Storage Capacity Calculator
The formula
The three multipliers everyone forgets
A capacity plan that counts only today's logical data is usually wrong by a factor of three to five. Three multipliers apply on top of the raw figure. Replication — distributed storage keeps whole copies, with 3× standard for Ceph and HDFS defaults. Protection overhead — RAID 6 or erasure coding consumes 17–40% depending on layout. Filesystem and metadata — block rounding, inodes, journals and snapshots typically take 5–15%. Then divide by your maximum fill level, because an array above 80% full fragments, loses sequential performance and has no room to rebuild a failed disk. Each step is individually defensible; skipping any one produces a system that fills up two years early.
Growth compounds, and archives never shrink
At 28% annual growth, 85 TB becomes 292 TB in five years — a 3.4× increase that no linear forecast predicts. The compounding is also asymmetric: data is cheap to create and expensive to delete, and in practice almost nobody deletes. Before buying capacity, ask whether tiering can absorb the growth instead. Moving cold data to an archive tier — object storage, tape or high-density nearline — usually costs a fifth of keeping it on primary flash. A five-year plan that treats all data as equally hot is a five-year plan to overspend.
Why you should never plan to 100% full
Two hard reasons. First, performance: most filesystems and arrays degrade sharply once free space drops below 15–20%, because allocation becomes fragmented and background tasks (scrubbing, balancing, snapshots) contend for the same blocks. Second, resilience: rebuilding a failed 16 TB disk needs somewhere to put 16 TB of data. An array that is exactly full cannot rebuild at all, which converts a routine disk swap into a data-loss event. Thin provisioning makes this worse rather than better, because it hides the problem until it is too late to fix.
Frequently asked questions
How much free space should I leave?
Does deduplication change the maths?
What is the difference between TB and TiB?
Should I buy for five years at once?
Source & methodology
Capacity-planning practice per SNIA storage-networking guidelines; growth rates from IDC Global DataSphere estimates; unit definitions per IEC 80000-13.
Storage Capacity explained: formula, worked example and honest limits
You are counting something that matters — this tool counts it exactly, including the edge cases calendars hide. Searchers land on a Storage Capacity page for three reasons — to plan, to compare options, and to check that somebody else’s arithmetic is honest. This tool serves all three, and everything after the calculator explains the model instead of hiding it.
Most confusion about Storage Capacity comes from mixing up the input you control with the input you merely observe. The formula treats them identically, but your decisions should not: one deserves measurement, the other deserves a defensible estimate.
Systems math in Technology & Computing has a peculiar property: it feels precise until an assumption breaks silently. Storage Capacity is exact arithmetic over provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉ — but only as truthful as the model. A wrong assumption about Current data volume (TB) will not throw an error; it will just quietly produce the wrong 1,204.7 TB.
Nothing below is stock text pasted around a number: the worked example computes capacity to provision = 1,204.7 TB from this tool’s own defaults, and the sensitivity section ranks the inputs that move the result — Planning horizon first, at ≈21.9 % of the headline for a ten-percent error either way in that input. Read the sections in order and you will be able to reproduce every figure by hand.
How Storage Capacity is computed, line by line
One formula, published in full: provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉. Anything you can read, you can reproduce in a spreadsheet, and that reproducibility is the whole point of printing it rather than hiding it behind a button.
Current data volume (TB) (example value 85) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Count from the record, not the estimate; whole-number inputs are cheap to verify and expensive to get wrong.
Annual data growth (%) (example value 28) is a measured number in the unit named by the label; the formula does not convert for you. Rates are the most mis-transcribed input on any page like this; copy it from the document rather than retyping from memory.
Planning horizon (years) (example value 5) is a numeric input whose unit is fixed by the label; a wrong unit is a wrong answer wearing a plausible disguise. If the period is partial, decide whether you mean elapsed or planned time and keep that meaning across every field.
Replication factor (copies) (example value 3) is a quantity rather than a category — precision here is cheap and worth having. Replace the example with the figure from your own records — the tool recomputes instantly and the printed steps follow your numbers.
RAID / erasure-coding overhead (%) (example value 0) is a plain measured quantity — the unit shown in the label is the unit the formula expects. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Filesystem and metadata overhead (%) (example value 10) is a measured number in the unit named by the label; the formula does not convert for you. Check the convention first: if the source says “6.5 % per year”, type 6.5 and let the formula do the rest.
Maximum fill level before performance degrades (%) (example value 80) is a quantity rather than a category — precision here is cheap and worth having. Enter it exactly as your document states it — quoted annual, monthly and effective rates are three different numbers.
Disk size you intend to buy (TB) (example value 16) is a measured number in the unit named by the label; the formula does not convert for you. Measure twice and use the unit the label asks for — length terms often scale the result geometrically.
