPU, memory, and IOPS utilization on the source, not off matching instance-type names.
Skipping a load test on the destination before cutover. Replication tools validate that data moved correctly, not that the destination instance can handle production query patterns at peak concurrency. A pre-cutover load test using pgbench or sysbench against realistic query shapes catches performance regressions before they become customer-facing incidents.
Forgetting connection string and driver-level differences. IAM-based authentication tokens, SSL/TLS certificate chains, and connection pooler configuration (RDS Proxy vs. PgBouncer vs. Cloud SQL Auth Proxy) are rarely drop-in compatible. Budget explicit engineering time for updating application-side connection handling, not just the database itself.
Total Cost of Ownership Beyond the Instance Bill
Instance pricing is the number everyone benchmarks, but it’s rarely the biggest line item on a real production bill. Storage I/O, backup retention beyond the free window, cross-region replication traffic, and egress charges for read replicas in a different region all add up. AWS and Azure both charge separately for provisioned IOPS above the baseline included with an instance, while Google Cloud SQL bundles a configurable IOPS tier directly into its storage pricing, which can simplify forecasting for teams that don’t want to model IOPS spend as a separate line item. Cross-region egress is the other quiet cost driver: all three providers charge for data leaving their network, and a cross-region read replica setup (common for disaster recovery) can add a meaningful percentage on top of the base instance cost depending on replication volume.
Reserved capacity and committed-use discounts change the calculus substantially. AWS Reserved Instances and Azure Reserved Capacity both offer up to roughly 40-60% savings on 1-3 year commitments, while Google’s Committed Use Discounts run up to roughly 57%. The catch: reserved pricing only pays off if your workload is stable enough to commit to a fixed instance size for the full term, which is a poor fit for teams still in a growth phase where instance sizing changes every few months.
Security and Compliance Comparison
All three managed database services support encryption at rest by default, VPC/VNet-level network isolation, and the standard compliance certifications (SOC 2, ISO 27001, HIPAA-eligible configurations, PCI DSS). The differentiators show up in the details: AWS RDS’s IAM database authentication integrates cleanly with existing AWS IAM policies for teams already managing fine-grained AWS permissions. Azure Database’s native Azure AD authentication is the strongest option for enterprises with existing Active Directory infrastructure who want single sign-on extended straight into the database layer. Google Cloud SQL’s IAM database authentication follows the same pattern as AWS but ties into Google’s Workspace/Cloud Identity ecosystem instead.
For customer-managed encryption keys, all three support bring-your-own-key configurations (AWS KMS, Azure Key Vault, Google Cloud KMS), which matters for regulated industries that require key rotation and revocation control outside the cloud provider’s default managed-key path.
Audit logging is another area where the three diverge in default behavior. AWS RDS requires explicitly enabling database activity streams or exporting logs to CloudWatch for full query-level audit trails, which adds a small but real cost. Azure Database’s audit logging integrates directly with Azure Monitor and Log Analytics workspaces, making it comparatively simpler to wire into an existing Azure-native SIEM pipeline. Google Cloud SQL’s audit logs flow into Cloud Logging by default for admin activity, though data-access audit logs for query-level detail still need to be explicitly enabled per instance. None of these differences are dealbreakers on their own, but for a compliance-driven procurement process, they’re worth confirming against your specific audit framework before signing a multi-year commitment with any one provider.
Which Provider Should You Choose?
There’s no universal winner here, and every 2026 cost-benchmark write-up that claims otherwise is likely comparing a single instance size that happens to favor whichever provider it’s promoting. The decision comes down to three questions: What’s your workload size and traffic pattern? What cloud ecosystem are you already committed to for compute, networking, and identity? And how much does sub-minute failover actually matter for your specific application?
If those three answers point toward “small, spiky, Microsoft shop” — Azure Database wins on price and integration. If they point toward “mid-size, steady, cost-sensitive, not locked into a vendor” — Google Cloud SQL is the strongest value at the 4-16 vCPU range most production workloads actually run. If they point toward “large, high-concurrency, need Oracle/SQL Server support, or already deep in AWS” — RDS and Aurora are worth the premium for the operational maturity and failover performance you get in return.
