# Treblle Financial Model

API observability SaaS - real-time monitoring, auto-generated docs, analytics, and quality scoring via a lightweight SDK install.

- Canonical: https://finamodel.com/startups/treblle
- Excel download: https://finamodel.com/startup-models/treblle.xlsx
- Category: Dev Tools
- Model type: SaaS ARR / Valuation
- Funding round: Seed
- Funding: $1.2M
- Founded: 2021
- Geography: Founded in Croatia; primary beta users worldwide; founder company (Flip) has clients in USA, Norway, Dubai [DECK, slide 3].
- Customer: B2B

## About the company

Treblle is API observability software offering monitoring, logs, documentation, analytics, testing, and quality scoring through a lightweight SDK. Developers can install it quickly and gain visibility into API performance, security, and behavior.

It has explicit volume-based subscription tiers from free to enterprise, ranging from 10,000 requests to custom request volumes and retention periods. Early traction included two million API requests across 30 live projects.

The model is subscription ARR with tier migration. Projects, API requests, free-to-paid conversion, tier mix, expansion, and churn determine revenue. Log-storage cost, developer acquisition, and enterprise upgrades drive margin.

## 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

- SDK/package installable on any API in <2 minutes, supporting PHP, Laravel, Rails, .NET, Python.
- Out-of-the-box features: real-time API monitoring, logging & error tracking, auto-generated documentation, API analytics, one-click testing, API quality score.
- Deep signal capture: user geolocation, device/app details, authentication detection, sensitive data masking, load times, response sizes, endpoint grouping, version detection, dynamic URL parsing.
- Dashboard shows per-project Treblle Score (composite of Performance, Security, Quality sub-scores visible in product screenshots: e.g., golfr.io scored Performance 17 / Security 100 / Quality 100 / Total 76).

## Market

- 25 million developers globally (2019); forecast 40 million by 2030.
- 400 billion API calls made per month.
- REST API popularity growing ~30% YoY.
- 66% of developers expected to use more APIs in 2020 vs. 2019.
- Global software development cost: $1.25T/year; $312B of that spent on problem-fixing, docs, monitoring, support.

## Revenue model

- SaaS subscription, volume-based on API requests processed + log retention period.
- Five tiers:
| Tier | Requests/mo | Log retention | Price/mo |
| -- | -- | -- | -- |
| Free | 10K | 7 days | $0 |
| Small | 200K | 30 days | $50 |
| Medium | 1M | 45 days | $200 |
| Large | 5M | 60 days | $500 |
| Enterprise | Custom | 90 days | Custom |
- All tiers include all features; differentiation is purely on volume and retention.
- Go-to-market: bottom-up, developer-led (free tier → upgrade); community channels (Twitter, Reddit, LinkedIn).
- Enterprise upmarket expansion planned post-initial growth.

## Traction & metrics

- 30 real-life projects live in past 9 months.
- 2 million API requests processed to date.
- 60 diverse beta users worldwide.
- Named beta customers: DwellSnap, Golfr, Autopix, Quadrant2, MOV.
- No revenue figures disclosed; product is pre-commercial launch (slide 16 implies launch occurs post-funding: "Launch the product" at Y1 3–6 months).

## Competition / moat

- Competitive set identified:
  - API Gateways: MuleSoft, Azure, AWS
  - Bug Trackers: Bugsnag, Sentry, Rollbar
  - Documentation & Testing: Postman, Swagger, RapidAPI
- Treblle's claimed differentiation: only product combining real-time monitoring, auto-generated docs, one-click testing, error tracking, API analytics, quality scoring, and device/location detection in one tool. Competitors described as covering each area only "partially".
- Moat framing: deep SDK-level intelligence (not code-level only), focused on real-life API workflows and diverse team use cases.

