Calculate data deduplication ratio and space savings from logical and physical data sizes. Plan backup and storage efficiency.
Data deduplication eliminates redundant copies of data, storing only unique blocks or chunks. In backup environments, deduplication ratios of 10:1 to 50:1 are common because multiple backup generations contain mostly identical data. In primary storage, ratios of 1.5:1 to 3:1 are typical depending on the workload.
This calculator computes the deduplication ratio from logical data size (total data before dedup) and physical data size (actual storage consumed after dedup). It shows the ratio, percentage of space saved, and the effective storage efficiency. Use it for evaluating backup appliances, planning deduplicated storage arrays, or estimating the benefit of enabling dedup on existing systems.
Understanding your deduplication ratio is essential for capacity planning. A 20:1 ratio means 100 TB of logical data consumes only 5 TB of physical storage—dramatically reducing hardware costs, power consumption, and data center footprint.
Quantifying this parameter enables systematic comparison across environments, deployments, and time periods, revealing optimization opportunities that improve both performance and cost-effectiveness.
Deduplication can reduce storage needs by 50–98%, but actual ratios depend on your data. This calculator quantifies your exact savings so you can make informed decisions about dedup-capable storage investments. This quantitative approach replaces reactive troubleshooting with proactive monitoring, enabling engineering teams to maintain service level objectives and minimize unplanned system downtime.
ratio = logical_size / physical_size; space_saved_pct = (1 − physical_size / logical_size) × 100
Result: 20:1 ratio; 95% space saved
100 TB logical / 5 TB physical = 20:1 dedup ratio. Space saved: (1 − 5/100) × 100 = 95%. Only 5 TB of physical storage is needed to hold 100 TB of backup data. At $0.023/GB, this saves $2,185/month.
Backup dedup is extremely effective because daily incremental backups share 95–99% of their data with previous backups. A 30-day backup window with daily incrementals can achieve 20–50× ratios. Weekly fulls with daily incrementals achieve even higher ratios.
Primary storage dedup is less dramatic (1.5–5×) but still valuable. File shares with many copies of templates, presentations, and documents benefit most. Databases with normalized data see minimal benefit from dedup.
Dedup reduces logical data to physical storage, but RAID adds overhead to physical storage. A 20× dedup ratio on 100 TB logical = 5 TB physical. With RAID 6 overhead (~30%), actual disk = 6.5 TB. Include RAID overhead in your capacity planning.
For backups: 10–50× is common. For VDI: 20–70×. For file servers: 2–5×. For databases: 1.5–3×. Ratios depend heavily on data redundancy—more redundant data yields higher ratios.
Inline dedup checks for duplicates before writing data to disk, saving storage immediately but using more CPU during writes. Post-process dedup writes data first, then deduplicates in a background job. Inline is preferred for backup appliances; post-process suits primary storage.
Slightly. Deduplicated reads may require reassembling data from multiple unique blocks, adding latency. Modern dedup systems use caching and metadata optimization to minimize this impact. Write performance impact depends on inline vs. post-process.
Yes, and you should. Dedup eliminates duplicate blocks; compression reduces the size of unique blocks. Together, they can achieve much higher total data reduction than either alone. Most storage systems apply dedup first, then compression.
Logical size is the total size applications see—the sum of all files and data as if no dedup existed. Physical size is the actual disk space consumed after dedup removes redundant copies. The ratio between them is the dedup ratio.
Smaller block sizes (4–64 KB) find more duplicate blocks, yielding higher ratios. Larger blocks (128 KB–1 MB) are faster to process but find fewer duplicates. Variable-length chunking adapts to data boundaries and typically outperforms fixed-length.