Apollo Financial Model

Apollo's Enterprise Data Graph is a managed GraphQL layer that sits between app teams and backend services. It replaces ad-hoc API integrations and BFF patterns with a single, governed schema ("the Graph") that:

• Lets app teams query exactly what they need without knowledge of underlying service topology
• Frees backend teams to evolve/replatform services without breaking client contracts
• Provides governance, schema management, observability, and usage analytics at enterprise scale

Core product lines implied: Enterprise Data Graph (subscription), Freemium (self-serve single-team graphs), Enterprise Support.

No explicit TAM/SAM/SOM figures stated in deck.

Demand-side framing offered:

• Digital outperformers grow revenue 5x faster, earn 60% higher shareholder returns, and achieve 20% higher operating margins
• "A new layer of the Internet that every enterprise will likely adopt"
• Apollo GraphQL open-source client reaches ~2.5M weekly npm downloads as of early 2021, up from near-zero in 2016
• Estimated 1.5M active developers on Apollo open-source
• 15% attach rate to modern web development (React ecosystem)
• 5 years of sustained exponential growth in open-source adoption

Three revenue streams:

1. Enterprise Data Graph (product subscription) - primary ARR driver; sold top-down to enterprises via AE+SDR teams
2. Freemium - self-serve single-team graphs; PLG motion, developer-led adoption that seeds enterprise pipeline
3. Enterprise Support - separate support contracts

GTM motion:

• Bottom-up: developers self-onboard on freemium; usage signals feed marketing intelligence
• Top-down: AE+SDR identifies strategic business buyer at target accounts; converts developer usage into an enterprise-wide graph consolidation deal
• New logo ARR target: $XXXk average per new logo
• Ramped AE productivity: $2M/year

Pricing basis: not explicitly stated; usage-volume (transactions/queries) is the likely expansion driver given the 4.7x first-year usage growth metric.

All dollar figures are redacted in the deck (replaced with $XX.XM / $XXXM placeholders). What is confirmed:

• ARR (Q2 2021 forecast): $XX.XM total ARR (Enterprise Product $XX.XXM + Freemium $X.XXM + Support $X.XXM)
• YoY enterprise ARR growth (20Q1–21Q1): X.XX×; X.XX× including freemium and support
• ARR target EOY 2023: $XXXM on ~$XXM total burn
• 5 consecutive quarters of increasing ARR growth rate as of Q2 2021
• Open-source: ~1.5M active developers; ~2.5M weekly npm downloads at peak (early 2021)
• Customers: 3 named (anonymised) enterprise customers cited - top fintech, top travel company, Fortune 500 retailer
• AE productivity: $2M/year (ramped)

Note: COVID-impacted logos in travel and office-sharing were excluded from core metrics.

All specific dollar amounts redacted. Structure is confirmed:

LTV:CAC scenarios (undiscounted, pro forma for 2023 scale): | Scenario | Lifetime | Avg NRR | LTV:CAC | | -- | -- | -- | -- | | Low | X years | XXX% | X.X | | Mid | X years | XXX% | X.X | | High | XX years | XXX% | X.X |

First Year Cash Flow (per $100k ARR):

• Average first-year prepayment: $XX,XXX
• Less: acquisition cost: ($XX,XXX)
• Less: cost to serve: ($XX,XXX)
• First year cash flow: $XX,XXX (positive)

Other confirmed metrics:

• 4.7x average first-year usage growth
• NRR first year: XXX% (redacted)
• GRR first year: XX% (redacted); rises to XX% with reactivation
• Gross margin: XX.X% (redacted)
• Cost to acquire $100k new ARR: $XX,XXX (redacted)
• Cost to acquire $100k expansion ARR: $XX,XXX (redacted)
• LTV:CAC stated as "X-X" range
• Cash flow positive in year one of a deal

No explicit competitive analysis slide. Moat argued through:

• 10 years of open-source GTM experience (prior company: Meteor, top-10 GitHub project, 65% EBITDA margin)
• 1.5M developers already using Apollo open-source = massive installed base and pipeline
• GraphQL standard ownership/stewardship: Apollo co-evolved with GraphQL from 2016
• Network effects: more enterprise graphs → more subgraph contributors → richer shared schema
• Governance and schema registry create switching costs once an enterprise standardises

Prior failed alternatives cited: API Gateways (operational only) and BFFs (don't scale).

Founders:

• Geoff Schmidt - Co-founder & CEO
• Matt DeBergalis - Co-founder & CTO
• Met at MIT; collaborators for 20 years; previously built Meteor (profitable OSS business)

Exec team:

• John Siebert - CRO (ex-MongoDB, New Relic, Forter)
• Eli Staykova - VP Marketing (ex-Cloudflare, Brex)
• Parag Sanghavi - VP Customer Success (ex-Databricks, AppDynamics)
• Catrina Zhang - CFO (ex-Amazon, Jawbone)
• Hatim Shafique - GTM Advisor (ex-Databricks, AppDynamics)

Investors: Andreessen Horowitz, Matrix (lead investors)

• Archetype + why: SaaS ARR model with PLG/usage-based expansion layer. Apollo has three distinct ARR streams (enterprise product, freemium, support), a PLG funnel driving enterprise pipeline, and unit economics explicitly framed per $100k ARR block - textbook enterprise SaaS with a usage-expansion kicker.

• Forecast horizon & granularity: Quarterly (Q1–Q4) for Years 1–2 (2021–2022); annual for Years 3–5 (through 2025/2026). EOY 2023 ARR target is the primary management milestone referenced.

• Key drivers & assumptions:

*Revenue build (ARR waterfall):*

• Beginning ARR by stream (Enterprise, Freemium, Support)
• New logo count per quarter
• Average new logo ARR
• Expansion rate (NRR – 100%)
• GRR / churn
• Freemium ARR growth
• Support ARR as % of enterprise ARR
• AE headcount ramp (key pipeline lever)
• AE ramp time to full productivity

*Cost structure:*

• Gross margin
• S&M: AE + SDR costs tied to headcount; CAC per $100k new ARR
• R&D:
• G&A:
• Total burn target: ~$XXM to reach $XXXM ARR

*Unit economics inputs:*

• First-year prepayment rate
• CAC: new logo vs. expansion
• LTV: scenario table exists; exact NRR and lifetime inputs redacted

• Scenarios (Base / Bull / Bear - which variables flex):
• Base: AE headcount per deck plan, NRR ~130%, GRR ~87%
• Bull: faster AE ramp + NRR >140% (consistent with 4.7x usage growth), freemium attach accelerates
• Bear: slower enterprise sales cycles, NRR ~115%, COVID-adjacent verticals remain impaired

• Required sheets / outputs:

1. `Assumptions` - all inputs, clearly labelled vs
2. `ARR Waterfall` - by stream, quarterly: opening ARR, new logo, expansion, churn, closing ARR
3. `P&L` - revenue, gross profit, S&M / R&D / G&A, EBITDA, net burn
4. `Unit Economics` - CAC, LTV, payback, LTV:CAC by scenario (mirrors slide 17)
5. `Headcount` - AE, SDR, CS, R&D, G&A; drives S&M and R&D cost lines
6. `Cash Flow` - operating + capex + financing; cumulative burn vs. runway
7. `Scenarios` - Base / Bull / Bear toggle feeding ARR Waterfall and P&L
8. `Dashboard` - KPI summary: ARR, NRR, GRR, burn rate, runway, LTV:CAC, new logos added