DSDatabricks Series D Financial Model
AI/ML Startup Financials (Free Excel Download)
Databricks is the unified analytics platform (Serverless Spark PaaS + Data Science Workspace SaaS) that bridges the gap between big data infrastructure and AI/ML applications.
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About this model
Databricks combines a serverless Spark platform with a collaborative Data Science Workspace. The cloud-agnostic product runs across AWS, Azure, Google Cloud, and on-premises environments, offering governed data connectors, notebooks, dashboards, and reports for enterprise analytics and AI workloads.
At the Series D deck date, Databricks had more than 500 customers, $21.9 million ARR, $118,000 average customer ARR, 136% net dollar retention, and 69% gross margin. Its Q1 2017 ARR was growing 248% year on year, with $36.7 million forecast for Q4.
The model uses quarterly ARR waterfalls for annual enterprise contracts: new customers, expansion, and churn. It translates ARR into recognised revenue and cloud COGS, models retention, sales efficiency and LTV:CAC, then forecasts technical headcount, operating cash flow, and deployment of its $56.1 million cash balance.
A turnkey financial model
Live formulas, no hardcoded values
Outputs are driven by live formulas, so the workbook updates from its assumptions instead of relying on hardcoded results.
All assumptions in one tab
Inputs are clearly marked in the Assumptions tab and separated from calculations, making it clear what to change and what to leave intact.
Statements always balancing
For integrated-statement models, the balance sheet, cash flow, and supporting schedules tie through properly.
Distinct schedules for clarity
Debt, working capital, taxes, and cash flow can get messy quickly. We group calculations in clear schedules, not across disconnected tabs.
No hidden macros or external links
There are no unexplained external workbook links or macros to undermine auditability or portability.
Changes flow through the model
Update a key driver and see the impact carry through the forecast, financing, and return outputs. We never use hardcoded numbers in formulas.
About Databricks Series D
How to build a detailed financial model for Databricks Series D
A complete walkthrough of the business, drivers, and assumptions behind the downloadable Databricks Series D model - distilled from its pitch deck and publicly available information.
Product & value proposition
- Two-layer platform:
- Databricks Serverless Spark Platform (PaaS): auto-tuning Spark with 10–40x speedup vs. open-source Apache Spark; custom data connectors; SOC2 & HIPAA compliant governance.
- Databricks Data Science Workspace (SaaS): collaborative notebooks, real-time dashboards, periodic reports - democratizes Spark access for data teams.
- Cloud-agnostic: runs on AWS, Azure, GCP, and on-prem (YARN/Cloudera/Hortonworks/Kubernetes).
- Built by the original Apache Spark creators at UC Berkeley; Spark is the de facto standard for enterprise AI/ML at scale.
- Addresses the "AI gap": the hardest part of AI is big-data infrastructure (Google NIPS 2015 framing).
Market
- Cloud computing market: Gartner estimate of $200B by 2020.
- Big data: "90% of the data created in last 2 years."
- ML/AI: described as having "just scratched the surface of use-cases."
- Spark ecosystem traction (proxy for addressable market): Summit attendees 1,100 (2014) → 3,900 (2015) → 5,100 (2016); Meetup members 12K (2014) → 66K (2015) → 300K+ (2016).
- No formal TAM/SAM/SOM breakdown provided.
Revenue model
- Subscription ARR - enterprise contracts billed annually. Implied by ARR metrics and average customer ARR.
- Average Customer ARR = $118K as of 3/31/17.
- 500+ customers across all major verticals (Ad/Marketing, Media, Healthcare/Pharma, Enterprise Software, Public Sector, Financial Services, Industrial/IoT, Retail/CPG).
- Channel: direct enterprise sales (CRO background: Axway, Stanford MBA; CMO: Alteryx pre-IPO).
- Partnership/cloud marketplace channel mentioned via Michael Hoff (SVP BD, ex-Tableau, Azure).
Traction & metrics
- ARR: $21.9M as of 3/31/17
- ARR annual growth: 248% (3/31/17 vs. 3/31/16)
- 5x growth Q1'17 vs. Q1'16
- ARR quarterly progression (actuals + forecast):
- Q1'16: $6.3M | Q2'16: $7.9M | Q3'16: $12.3M | Q4'16: $16.7M
- Q1'17: $21.9M | Q2'17: $25.6M (forecast) | Q3'17: $31.0M (forecast) | Q4'17: $36.7M (forecast)
- T2D3 comparison: T2D3 target ARR Q2'17 = $18M vs. Databricks actual/forecast = $25.6M - tracking ahead of T2D3.
- Q2'15: T2D3 $2M / Databricks $1.9M; Q2'16: T2D3 $6M / Databricks $7.9M; Q2'17: T2D3 $18M / Databricks $25.6M
- Customer count: 500+
- Average Customer ARR: $118K (up 112% yr/yr)
- Net Dollar Retention: 136% (3/31/17 vs. 3/31/16)
- Cash on hand: $56.1M as of 3/31/17
- Gross Margin: 69% (Q1'17)
- LTV:CAC ratio: 4.1 (3/31/17 TTM)
Unit economics
- Gross Margin: 69% Q1'17
- LTV:CAC: 4.1 (TTM as of 3/31/17)
- Net Dollar Retention: 136% - strong expansion motion; existing customers grow significantly.
- Average Customer ARR: $118K, up 112% yr/yr - driven by both new logos and expansion.
- Payback period: Not explicitly stated. With 69% GM and LTV:CAC of 4.1, implied payback ~14–18 months (typical for enterprise SaaS at this NRR), but not confirmable from deck.
Competition / moat
- Moat framing: Databricks created Apache Spark (Berkeley origins); deepest technical expertise in the ecosystem.
