Compression Ratio Calculator

Enter both sizes in the same unit. The ratio is unit-independent, so as long as they match the result is correct. For a 200 MB dataset that compresses to 50 MB, at a typical object-storage rate of $0.023 per GB per month and a 100 Mbps connection:

Ratio = 200 ÷ 50 = 4.00:1. Space saving = 75.00%. The compressed file is 25% of the original, and 150 MB (150,000,000 bytes) is recovered.

That translates to 0.15 GB of storage, worth $0.003 a month. That is negligible, which is the honest answer for a single file. Transfer is where the value is at this scale: at 100 Mbps the original takes 16.0 seconds and the compressed file 4.0 seconds, a saving of 12.0 seconds every time it moves.

That last point is the general one. For a single archive, compression is almost never about storage cost; it is about the time and bandwidth spent moving the thing, multiplied by how often you move it.

Scale changes which half matters. Take a 2 TB backup set that compresses to 640 GB, at the same $0.023 per GB per month, transferred over a 1 Gbps link.

Ratio = 2,000 ÷ 640 = 3.13:1, a 68.00% space saving, recovering 1,360 GB. At $0.023 per GB that is $31.28 a month and $375.36 a year, a real, recurring line item rather than a rounding error.

Transfer improves in the same proportion: at 1 Gbps the uncompressed set takes 16,000 seconds (4 hours 27 minutes) against 5,120 seconds (1 hour 25 minutes) compressed, saving 10,880 seconds, just over 3 hours on every full transfer.

Note that the ratio here (3.13:1) is *lower* than the single archive's 4:1, and yet this is the case where compression obviously pays. Ratio alone tells you nothing about value; ratio multiplied by volume and by how often you store and move the data is what decides it.

Compression works by removing redundancy, so how well it works is a property of the data rather than of the algorithm. Rough expectations: plain text, logs, and CSV compress extremely well, frequently 5:1 to 10:1 or beyond, because natural language and structured records repeat heavily. Source code and JSON typically land around 3:1 to 5:1. Database dumps and uncompressed images vary widely with content.

Already-compressed formats barely move. JPEG, PNG, MP4, MP3, and ZIP files have had their redundancy removed already, and running a general-purpose compressor over them usually yields under 1.1:1, sometimes slightly *larger* output, because the container adds a header to data it could not shrink. Compressing an archive of photos or video is one of the most common wasted operations in backup pipelines, and the calculator will show it plainly: enter a ratio near 1 and the savings collapse to nothing. The same holds for a PDF made from photos: it is about the size of the JPGs inside it, so the way to shrink one is to resize and recompress the pictures, which is what the Smaller file setting in the JPG to PDF converter does. For a PDF that already exists, Compress PDF shows both sides of this: its lossless clean-up saved 0% on a 40.7 MB scan made of JPEG pages, while recompressing those pages at its Balanced setting brought the same file to 8.1 MB, about 5:1.

The trade-off this calculator does not price is CPU time. Higher compression levels cost disproportionately more processor time for progressively smaller gains, the same diminishing-returns curve as the ratio itself, viewed from the other side. A modern fast codec at a moderate level usually beats a slow one at maximum, once you count the time spent compressing and decompressing on every access.

To decide whether a saving is worth having, size the underlying data first: the data storage calculator turns file counts into a GB total, the cloud storage plan comparison calculator prices the storage you would otherwise buy, and the download time calculator covers the transfer side for a specific file and connection.

This calculator performs exact arithmetic on the two sizes you enter. It cannot predict what ratio your data will achieve: that depends entirely on the content and the algorithm, and the only reliable way to know is to compress a representative sample and measure it. Published benchmark ratios are measured on standard corpora that may look nothing like your files.

It prices storage and transfer but not compute. Compression costs processor time on write and decompression costs it on every read, which on hot data accessed constantly can outweigh the storage saved. It also does not model the difference between lossless and lossy compression: a 10:1 ratio from a lossless algorithm and a 10:1 ratio from re-encoding an image at lower quality are the same number describing entirely different trade-offs, and only one of them is reversible. Compress Image lets you try both on one picture: its lossless PNG setting keeps every pixel identical, while its JPG, WebP and AVIF quality settings give up detail for size, with a compare view to judge the loss.

The storage saving assumes a flat per-GB rate. Real storage pricing has tiers, minimum object sizes, retrieval fees on archive classes, and per-request charges, and compression that produces many small objects can cost more in requests than it saves in bytes. Transfer time assumes the full line rate with no protocol overhead, latency, or contention, so treat it as a best case rather than a prediction.

Size the data before deciding what compression is worth: the Data Storage Calculator turns photo, video, and document counts into a GB total, and the Video File Size Calculator covers media specifically, the category where compression is already applied and further gains are minimal. The Cloud Storage Plan Comparison Calculator prices the storage a good ratio saves you from buying, and the Download Time Calculator estimates transfer time for a specific file and connection speed.