2024: A Milestone Year for Vertical SaaS IPOs
2024 was a slow year for tech IPOs in the US. One of the more successful ones was from ServiceTitan- a vertical SaaS for field service contractors. The company, founded in 2007 and funded by the likes of Bessemer Venture Partners, had a great start on the bourses. The stock popped about 42% on the first day and is now trading at about $104 per share, giving it a market cap of about $9 billion. The success of the ServiceTitan IPO gave early backers like BVP great returns, outperforming the home-run scenario projection by about 8x! This successful offering also brought the spotlight to vertical SaaS. In the era of AI, we feel this is a precursor to the impending success of vertical AI.
Why Vertical SaaS is an Interesting Space
Before diving into vertical AI, let us take some cues from vertical SaaS to understand why vertical is an interesting place to be. Euclid VC did a great analysis of listed vertical and horizontal SaaS stocks. A key finding is that the forward revenue multiple for vertical SaaS is higher than for horizontal peers (6.2x v/s 6.1x – median). This goes against the conventional wisdom that “vertical SaaS markets are inherently smaller than horizontals and so they would trade at lower multiples.” The reality is that investors are giving a premium to vertical SaaS. If you dig deeper, the market is essentially pricing in what sets vertical software apart from its horizontal cousins:
- Ability to Command Higher Market Share: Compared to horizontal SaaS companies, dominant vertical SaaS products generally achieve much higher market share. Doximity, for example, has an 80% market share, while top horizontal ones usually cap at 30-50%. The brand power of vertical software translates to deeper penetration, achieving a similar scale with 20-25% of the market size.
- Efficient Go-to-Market (G2M): Since brand pull and word-of-mouth work far better in vertical SaaS, G2M efficiency is much better than the broader software cohort. This translates into better free cash flows at scale.
- Increasing Annual Contract Values (ACVs) and Platform Potential: A vertical tool starting with a niche use case usually quickly expands to adjacent use cases and markets. Shopify and Toast are great examples of this, where a significant portion of their revenue now comes from other services.
Supercharging Vertical SaaS with AI
As we are all seeing now—”AI is eating SaaS.” This doesn’t mean that SaaS is dead (not yet, anyway), but it is evolving, taking a new shape, and becoming AI software. We believe that B2B AI is taking the shape of compound systems that bring together agents, human workers, software, data, and workflows to bring intelligence and automation to the enterprise. Our portfolio company, SuperAGI, exemplifies this. When you take the structural advantages of vertical SaaS and supercharge it with AI, you get a powerful vertical AI software system.
Advantages of Vertical AI
Vertical AI inherits the structural advantages of vertical SaaS in market share, sales efficiencies, and the ability to add product lines. We also see AI bringing much more value to vertical AI systems than to general-purpose B2B AI systems.
- Higher Accuracy: Vertical AI systems can achieve better accuracy by design. Constrained and vertical-specific environments allow developers to build better guardrails, evaluation systems, and edge-case solutions. Fine-tuning/pre-training with category-specific datasets results in potent and accurate systems.
- Better Workflows and Customer Experience: Vertical AI systems deliver tailored workflows and contextual AI, providing superior user experience and value derivation compared to generic horizontal AI products.
- Tangible ROI for Customers: Vertical AI products have lower inherent costs, providing higher business ROI and faster time to value for customers.
- Data Handling and Moats: Vertical AI systems derive greater value from structured and unstructured datasets. Reinforcement/fine-tuning loops create stronger data flywheels and moats.
By harnessing the power of AI and LLMs, vertical AI products can add value to core (e.g., writing contracts for the legal industry) and ancillary (e.g., customer communication/training) use cases. This makes them more powerful than horizontal products, which are usually not used for core tasks. Harvey for Legal and Hippocratic AI for Healthcare are early examples of companies leveraging the vertical strategy.
A Case for India
While India has produced great horizontal SaaS companies like Zoho and Freshworks, we also have a good track record in building profitable vertical global SaaS companies. Tracxn and RateGain exemplify this, with founders scaling globally in capital-efficient ways and taking their companies public. We believe India’s advantage lies in building vertical-specific but global AI companies efficiently. Categories that appear niche from the outside, when multiplied by AI value addition, offer the potential for building multiple billion-dollar vertical AI companies.
Call to Action for Indian Entrepreneurs
We believe that Indian entrepreneurs should target categories where they have domain understanding equal to or better than their global counterparts. Industries like manufacturing, real estate, and retail might seem non-glamorous, but this is actually an advantage as they are often less competitive.
While horizontal SaaS and AI product development demand technical expertise, vertical AI requires teams with deep domain and AI expertise. Ara and Vahe of ServiceTitan exemplify strong founder-market fit.
If you deeply understand an industry and see how AI can disrupt it, it’s time to start building. And reach out to me (gaurav@kae-capital.com)—there’s no such thing as “too early” for us at Kae.