It’s also worth weighing switching cost against savings before treating any of these price gaps as a reason to migrate an existing production database. A 10-16% instance-price advantage on paper can be wiped out by the engineering time spent on extension compatibility checks, connection-string rewrites, and a multi-week validation window, especially for a database under 500 GB where the absolute dollar savings are modest. The pricing comparisons in this article are most actionable for teams making a first choice on a new project, not necessarily a mandate to re-platform something already running smoothly. For an existing production workload, the calculation usually only tips toward migration when the price gap is compounding across a large fleet of instances, or when a specific architectural feature — Aurora’s failover speed, Azure AD-native auth, or Cloud SQL’s BigQuery federation — solves a real operational pain point beyond the monthly bill.
Frequently Asked Questions
Which is cheaper: AWS RDS, Azure Database, or Google Cloud SQL?
It depends on instance size. Azure Database tends to be cheapest at small (1-2 vCPU) PostgreSQL and MySQL tiers, often 15-25% below AWS RDS. Google Cloud SQL tends to be cheapest at medium-to-large (4-8 vCPU) PostgreSQL tiers, typically 10-16% below AWS RDS. AWS RDS is rarely the outright cheapest on hourly pricing but can be competitive with reserved instance discounts.
Is Aurora the same thing as RDS?
Aurora is a distinct engine within the RDS family. It’s PostgreSQL- and MySQL-compatible but uses a decoupled compute/storage architecture that AWS built in-house, which delivers faster failover and lower replica lag than standard RDS instances, at a higher price point.
Does Google Cloud SQL support Oracle or SQL Server?
Google Cloud SQL supports PostgreSQL, MySQL, and SQL Server, but not Oracle. AWS RDS is the only one of the three that supports Oracle natively as a managed database engine.
What’s the difference between Cloud SQL and AlloyDB?
Cloud SQL is Google’s general-purpose managed PostgreSQL/MySQL/SQL Server service. AlloyDB is a newer, PostgreSQL-compatible engine with built-in high availability at the storage layer and a columnar engine for faster analytical queries, positioned as Google’s answer to AWS Aurora for high-concurrency workloads. AlloyDB costs more than standard Cloud SQL.
Can I migrate from AWS RDS to Azure Database without downtime?
Near-zero downtime is achievable using Azure Database Migration Service or AWS DMS configured for continuous replication, followed by a brief cutover window to redirect application connection strings. Full zero-downtime requires careful planning around extension compatibility and connection draining.
Which provider offers the best free tier for testing?
AWS RDS and Azure Database both offer 750 hours per month of a small instance (db.t3.micro on AWS, B1ms on Azure) free for 12 months on a new account. Google Cloud SQL does not have a standing free tier for the service itself, though new Google Cloud accounts receive general-purpose credits that can be applied to Cloud SQL usage.
Do reserved instances or committed-use discounts apply to serverless database tiers?
Generally no. Reserved Instances (AWS), Reserved Capacity (Azure), and Committed Use Discounts (GCP) apply to fixed-size provisioned instances. Serverless tiers like Aurora Serverless v2, Azure Flexible Server Burstable, and Cloud SQL’s autoscaling billing are priced per vCPU-hour or per-second and are not eligible for the same commitment discounts.
Which provider has the best high-availability failover time?
Aurora (AWS) typically posts the fastest failover among the three, often under 30 seconds, due to its shared, multi-AZ storage architecture. Standard RDS Multi-AZ, Azure zone-redundant HA, and Google Cloud SQL regional HA all typically fail over in the 60-120 second range.
Related Coverage
- How to Set Up Amazon RDS: 12 Steps, 90 Min [2026]
- AWS vs Azure vs Google Cloud: 4x H100 GPU Price Gap [2026]
- EKS vs AKS vs GKE: $73/mo vs Free Control Plane [2026]
- AWS Transit Gateway vs VPC Peering vs PrivateLink: $0.05/hr Gap [2026]
- DuckDB vs SQLite: 938x Faster Scans, $250/mo Cloud [2026]
- AWS Outage Hits US-East-1: 28 Hours, 10% Credit [2026]
For broader cloud infrastructure context, see our cloud computing 2026 hub.
Sources: AWS RDS Pricing, Azure Database for PostgreSQL Flexible Server Pricing, Google Cloud SQL Pricing, Amazon Aurora Serverless v2, Azure Database for PostgreSQL Flexible Server Overview.
