SaaS (Software-as-a-Service)
Definition
SaaS is a software distribution model in which a vendor hosts an application in the cloud and provides access to customers on a subscription basis. The vendor manages infrastructure, maintenance, updates, and security — customers pay for access, not ownership.
Core Ideas
Business Model Mechanics
Key Metrics
- MRR (Monthly Recurring Revenue) — baseline monthly subscription revenue
- ARR (Annual Recurring Revenue) — MRR × 12
- Churn Rate — percentage of customers/revenue lost per period
- LTV (Lifetime Value) — average revenue per customer over their entire relationship
- CAC (Customer Acquisition Cost) — total sales/marketing spend ÷ new customers
- LTV/CAC Ratio — must be > 3 for a healthy business; > 5 is great
Revenue Formulas
- MRR = (# customers) × (avg monthly revenue per customer)
- Net Revenue Retention = (MRR end of period from existing customers) ÷ (MRR start of period)
- Payback Period = CAC ÷ (MRR per customer × gross margin)
Pricing Models
| Model | Description | Best For |
|---|---|---|
| Per-seat | Price per user/month | Team collaboration tools |
| Usage-based | Price per API call, GB, event | Infrastructure, developer tools |
| Freemium | Free tier; charge for advanced features | Consumer products, bottoms-up B2B |
| Flat-rate | Single price for all features | Simple products, small markets |
| Tiered | Feature-gated tiers (Starter/Pro/Enterprise) | Most B2B SaaS |
The Pricing Ladder (a worked case)
Pricing rarely starts where it ends. One observed progression, from a solo trading-data product (TradeInsight) into an AI agent product:
- Free — first three months, ~300 users. The signal being bought here is do people like it, nothing else.
- Paid, priced per period — conversion was poor; the cancellation reason volunteered most often was simply “too expensive.”
- Subscription + usage credits — a plan that unlocks the same capability across CLI and web UI, metered by consumption.
- Enterprise tier — for the buyers that need contracts rather than credit packs.
- Pay-by-output (aspirational) — the customer pays only for output produced.
Two things are worth extracting:
- Retention data beats conversion data for diagnosing pricing. Across two years: ~1000 free users, 221 people who ever started a trial or paid, and 17 still there. A funnel that leaks 92% of paying-intent users is a product problem wearing a pricing costume — repricing would not have fixed it.
- Pay-by-output requires confidence and infrastructure. It only becomes viable with faster product iteration, scalable infrastructure, and an enterprise motion — which is why this rung usually stays a long-term goal rather than a launch strategy.
The Vertical Harness Pattern
A specific shape of AI-native vertical SaaS: take a general-purpose agent harness that has proven itself in one domain, and rebuild it for another.
The template case is Claude Code — it stood out among AI agents by being CLI-first and narrowly coding-focused before expanding. The transplant: “Claude Code for coders, X for quant analysts and traders” — the harness, tools, and domain skills wrap the LLM, and the model itself is initially bring-your-own-subscription, later served through the platform.
Why the shape works commercially:
- The harness, not the model, is the product — so model progress is an input, not a competitor.
- Vertical depth (domain skills, data access, evaluation) is the moat, matching the vertical-vs-horizontal table above.
- BYO-model at launch removes inference COGS from a pre-revenue product; bringing models in-platform later converts that cost into a pricing lever.
Go-To-Market
- Bottom-up (PLG) — product-led growth; users adopt, then companies buy
- Top-down (SLG) — sales-led; direct outreach to enterprise buyers
- Community-led — build audience around a problem, then productise
- Content-led — SEO, blog, YouTube → inbound leads
Vertical vs Horizontal SaaS
| Vertical | Horizontal | |
|---|---|---|
| Target | One industry (e.g. legal, healthcare) | Cross-industry (e.g. CRM, HR) |
| Competition | Less direct competition | Highly competitive |
| Pricing power | Higher (specialist expertise) | Lower (commoditised) |
| Market size | Smaller | Larger |
AI-Era SaaS Considerations
- AI as a feature (copilot, summarisation, classification)
- AI-native products built on LLMs from day one
- Moats: proprietary data, fine-tuned models, deep workflow integration
- Cost structure: inference costs as COGS; margins compressed vs traditional SaaS
Relationships
- Entrepreneurship & SaaS — execution context for SaaS businesses
- AI & Machine Learning — AI-powered SaaS and LLM integrations
- Cloud & AWS Infrastructure — cloud as SaaS delivery infrastructure
- Trading & Finance — TradeInsight.info as a finance SaaS product
- AI Engineering Pitfalls — why an unguaranteed model API becomes the product’s SLA, and why bring-your-own-key breaks the SaaS bargain