Nasuni ROI Calculator
Estimate the ROI on consolidating file data onto Nasuni: infrastructure cost, team productivity, and AI value.
Start with the volume of file data under management and the platform in use today.
File data
Remote teams
Branch offices, project sites, and home-based staff.
Time per remote worker recovered when files open at local speed. Nasuni delivers up to 15x faster file access.
Not all recovered time becomes productive immediately.
Estimated value of Nasuni
Cost savings
Hardware acquisition and refresh, data center overhead, IT administration, and disaster recovery infrastructure, measured against NetApp or Dell Isilon. Net of what Nasuni charges.
Cloud choice on the platform in use today: Free choice of cloud.
Gains
Time returned and risk avoided, priced separately from cost. Nasuni caches active files at the edge, delivering up to 15x faster file access.
IT efficiency and business resilience use industry benchmark rates per terabyte. In the 2026 State of Enterprise File Data report, 35% of respondents said slow file access directly hurts productivity.
File estate figures are estimated from the inputs below and can be adjusted.
Organization
Sets the starting file density estimate.
Documents that hold the answers people look for. Estimated from file data and industry.
AI usage
Indicative blended cost per million tokens. Enter the rate if known.
Work an AI App takes on, so each person doesn’t have to.
Not all recovered time becomes productive immediately.
AI token cost today
Productivity gained each year with Nasuni
What it costs to run AI without a governed index
An AI App given direct access to a file system has no search capability. It rescans candidate files, pulls them in whole, and carries everything already scanned into the next round. The next query rescans the same files again.
| Per query | No governed index | With a structured index |
|---|---|---|
| Tokens burned rescanning | 144,000 | 49,000 |
| Cost of one query | $1.44 | $0.49 |
| Annual token bill | $559,872 | $190,512 |
| Tasks the agent completes | 50 to 60% | 90%+ |
| Difference a year | $369,360 | |
Pinecone research published in 2026 puts about 85% of an agent’s effort into retrieval rather than the work itself. The research finds that structured retrieval lifts task completion from the 50% to 60% range to above 90%. Across a benchmark of more than 30,000 legal documents, an agent working without a retrieval layer finished 6% of the tasks it was given.
Savings with Nasuni
Time to value: weeks, not quarters. Every figure above is built from the assumptions on this page and shown in full in the business case.
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Platform savings and AI value in one document, formatted for an executive audience, with every assumption shown.
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Business case
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AI value business case
Every figure in this document is generated from the calculator inputs, so nothing here can drift from what was displayed. Pages 2 and 3 show the derivation, assumption by assumption, with each source named.
File estate
| File data under management | 100 TB |
| Files at typical density for the industry selectedArchitecture, engineering, and construction, 292,710 files per terabyte, industry median where measured | 29,271,000 |
| Knowledge files, the basis for the subscription | 12 million |
Platform savings
| Platform in use today | NetApp or Dell Isilon |
| Net savings each year | $55,000 |
| Cloud choice on the platform in use today | Free choice of cloud |
| Operational impact each yearIT efficiency and business resilience at industry benchmark rates per terabyte | $72,000 |
Token economics
| AI queries a year600 people, 3 queries a day, 240 working days | 432,000 |
| Tokens per query, rescanning the file estate | 144,000 |
| Tokens per query, structured indexPinecone benchmark, 2026. To be replaced by independent Nasuni measurement. | 49,000 |
| Annual token bill without a governed indexMicrosoft Copilot, $10 per million tokens, indicative | $622,080 |
| Annual token bill with a structured index | $211,680 |
| Token spend avoided each year | $410,400 |
Productivity gained at the edge
| People in remote offices and locations | 1,000 |
| Hours a week each person saves with LAN-speed accessNasuni delivers up to 15x faster file access | 1 |
| Hours returned each yearNasuni delivers up to 15x faster file access, so this figure is an upper bound | Up to 14,560 |
| Productivity value gained | Up to $735,713 |
Productivity gained through AI
| People using AI Apps | 600 |
| Hours a week each person saves with AIAnchored on McKinsey Global Institute research: about 9 hours a week gathering information, roughly a third of which an AI App can take on | 3 |
| Share of saved time that becomes productiveThe share of recovered time that becomes finished work | 15% |
| Hours returned each year | 12,960 |
| Expressed as full-time equivalents | 6.9 FTE |
| Productivity valueLoaded knowledge-worker cost of $95,000 over 1,880 hours | $654,894 |
Investment and return
| Platform value each yearNet savings, remote productivity, and other operational impact | $264,500 |
| AI value each yearToken spend avoided plus productivity value gained | $1,065,294 |
| Total annual value | $1,986,329 |
| Platform investmentAnnual Nasuni subscription for the file estate | $82,500 |
| AI investmentPriced on knowledge file count rather than seats | $121,200 |
| Total annual investment | $203,700 |
| Net annual value | $1,126,094 |
| Net value over three years | $3,378,282 |
| Returned for every dollar invested | 6.5x |
What gets indexed
Metadata indexing carries real value on its own. An AI App can locate the current issued drawing set, identify the project folder holding it, report who last modified it and when, and answer questions across thousands of files that no full-text index would reach. In engineering and manufacturing estates this is often the larger share of the value, and it applies across the whole estate at no additional cost.
Take it to your team.
The assumptions are on pages 2 and 3, in the units finance and infrastructure teams already work in.
How these figures are calculated: Hard cost savings are based on aggregated customer TCO data and are stated net of Nasuni subscription cost. Operational impact uses industry benchmark rates per terabyte. File counts are estimated from a median across 48 measured customer estates; density varies widely between environments. Productivity figures follow McKinsey Global Institute research on information-gathering time, reduced for the share better retrieval addresses and again for the share of recovered time that becomes finished work.
Sources: Nasuni delivers up to 15x faster file access. In the 2026 State of Enterprise File Data report, 35% of respondents said slow file access directly hurts productivity. Token consumption and agent task completion figures come from Pinecone research published in 2026; an independent measurement of Nasuni is underway and will replace them.
All figures are illustrative and results may vary.