🗄️ Database Storage Cost Calculator

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.

🗄️ Database Size Inputs
Typical application row size is ~200-500 bytes, depending on column count and data types.
Indexes typically add 20-40% on top of raw table data; more with heavy indexing or full-text search.
Higher replication factors improve availability and durability but multiply stored data (and often cost).
📈 Storage Cost Estimate
Total Storage (with index & replication)
Raw Data Size
With Index Overhead
Cheapest Provider
Cheapest Monthly Cost

📊 Monthly Cost by Provider

Monthly Cost Comparison
⚠️ Prices updated June 2026 and reflect storage-only list pricing (per GB/month) — actual bills also include compute, I/O, backups, and data transfer, and rates vary by region. Verify current pricing on each provider's page before budgeting.
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Enter your details and click Calculate to see results

Guide

About the Database Storage Cost Calculator

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 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.

How It Works

Enter your number of rows and average row size in bytes to get raw data size. The calculator then adds your index overhead percentage (default 30%, typical for moderately indexed tables) to account for the extra storage indexes consume, and multiplies the result by your replication factor (1 for a single copy, 2 or 3 for high-availability setups with live replicas). That final "total size" figure is then multiplied by each provider's published per-GB-per-month storage rate — AWS RDS gp3 ($0.115/GB), Aurora storage ($0.10/GB), Google Cloud SQL ($0.17/GB), MongoDB Atlas dedicated tier (~$0.25/GB), DynamoDB standard ($0.25/GB), and self-managed block storage ($0.08/GB) — to produce a side-by-side monthly cost comparison.

Why It Matters

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.

Tips for Accurate Results

  • Index overhead varies widely by workload — a handful of simple B-tree indexes might only add 15-20%, while multiple composite indexes or full-text search indexes can add 50% or more.
  • Only apply a replication factor above 1 if your billing model actually charges you for replica storage — some managed services (like Aurora) include a baseline of replication in the storage price already.
  • This calculator estimates storage cost only — compute (instance hours), I/O operations, backup storage, and data transfer are typically separate line items on your actual bill.
  • Re-check current provider pricing periodically, since storage rates do change and can vary by region, storage tier, and volume-based discounts.
  • For rapidly growing datasets, model your projected row count 6-12 months out, not just today's size, to avoid under-budgeting for storage growth.
About

Understanding Database Storage Cost

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Row Size & Row Count

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, directly increasing storage cost.

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Index Overhead

Every index you create is a separate data structure stored alongside your table, typically adding 20-40% more storage. Indexes speed up reads dramatically, but each one has a real, ongoing storage cost worth weighing against the query performance it buys.

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Replication for HA

Keeping 2 or 3 live copies of your data across nodes protects against node failure and data loss, but directly multiplies the storage you're billed for. Weigh your uptime requirements against the added storage cost when choosing a replication factor.

FAQ

Frequently Asked Questions

Common questions about database storage cost

How is database storage cost calculated?
Raw data size = number of rows × average row size. This calculator then adds index overhead (typically 20-40% of raw data, default 30%) and multiplies by your replication factor (1 for no extra replicas, 2 or 3 for HA setups), then multiplies the resulting total GB by each provider's per-GB-per-month storage price.
What is a typical average row size?
Most application database rows fall between roughly 200 and 500 bytes, depending on column count and data types. Rows with large text fields, JSON blobs, or many columns can be several kilobytes; narrow tables with just a few integers and short strings can be under 100 bytes.
Why does index overhead matter?
Indexes speed up queries but consume additional storage — often 20-40% on top of the raw table data, and sometimes more with many indexes or full-text search indexes. Ignoring index overhead when estimating storage cost typically underestimates your real bill.
Why would I choose a replication factor greater than 1?
Replication factor 2 or 3 keeps additional live copies of your data across nodes or availability zones for high availability and durability. Most managed database services replicate by default for durability, and some pricing models bill you for that extra storage — factor it in when the provider does.
Why do storage prices vary so much between providers?
Managed database services bundle storage pricing with different underlying hardware, redundancy guarantees, and operational tooling. Fully managed document databases (MongoDB Atlas) and NoSQL services (DynamoDB) tend to price storage higher than raw block storage (self-managed, AWS Aurora) because of the added management layer, automatic scaling, and built-in redundancy.

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