Cost per TB (installed) (example value $95.00) is a currency figure — enter the total exactly as your document states it, fees included. If your source quotes a figure before and after some deduction, decide which one the formula wants and be consistent for every field.
Expected deduplication/compression ratio (example value 1) is a plain measured quantity — the unit shown in the label is the unit the formula expects. The example value is there to demonstrate the mechanics, not to describe your situation — overwrite it.
Storage Capacity on the example inputs: the complete substitution
The tool prints its own working; this is the same arithmetic on the example values, reproduced so you can follow it by hand. Starting figures: Current data volume (TB), Annual data growth (%), Planning horizon (years), Replication factor (copies). Run to the end, the capacity to provision resolves to 1,204.7 TB — 76 × 16 TB disks for 5 years of growth on the bands this tool uses.
- Logical data in 5 years = current × (1 + growth)^years = 85.0 × (1 + 28%)^5 = 292.1 TB.
- Overhead multiplier = 3 copies × (1 + 0%) × (1 + 10%) ÷ 1 dedup = 3.300×.
- Physical requirement = 292.1 × 3.300 = 963.8 TB.
- Provisioned capacity = physical ÷ fill level = 963.8 ÷ 80% = 1,204.7 TB.
- Disks = ⌈1,204.7 ÷ 16⌉ = 76; capital cost = 1,204.7 TB × $95 = $114,450.
Logical data in 5 years — 292.1 TB. Compare it against the main result of 1,204.7 TB before drawing conclusions.
Physical requirement — 963.8 TB. This is the number experienced users quote when the headline alone would mislead.
Including fill-level headroom — 1,204.7 TB. This is the number experienced users quote when the headline alone would mislead.
Disks required — 76. This is the number experienced users quote when the headline alone would mislead.
Estimated capital cost — $114,450. This is the number experienced users quote when the headline alone would mislead.
Overwrite any input above and every line of this example updates with it — the substitution is live, not a screenshot of somebody else’s numbers.
The Storage Capacity corridor: your result across a realistic band
Read this table as the practical error bar on the headline figure. It answers the question people actually have after seeing a single number: how wrong could this be if the main input is off by ten or twenty percent?
| Planning horizon setting | Capacity to provision |
|---|---|
| 80 % of the example value | 941.2 TB |
| 90 % of the example value | 941.2 TB |
| example inputs | 1,204.7 TB |
| 110 % of the example value | 1,204.7 TB |
| 120 % of the example value | 1,542.1 TB |
The corridor runs from 941.2 TB to 1,542.1 TB across that band — which is the range worth planning inside rather than the single 1,204.7 TB point estimate. Reproduce it in the form above by editing Planning horizon alone; the substitution steps will follow your figures exactly.
The levers behind Storage Capacity, ranked by effect
Generic advice says “it depends on your inputs”; this section says exactly how much. Each field was perturbed by ±10 % in isolation and the capacity to provision recomputed, so the ranking below is measured rather than asserted. For Storage Capacity the ranking is measured rather than guessed, and it belongs to these defaults: shift the starting point and the order can shift with it.
- Nudge Planning horizon by ±10 % and Storage Capacity lands between 941.2 TB and 1,204.7 TB (sensitivity ≈21.9 %)
- Annual data growth +10 % → Storage Capacity = 1,342.4 TB · −10 % → 1,078.6 TB (the capacity to provision shifts ≈11.4 %)
- Nudge Maximum fill level before performance degrades by ±10 % and Storage Capacity lands between 1,095.2 TB and 1,338.6 TB (sensitivity ≈11.1 %)
- Expected deduplication/compression ratio +10 % → Storage Capacity = 1,095.2 TB · −10 % → 1,338.6 TB (the capacity to provision shifts ≈11.1 %)
- Nudge Current data volume by ±10 % and Storage Capacity lands between 1,084.3 TB and 1,325.2 TB (sensitivity ≈10.0 %)
- Nudge Replication factor by ±10 % and Storage Capacity lands between 1,084.3 TB and 1,325.2 TB (sensitivity ≈10.0 %)
Planning horizon is the dominant lever here: ten percent either way moves the result by about 21.9 %, from 941.2 TB to 1,204.7 TB. Spend your verification effort there first, and treat Annual data growth as the second priority (≈11.4 % per ten percent). The rest of the inputs earn only a rounding-error of attention, and that asymmetry is the practical payoff of measuring rather than asserting.
