Estimate monthly cost of storing your database's data across AWS RDS, Aurora, Google Cloud SQL, MongoDB Atlas, DynamoDB, and self-managed block storage — accounting for index overhead and replication.
Enter your details and click Calculate to see results
Database storage cost is easy to underestimate because the number that matters isn't your raw row count times row size — it's that figure plus index overhead plus however many replicas your provider keeps for durability and availability. This database storage cost calculator walks through that full chain and prices the result across six common managed and self-managed storage options, so you can compare AWS RDS, Aurora, Google Cloud SQL, MongoDB Atlas, DynamoDB, and plain block storage on equal footing for the same dataset.
It computes raw data size from rows × row size, adds an index overhead percentage, multiplies by a replication factor, and prices that final total against six providers' published per-GB-per-month storage rates.
Backend engineers choosing a managed database provider, startup founders projecting database infrastructure costs, DBAs planning capacity for a growing production dataset, and anyone comparing relational (RDS, Aurora, Cloud SQL) against NoSQL (MongoDB Atlas, DynamoDB) storage pricing need this row-count-to-cost math.
Storage pricing differences of 2-3× between providers compound quickly at scale — a 500 GB dataset costs roughly $40/month on self-managed block storage but over $125/month on MongoDB Atlas or DynamoDB, purely on storage. Factoring in index overhead and replication before comparing providers avoids the common mistake of budgeting only for raw data size and being surprised when the real bill (with indexes and replicas) turns out 50-100% higher than expected.
How this calculator turns row count into a monthly storage bill
1 GB here means 1,073,741,824 bytes (1024³), the binary gigabyte most cloud storage metering uses internally.
Raw data size is simply rows × average row size. Wide tables with many columns, large text fields, or JSON blobs push average row size well above the typical 200-500 byte range.
Every index is a separate data structure stored alongside your table, typically adding 20-40% more storage — a real, ongoing cost worth weighing against query performance gains.
Keeping 2 or 3 live copies of your data across nodes protects against node failure, but directly multiplies the storage you're billed for.
From entering your row count to comparing all 6 providers
Enter the total number of rows your database table (or your whole database) currently holds, or expects to hold.
Enter the average size of one row in bytes — typical application rows run 200-500 bytes depending on column count and data types.
Enter the percentage of extra storage your indexes consume on top of raw table data, typically 20-40% for a moderately indexed table.
Select 1 for a single copy with no extra replicas, or 2-3 for high-availability setups that keep live replica copies of your data.
Click "Calculate Storage Cost" to see your total storage size and a side-by-side monthly cost comparison across AWS RDS, Aurora, Google Cloud SQL, MongoDB Atlas, DynamoDB, and self-managed block storage.
Using the calculator's own default scenario — 10 million rows, 300 bytes/row, 30% index overhead, replication factor 1
Suppose your table has 10,000,000 rows averaging 300 bytes each, with 30% index overhead and no extra replicas.
Explanation: At this modest 10-million-row scale, the absolute dollar spread between cheapest and priciest is small ($0.29 vs $0.91/month), but the ratio — over 3× — holds at any scale, so a 1TB dataset would see roughly $80 vs $250/month purely on storage. This is why comparing providers before committing matters more as data grows, not less.
What your projected storage bill generally implies
| Total Storage | What It Generally Means | Recommended Next Step |
|---|---|---|
| Under 10 GB | Small app or MVP-scale database | Storage cost is negligible; optimize for developer experience instead |
| 10 – 100 GB | Small production database | Compare providers now, before switching becomes disruptive |
| 100 GB – 1 TB | Meaningful production dataset | Evaluate index overhead and replication settings for savings |
| 1 – 10 TB | Large-scale production database | Consider self-managed or Aurora for the storage savings at this scale |
| Over 10 TB | Very large dataset | Negotiate enterprise pricing; consider data archival/partitioning strategies |
If index overhead is a large share of your total: review whether every index is actually used by your query patterns — unused indexes are pure storage cost with no query benefit.
If replication is doubling or tripling your bill: confirm your provider doesn't already bundle a baseline replica into its standard storage price before adding your own multiplier on top.
This is a storage-only estimate. Always add compute, I/O, backup, and data transfer costs for a full monthly budget.
This calculator provides planning estimates only. Actual charges depend on your provider's live pricing, region, and any negotiated enterprise rate on your account.
Where comparing managed database storage pricing up front genuinely helps
Compare storage-only pricing across managed and self-managed relational options.
Project storage cost as user count and data volume grow over the next year.
Model the storage impact of adding replication for high availability.
Combine with an estimated backup overhead percentage for a fuller cost picture.
Compare MongoDB Atlas or DynamoDB storage pricing against RDS or Aurora.
Project storage cost 6-12 months out based on expected row-count growth.
Compare current provider cost against alternatives before a migration decision.
Use it in a course to make index overhead and replication cost tradeoffs concrete.
Quantify the storage cost of indexes when deciding whether to drop unused ones.
Estimate per-tenant database storage cost for a multi-tenant SaaS pricing model.
Project storage cost for a mobile app's user and event data growth.
Bring a concrete storage cost comparison into a database vendor evaluation.
What this database storage cost calculator does well, and where it can't replace a live quote
Approximate per-GB-per-month storage rates used by this calculator
| Provider | $/GB/month | Type | Best For |
|---|---|---|---|
| Self-Managed (block storage) | $0.08 | Self-hosted | Teams with ops capacity wanting lowest storage cost |
| AWS Aurora | $0.10 | Managed relational | MySQL/PostgreSQL-compatible with distributed storage |
| AWS RDS (gp3) | $0.115 | Managed relational | Standard managed relational databases |
| Google Cloud SQL | $0.17 | Managed relational | GCP-native relational workloads |
| MongoDB Atlas | $0.25 | Managed document DB | Document-oriented, flexible-schema workloads |
| DynamoDB Standard | $0.25 | Serverless NoSQL | Key-value workloads needing auto-scaling |
Summary: This database storage cost calculator gives you an instant, free comparison of managed database storage pricing across 6 providers, correctly accounting for index overhead and replication rather than just raw data size. Pair it with the Cloud Storage Cost Calculator and Cloud Cost Calculator for a complete infrastructure budget.
Common questions about database storage cost calculator estimates
Official documentation to complement this calculator
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