Comparing Managed Postgres Costs: Supabase and Amazon RDS

Fill in one column per option, using current published rates and quotes rather than memory:

• Compute: instance size or plan, and whether a bigger tier is needed at peak.
• Storage: current data size, growth per month, and any provisioned performance settings.
• Backups and recovery: included retention, point-in-time recovery, and the cost of extra retention or off-account copies.
• High availability: a standby instance in another zone is billed as additional capacity. Decide whether you need it and price it for both.
• Read replicas: count them and their sizes.
• Data transfer: traffic leaving the platform, including to your app servers if they run elsewhere.
• Connection pooling and proxies: included or extra.
• Adjacent services: authentication, file storage, generated APIs and realtime, priced at what you'd pay if you didn't get them here.
• Support plan and staff time: the price of help, plus hours spent on upgrades, tuning and incidents.

Skipping the last two rows is the usual reason a cheaper-looking option turns out to cost more.

Say your app has a 50 GB database growing 5 GB a month, moderate traffic, and needs high availability. You also need user authentication and file storage. Now lay out two columns using round placeholder figures, since real rates change.

In the RDS column you might enter a database instance, a standby copy, storage and backups, then add lines for an identity provider and an object store because RDS doesn't include them. In the Supabase column you'd enter a plan that includes the database, auth and storage up to certain limits, then check what an overage would cost at your projected growth. Suppose the RDS total looks 30 percent lower on the database rows alone but adds two other subscriptions and four hours a month of maintenance. Once you price those in, the gap may narrow or reverse. The point of the example is the method: compare complete solutions, not database rows in isolation.

Today's bill matters less than the one after growth. Extend the worksheet:

1. Estimate storage and traffic growth per month from your own history, not a generic curve.
2. Note the step points, such as when you outgrow a plan tier or need a larger instance class.
3. Add planned features that change load: search, analytics queries, background jobs.
4. Price the next tier up for each platform and see where the lines cross.
5. Add a contingency for spikes and for a replica if reporting queries start to compete with production traffic.

Platform limits also matter as much as prices, such as maximum connections, supported extensions and available regions. Confirm them in documentation and a demo before committing.

Watch these:

• Data transfer. Running the app in a different region or provider from the database can add charges and latency.
• Backups kept longer than the plan includes, or copies stored in a second account.
• Idle non-production databases. Staging and development copies add up when each has its own instance.
• Storage that only grows. Bloat, old logs and unused tables inflate the bill until someone cleans up.
• Migration cost later. Choosing a platform that's hard to leave has its own price; see Supabase to AWS RDS migration.

If you'd rather see the technical tradeoffs alongside price, read MongoDB Atlas vs AWS RDS vs Supabase and Supabase vs AWS RDS for tech startups.

Use cost as one input, not the verdict. Ask who will operate the database at 2 a.m., how strict your recovery targets are (the Postgres backup checklist helps set them), whether you need private networking, and how much you value bundled services against control. A small team shipping fast often values the bundle; a team with strict network or compliance needs often values the control.

Revisit the comparison once a year or when usage doubles. Other line items deserve the same worksheet treatment: see the GitHub Actions minutes calculator and the Auth0 pricing calculator, which cover the CI and identity parts of the bill.