Is your Storage Capacity good — reading the result bands
A number on its own is trivia; a number against a range is intelligence. On the example inputs the band reads “76 × 16 TB disks for 5 years of growth”; your own figures place you on a different rung of the same ladder. How much of that band is luck? On Planning horizon alone ten percent either way covers 941.2 TB to 1,204.7 TB. The practical reading rules: compare against your own history before comparing against other people; treat band boundaries as zones, not walls — one point on the wrong side of a threshold rarely changes a real outcome; and when a companion output contradicts the headline, believe the pair.
Related measures answer related questions, and the distinctions matter: Storage Capacity is the quantity, Capacity to provision is this tool’s reading of it, and the decision that follows belongs to neither of them separately.
The errors that quietly break a Storage Capacity figure
Two hard reasons. First, performance: most filesystems and arrays degrade sharply once free space drops below 15–20%, because allocation becomes fragmented and background tasks (scrubbing, balancing, snapshots) contend for the same blocks. Second, resilience: rebuilding a failed 16 TB disk needs somewhere to put 16 TB of data. An array that is exactly full cannot rebuild at all, which converts a routine disk swap into a data-loss event. Thin provisioning makes this worse rather than better, because it hides the problem until it is too late to fix.
Using stale inputs. Storage Capacity is only as current as the numbers behind it — an estimate carried over from a previous season, rate table or report quietly biases every output. Re-measure the inputs first; that single habit fixes more bad Storage Capacity figures than any formula change.
This model has its own particular trap: Planning horizon carries ≈21.9 % of the response per ten percent of error, so effort spent elsewhere is largely decorative.
The subtlest error is answering the wrong question precisely. Confirm that capacity to provision is actually the quantity your decision needs — the arithmetic is flawless either way, which is what makes the mistake invisible.
Storage Capacity verification checklist
- Validate inputs against a live measurement, not datasheet peak figures.
- Re-run the calculation with a ±10 % stress on the two most sensitive inputs.
- Put Planning horizon under a magnifier; the sensitivity table above is the reason why.
- Compare the output against one published benchmark or a colleague’s independent estimate.
- Confirm the example values are plausible for your own situation before replacing them.
- Run the ±10 % stress on the top two levers and check that your conclusion survives both.
A routine for decision-grade Storage Capacity
The tool does the arithmetic; the routine below is what makes the arithmetic trustworthy. Five steps, a minute total — and afterwards you will know not just what the number is, but which assumption produced it.
Step 1 — collect, then type. Everything this tool asks for is listed above (current data volume (tb), annual data growth (%), planning horizon (years), replication factor (copies), raid / erasure-coding overhead (%), filesystem and metadata overhead (%) and the remaining fields listed above). Pull each figure from something you can point at — paperwork, a dashboard, a label — before touching a field. Guesswork at the input stage cannot be repaired at the interpretation stage. A calculation you cannot explain to somebody else is a calculation you cannot defend.
Step 2 — enter the conservative case first. Resist the flattering scenario. Type the inputs you would defend in front of a sceptical reader, read the Capacity to provision (1,204.7 TB for the example values), and note the verdict band. If a result feels obviously right, that is the moment to check the inputs hardest.
Step 3 — isolate your variables. Change one field, observe, revert. The live sensitivity list does the ranking for the default inputs; verify it against your own, because dominance shifts when the starting point shifts. Note the assumption you are least sure about; that is where the real risk lives.
Step 4 — compute a corridor, not a point. Three runs — worst, likely, best — turn Storage Capacity from a snapshot into a range. Plan against the pessimistic end and treat the optimistic end as the upside story. The inputs are the part of the work that nobody else can do for you.
Step 5 — save the story with the figure. A number without its inputs and its date is unusable within a quarter. Record 1,204.7 TB, the assumptions behind it, and today’s date; your future self will be auditing a stranger’s arithmetic. Write the figure down before moving on; memory flatters whichever number you saw last.
Who actually uses Storage Capacity — and when
In Networks & Storage the figure computed here is usually the first one somebody asks to see. Having it — with the formula behind it — changes your position in the conversation from participant to reference. The point of transparency is that a reader can disagree with a specific line rather than with you.
The audience for this page is people planning around a point or span in time. In professional Networks & Storage practice the pattern is always the same — screen with Storage Capacity, then invest slow analysis only in whatever survives the screen. Compare the calculation to the rough guess you arrived with, and ask why they differ.
Re-running this page costs nothing and catches the slow drifts that never announce themselves. In Technology & Computing the expensive mistakes are rarely sudden; they are small changes in Current data volume (TB) that nobody recalculated for a year. When in doubt, prefer the boring input: the interesting one is usually the one you misread.
People also ask about Storage Capacity
What affects Storage Capacity the most?
The short answer: Planning horizon. The longer answer is the sensitivity table on this page, where every numeric input was probed by ten percent and the capacity to provision recalculated — Planning horizon moved it furthest, by roughly 21.9 % (941.2 TB to 1,204.7 TB).
