Aws Documentdb Pricing Calculator |top| May 2026

If you are migrating from open-source MongoDB to AWS DocumentDB (a purpose-built database for JSON data), you quickly realize one truth: Performance is elastic, but costs can be rigid. Unlike Amazon S3, where pricing is simple storage arithmetic, DocumentDB pricing is a multi-dimensional chess game involving instances, IOPS, storage, and backups.

"I need to retain 30 days of change logs." Enter 2,000GB. The calculator adds that cost. Most users forget this, then cry when the bill arrives. Common Pitfalls (And How the Calculator Saves You) | Pitfall | Calculator Fix | | :--- | :--- | | Forgetting Data Transfer | The calculator has a "Data Transfer" tab. If you query DocumentDB from EC2 in different AZs, you pay cross-AZ fees. Add those here. | | Assuming 100% Utilization | The calculator defaults to "Always On" (730 hours/month). For dev environments that shut down at night, use the "Partial month" toggle. | | Mixing Instance Families | Your primary can be r5.large but your read replica can be r5.xlarge . The calculator allows asymmetric clusters. Use it. | Final Verdict: Is the Calculator Good Enough? Yes, but only if you have metrics. aws documentdb pricing calculator

The calculator assumes you are constantly at that number. If you bulk load 2TB of data for one day then delete it, the calculator won't catch that nuance—you must manually adjust. The true power of the AWS DocumentDB calculator is scenario modeling. Scenario A: The Serverless Gamble AWS DocumentDB recently added Serverless (v4.0). Instead of picking r5.large , you pick "Serverless" and define a capacity range (0.5 to 16 ACUs—Application Capacity Units). If you are migrating from open-source MongoDB to

If you are guessing your I/O rate ("Uh, maybe 500 IOPS?"), the calculator is worthless—garbage in, garbage out. However, if you export CloudWatch metrics from a staging environment (e.g., DatabaseCursors , ReadIOPS , WriteIOPS ), the calculator becomes a crystal ball. The calculator adds that cost

MongoDB compatibility. Serverless scalability. Enterprise price tags.