## Team & funding ask / use of funds

- Vedran Cindrić - Full-stack developer, 15 years experience.
- Darko Blažević - Designer, 15 years experience.
- Tea Šakić - iOS developer, 5 years experience.
- All three previously co-founded Flip, a web/mobile development agency (est. 2011, 100+ clients, 100+ projects, avg. budget €50K–€100K, markets: USA, Norway, Dubai).
- Funding ask: €1.5–2 million.
- Use of funds - post-funding milestones:
  - Y1 Q1: Product launch prep + hire full-stack developer.
  - Y1 Q2: Product launch + hire CMO.
  - Y1 Q3: Hire Customer Success Manager, onboard users.
  - Y1 Q4: Hire Sales Manager, boost sales.
  - Y2 Q1: Expand features + hire front-end developer.
  - Y2 Q2: Push sales on new features + hire back-end developer.
  - Y2 Q3–Q4: "10x value" feature round; continue growth.
- No valuation or equity split disclosed.

---

## Recommended financial model

- **Archetype + why:** SaaS ARR / subscription model with volume-based tier migration. Revenue is purely recurring subscription; pricing tiers are defined; developer-led bottom-up growth makes cohort-based subscriber forecasting natural. No marketplace, no usage metering beyond tier thresholds.
- **Forecast horizon & granularity:** 3 years (Y1–Y3), monthly for Y1, quarterly for Y2–Y3. Deck's post-funding milestones are quarterly, making monthly Y1 natural for headcount and cash burn tracking.
- **Key drivers & assumptions:**

| Driver | Value | Source |
| -- | -- | -- |
| Free tier conversion rate to paid | 5% of free signups convert within 90 days - typical PLG B2D benchmark |
| Paid subscriber growth (new logos/mo) | starts at ~5/mo post-launch (Y1 Q2), scaling to ~30/mo by end Y2, reflecting small developer community GTM |
| Tier mix at entry | 70% Small ($50), 20% Medium ($200), 10% Large ($500); Enterprise negligible Y1 |
| Monthly tier upgrade rate | 5% of paid subs upgrade one tier per quarter as API traffic grows |
| Monthly churn rate | 2% gross monthly churn - early-stage B2D benchmark |
| ARPU blended (at launch) | ~$80/mo based on 70/20/10 tier mix on $50/$200/$500 |
| Headcount ramp | 4 hires in Y1 (dev, CMO, CSM, Sales), 2 in Y2 H1 (FE dev, BE dev) |
| Avg. salary | €50K/year per hire - Croatia mid-market |
| Founder salaries | €60K/year each × 3 founders |
| Infrastructure / COGS | ~15% of revenue - log storage and compute scale with request volume |
| S&M spend | €10K/mo in Y1 post-launch (community, content, paid); scales with revenue in Y2 |
| R&D spend | primarily headcount (devs); minimal external |
| G&A | €3K/mo flat in Y1 |
| Funding raised | €1.75M midpoint of €1.5–2M range |
| Pre-funding runway | funded by Flip agency cashflow; no burn stated |

- **Scenarios (Base / Bull / Bear - which variables flex):**
  - **Base:** 5% free-to-paid conversion, 2% churn, blended ARPU ~$80, 4 Y1 hires.
  - **Bull:** 8% conversion, 1% churn, faster Enterprise deals in Y2, ARPU lift to ~$120.
  - **Bear:** 3% conversion, 3.5% churn, Enterprise delayed to Y3, ARPU stays at ~$65.

- **Required sheets / outputs:**
  1. **Assumptions** - all drivers in one place with scenario toggle (Base/Bull/Bear).
  2. **Subscriber Model** - monthly free signups → paid conversion → tier mix → churn waterfall → ending paid subscribers by tier.
  3. **Revenue** - MRR / ARR by tier; blended ARPU; net revenue retention.
  4. **Headcount & Opex** - hiring plan per roadmap, salaries, S&M, R&D, G&A.
  5. **P&L (3-year)** - Revenue, COGS (~15%), Gross Profit, Opex, EBITDA.
  6. **Cash Flow & Runway** - opening cash (€1.75M raise), monthly burn, runway to breakeven.
  7. **KPI Dashboard** - MRR, ARR, paying customers, churn rate, CAC (once S&M spend defined), LTV, LTV/CAC, months of runway.

## Frequently asked questions

### Is the Treblle financial model free?

Yes. The Treblle 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.