Which matters more: annual data growth or planning horizon?
By the numbers, Planning horizon: a ten-percent change in it moves the result ≈21.9 %, versus ≈11.4 % for Annual data growth. Spend your measuring effort in that order — precision on the weaker lever buys almost nothing.
What is a typical Storage Capacity?
With the example inputs on this page the result is 1,204.7 TB, labelled “76 × 16 TB disks for 5 years of growth”. “Typical” varies with situation, which is why every result here ships with its verdict band and its sensitivity list: they turn one figure into a range you can locate yourself inside.
How do I calculate Storage Capacity by hand?
Three steps: write the formula (provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉), insert your values, and simplify. The worked example section shows exactly that chain for the default inputs; replicating it once on paper teaches you more about Storage Capacity than any number of calculator runs.
What happens to Storage Capacity if planning horizon changes by 10 %?
Try it in the form above and the printed substitution will follow you; the pre-computed answer for the defaults is 941.2 TB to 1,204.7 TB across the ten-percent probe, i.e. ≈21.9 % sensitivity on the capacity to provision.
Is the Storage Capacity result of 1,204.7 TB good or bad?
Neither, on its own — the label “76 × 16 TB disks for 5 years of growth” is the band this figure falls in for the example inputs, and your own inputs will place you elsewhere on the same ladder. Judge the number against your history and your alternatives, and remember that a ten-percent error in Planning horizon alone moves it by ≈21.9 %.
Is Storage Capacity accurate?
The arithmetic is exact: provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉ is evaluated with full floating-point precision and every substitution is printed in the worked example. What limits accuracy is input quality and the model’s assumptions, so treat the 1,204.7 TB result as the correct answer to the question you typed — and check the assumptions section before acting on it.
How do I calculate Storage Capacity manually?
Write down the inputs, then follow the printed steps in order — provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉. A spreadsheet replicates it with two or three cells. The manual route is worth doing once to build intuition; after that the calculator saves time and eliminates transcription slips.
How much free space should I leave?
Plan to 75–80% of raw capacity for performance-sensitive primary storage. Above that, filesystems fragment, thin-provisioned pools risk exhaustion and a failed disk has nowhere to rebuild into.
Does deduplication change the maths?
Substantially, where it applies. VDI images and backup data deduplicate at 5:1 or better; databases, encrypted data, video and already-compressed files barely reduce at all. Never plan capacity on vendor ratios for a mixed workload.
Is my data private on this page?
Yes — computation happens locally in your browser, with no account, no tracking of your inputs and nothing sent to a server. The printed formula is the whole story of what happens to your figures.
The vocabulary of Storage Capacity
- Logical data in 5 years
- 292.1 TB on the example inputs — a companion figure printed beside the headline of Storage Capacity so that no result is read in isolation.
- Physical requirement
- 963.8 TB in the default Storage Capacity calculation — one of the secondary figures an experienced reader checks before trusting the primary number.
- Including fill-level headroom
- 1,204.7 TB for the Storage Capacity worked case; it sits next to the main result because it carries context the headline leaves out.
- Capacity to provision
- The primary output of this tool: 1,204.7 TB for the default inputs, computed exactly from provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉.
- Worked example
- Every substitution in order, from the example inputs to 1,204.7 TB — the audit trail for the headline figure.
- Verdict band
- A named zone on the range of plausible results; the example inputs land in “76 × 16 TB disks for 5 years of growth”, and your inputs may not.
- Sensitivity (±10 %)
- How far the Storage Capacity moves when a single input is nudged ten percent either way; the ranked list on this page is computed live from this engine.
The provenance of this Storage Capacity calculation
The model is published arithmetic implemented exactly as written: provisioned TB = logical TB × copies × (1 + RAID%) × (1 + FS%) ÷ dedup ÷ fill level; disks = ⌈provisioned ÷ disk size⌉. Source of record: Capacity-planning practice per SNIA storage-networking guidelines; growth rates from IDC Global DataSphere estimates; unit definitions per IEC 80000-13. Where a professional version of this calculation differs, it differs in inputs and jurisdiction-specific coefficients, not in the algebra — which is why re-checking your inputs beats hunting for a second calculator.
Nothing here replaces a qualified professional, and nothing here is approximate either — the arithmetic is exact, and the honesty is in the inputs you supply. The design constraint was set by usefulness rather than by comfort: an estimate you can interrogate beats an answer you have to trust.
Next steps after the calculation
Mark the computed date or span in your calendar with its assumptions attached; plans drift when the inputs do.
This page was written for people planning around a point or span in time, and the promise is that nothing above required trust. Bookmark the tool, re-run it whenever an input moves by more than a few percent, and keep the date with the number.