# Coralogix Financial Model Cloud-native observability platform (logs, metrics, security) using patented stateful streaming analytics to decouple indexing from real-time analysis, delivering up to 70% storage savings vs. legacy tools. - Canonical: https://finamodel.com/startups/coralogix - Excel download: https://finamodel.com/startup-models/coralogix.xlsx - Category: Crypto/Web3 - Model type: SaaS ARR / Valuation - Funding round: Series C - Funding: $55M - Founded: 2021 - Geography: Global (US-headquartered; ISO/GDPR/PCI compliance suggests EU and US customer base) [DECK, slide 5] - Customer: B2B ## About the company Coralogix is a cloud-native observability platform for logs, metrics, and security analytics. Its stateful streaming architecture separates real-time analysis from conventional indexing, which the company says can reduce storage costs by up to 70% compared with legacy observability tools. The company sells globally to enterprises and had raised more than $40 million when the deck was prepared. Its compliance posture spans ISO, GDPR, and PCI, while the product’s core economic tension is clear: customers want growing data ingestion and broad retention, but the provider must control the cloud cost of processing that data. Forecast enterprise logos, initial contract value, data ingestion, expansion, renewals, and churn to build ARR and usage revenue. Derive COGS from ingestion and storage economics instead of applying a generic margin, then include implementation and customer-success capacity. Scenarios should flex net retention, usage growth, pricing, and cloud efficiency, which jointly determine the benefit of the streaming architecture. ## 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 - Patented "Streama" technology: analyze logs/metrics in real time before indexing; skip indexing for low-priority data - Three pillars: Log Analysis, Metrics, Cloud Security - Key differentiators vs. Splunk/SumoLogic/Elastic: - No storage/indexing required for real-time analytics - reduces storage costs up to 70% - No data loss, no throttling, no overages - OS-standard syntax (no proprietary query language) - removes vendor lock-in - ML-powered anomaly detection and dynamic alerting without static thresholds - Pricing model: pay per GB ingested - usage-based, not per-seat - Data platform angle (roadmap): targeting a broader "cloud data platform for any data, any syntax, any storage" by 2023 - 100+ integrations; pluggable API for any new integration in under 24 hours ## Revenue model - **Pricing unit:** GB ingested per month - consumption/usage-based - **Tiers implied by "TCO Data Orchestration" feature:** customers can route data to hot index, archive, or analyze-without-index (three tiers of data treatment), which implies tiered pricing by data pipeline/storage tier. Explicit tier pricing not shown. - **Sales motion:** implied enterprise/mid-market B2B (Fortune 100 customers, Masterclass, Payoneer, Fiverr, Lufthansa, Adobe); no self-serve PLG motion explicitly described. - **Channel:** direct sales inferred; marketplace/reseller not mentioned. ## Traction & metrics - 2,000+ paying customers - 10 Fortune 100 customers - $40M+ raised (cumulative) - 3M+ events processed per second - 500K+ apps monitored - 9.8/10 G2 Crowd rating - 3 patents for "Streama" technology - Founded 2015 - No ARR or revenue figures disclosed. **Case study - monday.com:** - monday.com annual revenue at time of case study: $130M - 10x log volume growth at monday.com - 60% reduced MTTR after Coralogix - $100K saved/year by monday.com using Coralogix ## Unit economics - Only indirect signal: "up to 70% storage savings" for customers; "pay per GB ingested". No Coralogix-side margin data in deck. ## Competition / moat **Named competitors (positioned as legacy):** - Splunk (ingest/search/visualize, 2003) - SumoLogic ("Splunk in the cloud," 2010) - Elastic (multi-tenant search engine, 2010) - LogLogic (2002) **Moat claims:** - 3 patents on "Streama" stateful streaming technology - structural differentiation - First-mover on decouple-index-from-analysis architecture - Network effects not claimed; stickiness via integrations (100+), CI/CD workflow embedding, and data already flowing through platform - OS-standard syntax (Lucene/PromQL) lowers switching-in cost vs. Splunk SPL ## Team & funding ask / use of funds **Team:** - Ariel Assaraf, CEO - veteran of Israeli intelligence unit 8200; former GL at Verint - Matt Handler, COO & President - former leader at Sumo Logic, NTT, Whitehat, LogLogic, HP - Yoni Farin, CTO - former engineering leader at Verint and Motorola **Funding:** - $40M+ raised to date --- ## Recommended financial model **Archetype + why:** Usage-based SaaS (GB-ingested model) with a subscription revenue layer - sometimes called a "consumption ARR" model. Revenue is driven by data volume per customer, not seat count. The right archetype is a **usage-based B2B SaaS model** with: - Customer count × average GB/month/customer × price per GB = revenue - Layered by data tier (hot-indexed, archive, analyze-only) to capture the TCO optimization upsell/downsell dynamic **Forecast horizon & granularity:** - 3 years monthly (Year 1–2 monthly, Year 3 quarterly is acceptable) - Monthly is required because usage-based businesses have visible seasonality and expansion/contraction revenue within cohorts **Key drivers & assumptions:** | Driver | Value / Tag | | -- | -- | | Starting customer count | 2,000 | | New customer adds / month | ~50–80/mo, implying ~600–960/yr net new; growth stage company | | Average GB ingested / customer / month | 1–5 TB/month (log-heavy enterprise; varies widely by industry) | | Price per GB ingested | $0.15–$0.40/GB based on observable DevOps SaaS market comps (Datadog, Sumo) | | Net revenue retention (NRR) | 120–130%; usage-based SaaS with data volume growth tends to expand revenue within cohort; monday.com 10x log volume growth is directional support | | Gross logo churn rate | 8–12% annually; B2B SaaS mid-market baseline | | Gross margin | 65–75%; cloud-hosted SaaS with significant AWS/Kafka infrastructure costs; usage-based models often run 60–70% GM at scale | | Sales & marketing as % of revenue | 40–55%; growth-stage enterprise SaaS typical range | | R&D as % of revenue | 20–30%; platform-heavy company with patented tech | | G&A as % of revenue | 10–15% | | Data tiers mix (hot / archive / analyze-only) | 40% / 30% / 30% blended; directly impacts COGS (storage costs) | | Fortune 100 customer count | 10; higher ACV per enterprise customer - model separately if possible | **Scenarios (Base / Bull / Bear - which variables flex):** - **Base:** 50 new logos/month, NRR 120%, GM 70%, GB/customer growing 15% YoY - **Bull:** 80 new logos/month, NRR 130%, GM 72%, GB/customer growing 25% YoY (cloud workload explosion) - **Bear:** 30 new logos/month, NRR 108%, GM 65%, GB/customer flat (customers limit ingestion for cost control) **Required sheets / outputs:** 1. **Assumptions** - all drivers centralized, tagged or 2. **Customer cohort model** - monthly new customer adds, churn, expansion by cohort; outputs ending customers and cohort ARR 3. **Revenue build** - consumption revenue (GB × price) + any base subscription fee; split by data tier (hot/archive/analyze) 4. **P&L (Income Statement)** - Revenue → Gross Profit → OpEx (S&M, R&D, G&A) → EBITDA → Net Loss 5. **Cash & runway** - starting cash (infer from $40M+ raised minus burn), monthly cash burn, months of runway 6. **KPI dashboard** - ARR, NRR, Gross Margin %, CAC payback (once assumptions populated), logo count, GB processed ## Frequently asked questions ### Is the Coralogix financial model free? Yes. The Coralogix 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.