# Wasabi Financial Model

Hot cloud object storage at 1/5th the price of Amazon S3, 100% S3-compatible, sold via channel partners and direct.

- Canonical: https://finamodel.com/startups/wasabi
- Excel download: https://finamodel.com/startup-models/wasabi.xlsx
- Category: Enterprise/Security
- Model type: 3-Statement
- Funding round: Series B
- Funding: $30M
- Founded: 2020
- Geography: US, EU, Japan [DECK slide 3].
- Customer: B2B

## About the company

Wasabi provides hot cloud object storage that is S3-compatible and positioned at one fifth of Amazon S3's price. It sells to infrastructure customers directly and through channel partners across the US, Europe, and Japan.

Revenue combines pay-as-you-go storage with Reserved Capacity Storage, a multi-year contract product. Stored terabytes are the central commercial unit because they determine both customer billing and cloud-infrastructure cost; the company had received $110 million of investment at deck date.

The model separates PAYG from prepaid capacity, forecasting TB stored, pricing, contract terms, churn, and channel mix. Storage COGS, gross margin, capital expenditure, sales costs, and operating overhead then show the economics and cash flow of scaling cloud storage.

## What's included

- 5-year monthly revenue build with stage-appropriate growth assumptions
- Full P&L, headcount plan, and operating-expense schedule
- Cash-flow statement, runway, and burn-rate tracking
- Valuation via exit multiple with a DCF cross-check
- Returns analysis with MOIC and IRR
- Unit economics including CAC, LTV, payback, and cohort retention

## Product & value proposition

- S3-compatible hot cloud object storage: no egress fees, no API call fees.
- Pricing: $0.0059/GB/month vs. Amazon S3 Standard $0.023/GB/month, Google Multi-Regional $0.026/GB/month, Azure RA-GRS Hot Access $0.046/GB/month.
- 11 nines of durability, fully encrypted, immutable option.
- Patented purpose-built distributed file system; higher disk utilisation and density vs. hyperscalers.
- "Gen2 Edge" product: local caching + cloud replication + "bottomless" local storage spillover.
- Reserved Capacity Storage (RCS): multi-year (3–5 yr) prepaid contracts positioned vs. on-prem hardware capex.

## Market

- Global Cloud Storage Market: $57B in 2019, CAGR 27.5%.
- Worldwide data projected to reach 175 zettabytes by 2025; at $5.99/TB/mo. Wasabi frames a $12T theoretical opportunity.
- Structural tailwind: Gartner projects 80% of enterprises shut down traditional data centres by 2025 vs. 10% at time of deck.

## Revenue model

Two distinct pricing motions:

**Pay-as-you-go (PAYG)**
- $0.0059/GB/month ($5.99/TB/month).
- No egress or API charges - all-in per-GB price.
- Revenue = TB stored × $5.99/TB/month.

**Reserved Capacity Storage (RCS)**
- Multi-year (3–5 year) prepaid contracts sold against hardware capex budgets.
- Example pricing confirmed from image: 100 TB for 5 years = $38,456 incl. premium support vs. NetApp $246,483; 240 TB for 5 years = $92,294 vs. Dell EMC $476,000 and vs. Cloudian $222,816.
- Implied RCS rate: ~$38,456 / (100 TB × 60 months) ≈ $6.41/TB/month (slightly above list PAYG, includes premium support).
- Wasabi notes: upfront cash, significant "breakage" (prepaid capacity unused), and customer lock-in.

**Channel**
- 45% of revenue through channel and rising; 100% channel in Europe and Japan.
- Partners: MSPs, CSPs, VARs, Technology Alliance Partners (Veeam is flagship TAP).
- Full partner API for multi-tier distribution.

## Traction & metrics

- 18,000 customers.
- 3,000+ partners.
- Customer growth: chart shows near-flat through 2017–2018, then hockey-stick growth through 2019 into 2020, reaching ~14,000 on the line chart with a stated total of 18,000.
- 100+ employees.
- No ARR, MRR, churn, NRR, or revenue figures stated.

## Unit economics

- Cost advantage vs. hyperscalers is structural (patented file system, higher disk utilisation, lower overhead) - implies material gross margin at scale, but no numbers disclosed.
- RCS "breakage" noted as a positive unit economic driver.

