Example data table
| Scenario | Tables | Rows/table | Row size | Replicas | Backups | Forecast | Estimated storage |
|---|---|---|---|---|---|---|---|
| Small SaaS | 50 | 200,000 | 600 B | 2 | 2 full + 14 incr | 12 months | ≈ 38.06 GiB |
| Analytics store | 200 | 1,000,000 | 1.2 KiB | 3 | 7 full + 30 incr | 18 months | Varies with compression and growth |
Examples are illustrative; measure real row and index sizes for accuracy.
Formula used
This calculator models storage as a sum of data, indexes, operational overhead, retained logs, replicas, and backups. It also applies compound growth and optional compression.
| Step | Computation |
|---|---|
| Raw data | Raw = Tables × RowsPerTable × RowSizeBytes |
| Indexes + engine | Base = Raw × (1 + Index% + Engine%) |
| Free space + logs | Initial = Base × (1 + Free%) + (LogsPerDay × RetentionDays) |
| Forecast (compound) | Forecast = Initial × (1 + MonthlyGrowth%)^Months |
| Compression | Compressed = Forecast × (1 − CompressionReduction%) |
| Live replicas | Live = Compressed × ReplicationCopies |
| Backups | Backups = (Compressed × FullBackups) + (Compressed × DailyChange% × IncrementalDays) |
| Total | TotalStorage = Live + Backups |
Actual usage varies by database engine, page size, fill factor, indexing strategy, and vacuum/compaction behavior.
How to use this calculator
- Start with tables, average rows, and a realistic row size.
- Add index and engine overhead based on your schema and engine.
- Set reserved free space for maintenance and peak load periods.
- Include retained logs if you keep WAL/binlogs for recovery.
- Apply growth and forecast months to plan ahead.
- Set compression, replication copies, and backup retention policies.
- Click Calculate, then download CSV or PDF.
Capacity drivers: tables, rows, and row size
Raw data volume starts with Tables × Rows × RowSize. For example, 50 tables with 200,000 rows each and a 600‑byte average row produce about 6.0 GB of raw data. If you do not know row size, estimate it from column payloads plus row overhead for headers, alignment, and null maps. Measure with a representative sample, not an empty database, because real values include variable strings and sparse columns.
Index and engine overhead ranges
Indexes commonly add 20–80% on transactional workloads, depending on how many secondary indexes you maintain and whether keys are wide. Engine overhead (pages, catalogs, MVCC/undo, allocation maps, vacuum space) often adds another 5–20%. If you tune fill factors to reduce page splits, the database may use more space but write faster. Start with 30% indexes and 12% engine overhead, then refine using actual reports. For analytics, overhead may be lower, but text search, multi-column indexes, and frequent updates can push it higher quickly in large tenant databases.
Forecast growth with compound math
Growth is rarely linear. This calculator compounds monthly growth, so 3% per month becomes about 1.43× after 12 months, while 8% becomes about 2.52×. Add reserved free space (often 15–25%) to keep performance stable during reindexing, vacuum, and bursts. Many teams alert at 70–75% used so scaling happens before maintenance windows become risky.
Replication and availability multipliers
High availability multiplies live storage. If you run one primary plus two replicas, the same dataset exists three times, before backups. Use the replication copies field to model what you pay for. Extra replicas reduce query latency but increase storage and backup time. Keep replica count aligned with recovery objectives and read scaling needs.
Backups, logs, and retention economics
Backups are often the hidden majority of storage. Two retained full snapshots consume roughly 2× the compressed dataset. Incrementals depend on churn: a 5% daily change rate retained for 14 days adds about 70% more. Long WAL/binlog retention improves point‑in‑time recovery, but it can grow quickly on write-heavy services. Review retention quarterly and align it with compliance and restore testing.
FAQs
How do I find a realistic average row size?
Use a production-like sample. Divide table data size by row count, and include variable columns and null patterns. Empty tables underestimate strings, JSON payloads, and index key widths.
What index overhead percentage should I start with?
Start with 30% for typical OLTP schemas, then adjust using real index size reports. Heavily indexed tables, wide composite keys, and full‑text indexes can push overhead above 80%.
Why are binary and decimal totals different?
Binary units use powers of 1024 (GiB), while decimal units use powers of 1000 (GB). Cloud storage bills often use decimal, but operating systems commonly report binary.
Does the compression reduction apply to backups as well?
Yes, this model applies compression before replication and backups, assuming backups store compressed pages or blocks. If your backup tool re-compresses differently, use a more conservative compression percentage.
How should I set daily change rate for incrementals?
Estimate the portion of data that changes each day: inserts, updates, and deletes. For steady OLTP systems 2–8% is common; batch loads and reprocessing jobs can be much higher.
Can I use this for NoSQL or warehouse systems?
Yes for planning, but tune assumptions. Columnar warehouses compress more, while document stores may have larger row payloads. Measure real sizes, then adjust overhead, growth, and retention for your platform.