Best Cloud SQL (2026)
Ranked picks for cloud sql. No "it depends."
Supabase
Postgres + auth + storage + realtime. The Firebase that doesn't lock you in.
Full Rankings
Postgres + auth + storage + realtime. The Firebase that doesn't lock you in.
Pros
- +Open source
- +Generous free tier
- +Full Postgres
- +Auth built-in
- +Realtime
Cons
- -Newer ecosystem
- -Less documentation
- -Some rough edges
Serverless MySQL with branching. DevOps for databases.
Pros
- +Database branching
- +Serverless
- +No downtime deploys
Cons
- -MySQL only
- -No foreign keys
- -Free tier removed
Serverless Postgres with branching. PlanetScale vibes, Postgres reality.
Pros
- +Serverless Postgres
- +Branching
- +Generous free tier
- +Scale to zero
Cons
- -Newer
- -Cold starts
- -Smaller community
Google's data warehouse that makes querying petabytes feel like a casual stroll, as long as you don't mind the bill.
Why we picked it
BigQuery is a serverless data warehouse built for petabyte-scale analytics, not OLTP. Its separation of compute and storage lets you query massive datasets without provisioning, but the pay-per-query model can surprise you if you're not careful. For real-time transactional workloads, Cloud Spanner or Cloud SQL are the right choices — BigQuery is for analytics, period.
→ Use it when you need to run ad-hoc SQL queries on terabytes to petabytes of data and you're okay with paying for each query rather than a fixed compute cost.
Pros
- +Serverless architecture means zero infrastructure management
- +Blazing-fast SQL queries on massive datasets with Google's distributed processing
- +Built-in machine learning and seamless integration with Google Cloud services
Cons
- -Costs can spiral quickly with complex queries or large data scans
- -Limited control over performance tuning compared to self-managed warehouses
Managed databases for people who'd rather not manage databases. It's like having a DBA on retainer, but cheaper and less opinionated.
Pros
- +Automates backups, patching, and scaling, so you can focus on your app instead of babysitting servers
- +Supports multiple engines like PostgreSQL and MySQL, making it easy to switch or standardize
- +Built-in high availability with Multi-AZ deployments, because downtime is for amateurs
Cons
- -Costs can sneak up on you with instance sizes and storage, especially if you forget to turn things off
- -Limited control over the underlying OS and some database settings, which can be frustrating for power users
SQL Server's cloud-bound cousin. All the enterprise-grade features, none of the hardware headaches.
Why we picked it
Azure SQL Database is the best choice if you're already locked into the Microsoft ecosystem. It offers near-perfect compatibility with SQL Server, making migration trivial, and its built-in high availability and automated backups are genuinely hands-off. The catch is pricing — it gets expensive fast with higher tiers, and you're tied to Azure's networking quirks. For a pure SQL Server experience without managing VMs, it beats Amazon RDS for SQL Server on feature parity but loses on cost flexibility.
→ Pick it when your stack runs on Azure or you're migrating an existing SQL Server workload and want the smoothest possible lift-and-shift to the cloud.
Pros
- +Fully managed with automated backups and high availability
- +Built-in intelligence for performance tuning and security
- +Supports serverless compute and Hyperscale for massive scalability
Cons
- -Can get pricey for high-performance workloads
- -Limited to Microsoft SQL Server compatibility
AWS's database that makes you feel fancy without the price tag of Oracle, but still costs more than your rent.
Why we picked it
Aurora delivers MySQL and PostgreSQL compatibility with 5x the throughput of standard MySQL and 3x that of standard PostgreSQL, while claiming up to 1/10th the cost of commercial databases. Its auto-scaling storage up to 128 TB and six-way replication across three AZs make it the most resilient and performant option on AWS. The closest competitor, RDS, can't match the performance or failover speed without manual tuning, and Google Cloud SQL lacks the same storage auto-scaling and replication guarantees.
→ Use it when you're on AWS, need MySQL or PostgreSQL compatibility with enterprise-grade performance and availability, and want to avoid the operational overhead of manual scaling or replication management.
Pros
- +Fully managed with automatic scaling, backups, and patching
- +Up to 5x MySQL and 3x PostgreSQL performance with cloud-optimized storage
- +High availability and durability through multi-AZ replication
- +MySQL and PostgreSQL compatibility for easy migration
Cons
- -Can get expensive quickly with scaling and I/O costs
- -Vendor lock-in to AWS ecosystem
- -Limited to AWS regions, which might affect latency for global apps
MySQL with a Microsoft hug—managed so you don't have to babysit your database.
Pros
- +Fully managed with automated backups and patching
- +High availability built-in with flexible server options
- +Seamless integration with other Azure services
- +Strong security features like encryption and firewall rules
Cons
- -Can get pricey compared to self-hosted MySQL
- -Limited control over underlying infrastructure
The cockroach of databases: hard to kill, spreads everywhere, and surprisingly good at SQL.
Pros
- +Strong consistency across distributed nodes without manual sharding
- +PostgreSQL wire protocol compatibility for easy migration
- +Automatic data replication and rebalancing for high availability
Cons
- -Higher latency compared to single-node databases due to distributed overhead
- -Complex licensing and pricing can be a headache for scaling
Head-to-head comparisons
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