## Competition / moat

- Direct competitors: AWS S3, Google Cloud Storage, Azure Blob Storage, on-prem hardware (NetApp, Dell EMC, Cloudian).
- Moat arguments made in deck:
  - Price floor: Amazon cannot drop S3 price without wiping ~$5B off >$6B storage revenue line.
  - Channel lock-up: first-mover to secure best MSP/VAR/TAP partners blocks competition.
  - Technical barrier: building cloud storage at scale is difficult.
  - S3 compatibility lowers switching cost from AWS to Wasabi (no re-engineering).
- Strategy: land-grab model - scale fast, lock channel, invest in brand.

## Team & funding ask / use of funds

- Founders: David Friend (CEO) & Jeff Flowers (CTO) - serial entrepreneurs; co-founded Carbonite (NASDAQ: CARB, sold to OpenText for $1.4B), Faxnet, Pilot Software, Computer Pictures.
- Leadership: SVP Sales (Marty Falaro, Oracle/Acme Packet), SVP Product (James Donovan), CMO (Michael Welts), CFO (Michael Bayer), SVP Engineering (Ken Kuenzel).
- Funding raised to date: $110M total; $70M Series B in 2H 2018.

## Recommended financial model

- **Archetype + why:** Usage-based cloud infrastructure revenue model (TB-stored driver) with two revenue streams - PAYG ($/TB/month) and RCS (multi-year prepaid contracts). This is the natural fit: the core economic unit is TB under management, which drives both revenue and COGS. A 3-statement model sits underneath.

- **Forecast horizon & granularity:** Monthly for Year 1–2 (to capture PAYG billing cycles and RCS cash timing), then quarterly / annual for Years 3–5. 5-year total horizon to match founders' $1B target.

- **Key drivers & assumptions:**

| Driver | Value / Source |
| -- | -- |
| PAYG price per TB/month | $5.99 |
| RCS average contract size (100 TB / 5 yr) | $38,456 |
| Customers at model start | 18,000 |
| Partners at model start | 3,000+ |
| Channel % of revenue | 45% and rising |
| Average TB stored per customer | 5 TB/customer starting point; ramp to 15 TB over 5 years - typical SMB/media workload expansion |
| New customer adds per month (Year 1) | ~600–800/month based on hockey-stick slope visible in chart (slide 5) |
| Monthly churn rate | 1.5%/month - cloud storage is sticky but no retention data in deck |
| PAYG gross margin | 50–60% at scale; cost advantage claimed vs. hyperscalers but not quantified |
| RCS breakage rate | 10–15% of prepaid capacity unused - cited as positive but not quantified |
| RCS % of new bookings | 20–30% of new business (growing as VAR channel matures) |
| COGS per TB (storage + DC + bandwidth) | ~$2.50–3.00/TB/month, implied by pricing and "much cheaper" cost structure |
| S&M % of revenue | 40–50% (channel-heavy, but heavy partner investment still required) |
| R&D % of revenue | 15–20% (platform company; patented tech) |
| G&A % of revenue | 8–12% |
| Headcount at model start | 100+; ramp to ~500 by Year 5 |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - Bear: slower customer adds (300/month), higher churn (2.5%), pricing pressure forces 10% price cut by Year 3.
  - Base: 600/month new customers, 1.5% churn, pricing holds.
  - Bull: channel acceleration (1,000+/month new customers), RCS mix rises to 35%, breakage benefits kick in.

- **Required sheets / outputs:**
  1. Assumptions - all drivers, pricing, scenario toggles.
  2. Customer Cohort Model - monthly cohorts tracking TB/customer ramp and churn.
  3. Revenue - PAYG (TB × price) + RCS (bookings, cash received, recognised revenue).
  4. COGS & Gross Margin - cost per TB stored, data centre capex/opex.
  5. P&L (Income Statement) - revenue, COGS, S&M, R&D, G&A, EBITDA, net income.
  6. Balance Sheet - prepaid RCS deferred revenue schedule is key.
  7. Cash Flow Statement - emphasise RCS upfront cash timing vs. GAAP revenue recognition.
  8. KPI Dashboard - TB under management, customer count, MRR, NRR (assumed), gross margin %, runway.

## Frequently asked questions

### Is the Wasabi financial model free?

Yes. The Wasabi model is a free Excel download with live formulas.

### Can I change the assumptions?

Yes. The workbook is editable and its live formulas recalculate when assumptions change.