- Platform lock-in: collaborative workspace + serverless infra = stickiness across data engineering, data science, and analytics teams.
- Cloud-agnostic positioning differentiates from single-cloud-native offerings (AWS EMR, Azure HDInsight, GCP Dataproc).
- On-prem expansion addresses Hadoop/Cloudera/Hortonworks incumbents.
- Named competitors: Not explicitly named in deck. Implicitly: legacy data warehouses (Netezza, Oracle), Hadoop vendors (Cloudera, Hortonworks, MapR), cloud storage.
- "AI gap" framing positions Databricks as the only unified solution across ETL, ML, and streaming.
Team & funding ask / use of funds
- Team:
- Ali Ghodsi - CEO & co-founder; PhD/MBA, Adjunct Professor UC Berkeley
- Patrick Wendell - VP Engineering & co-founder; UC Berkeley MSc, Princeton BS
- Ron Gabrisko - CRO; Cyclone (pre-revenue → Axway acquisition), Stanford MBA/MS
- Rick Schultz - CMO; Alteryx (pre-revenue → IPO), Oracle VP Product Marketing
- John Winkenbach - SVP Finance; CFO Jobvite, VP Finance Technorati
- Hatim Shafique - CCO; AppDynamics (pre-revenue → IPO)
- Michael Hoff - SVP BD & Partnerships; Tableau VP Channels, EMC Global VP Sales, MSFT GM Windows Azure
- Cash on hand pre-raise: $56.1M (3/31/17).
Recommended financial model
- Archetype + why: SaaS ARR subscription model with usage-based expansion layer. Databricks sells annual enterprise contracts with strong net dollar retention (136%), so the correct model is an ARR waterfall (new ARR + expansion ARR − churn ARR) driving a P&L, not a transactional revenue model. The 69% gross margin, LTV:CAC of 4.1, and cloud infrastructure cost structure are all consistent with a cloud SaaS P&L.
- Forecast horizon & granularity: Quarterly for Years 1–2 (2017–2018), annual for Years 3–5 (2019–2021). The deck already provides quarterly ARR actuals through Q1'17 and management forecasts through Q4'17, so Q-level is natural.
- Key drivers & assumptions:
| Driver | Value | Source |
|---|---|---|
| ARR at model start (Q1'17) | $21.9M | - |
| ARR annual growth rate (historical) | 248% | - |
| Q4'17 ARR target | $36.7M | - |
| Average Customer ARR | $118K | - |
| Customer count | 500+ | - |
| Net Dollar Retention | 136% | - |
| Gross Margin | 69% | - |
| LTV:CAC | 4.1 | - |
| ARR growth rate Year 2 (2018) | ~150% | Deceleration from 248% consistent with T2D3 double-double-double trajectory and scale |
| ARR growth rate Year 3 (2019) | ~100% | Continued scale; Series D capital deployed into S&M |
| ARR growth rate Years 4–5 | 60–80% | Normalization as base grows; comparable SaaS at $100M+ ARR |
| NDR long-term | 120–130% | Slight moderation from 136%; still strong expansion motion |
| Gross margin long-term | 72–75% | Modest improvement as platform scales; cloud infra costs optimized |
| S&M % of revenue | 45–55% | Typical high-growth enterprise SaaS; consistent with LTV:CAC of 4.1 |
| R&D % of revenue | 25–35% | Deep technical product; Spark ecosystem investment |
| G&A % of revenue | 8–12% | Standard enterprise SaaS overhead |
| New customer adds per quarter | ~30–50 | Implied from 500+ customers over ~10 quarters of operation |
| Average contract length | 1 year | Standard enterprise SaaS; not stated in deck |
- Scenarios (Base / Bull / Bear - which variables flex):
- Base: ARR growth decelerates per table above; NDR holds at ~128%; GM improves to 73% by Year 3.
- Bull: NDR sustains at 136%+; on-prem expansion adds a new revenue stream; cloud partnerships accelerate new logo adds; ARR growth stays >100% through 2019.
- Bear: Enterprise sales cycle elongates; ARR growth decelerates to ~80% in 2018; NDR compresses to 115% as budget scrutiny increases; GM pressure from cloud infra costs.
- Flex variables: new ARR growth rate, NDR, gross margin, S&M efficiency (CAC).
- Required sheets / outputs:
- Assumptions - all drivers in one place, color-coded vs.
- ARR Waterfall - beginning ARR, new ARR, expansion ARR, churn ARR, ending ARR (quarterly then annual)
- Customer Cohort Schedule - new logos per period, average ACV, expansion rate by cohort
- P&L - revenue (ARR → recognized revenue), COGS, gross profit, S&M, R&D, G&A, EBITDA, net income
- Cash Flow - operating cash flow; key: deferred revenue build (annual prepay), capex light
- KPI Dashboard - ARR, ARR growth %, NDR, gross margin, LTV:CAC, average customer ARR, cash runway
- Scenario Toggle - Base / Bull / Bear switcher driving all sheets
Frequently asked
Is the Databricks Series D financial model free?+
Yes. The Databricks Series D model is a free Excel (.xlsx) download with live formulas. Sign up with your email and the workbook is yours to keep, review, and edit.
What's included in the model?+
A 5-year monthly forecast with P&L, cash flow and runway, valuation (exit multiple plus a DCF cross-check), MOIC/IRR returns, and unit economics, with live formulas throughout.
How was this model built?+
It was built from Databricks Series D's pitch deck and publicly available information, then structured to investment-banking standards as a fully editable Excel model.
Can I change the assumptions?+
Yes. You can change assumptions and the live formulas will recalculate in the downloadable Excel model.
Have more financial modelling questions? Contact us
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