At Index Ventures, we view the emergence of vertical SaaS (vSaaS) — cloud-based software tailor-made for specific industries — as part of a broader trend of end users increasingly demanding superior technology products.
Consumers want solutions-oriented software made specifically to solve their exact business problems. In an environment where we are inundated with software, narrow and specific is well positioned versus broad and generalized.
The concept is not new: Even the largest horizontal tech companies verticalize their sales organizations and product features when they have enough scale within each vertical for that to be a sensible approach.
Cloud giants AWS, Azure and Google Cloud Platform prominently feature vertical industry solutions with dedicated sales teams, as do other large platforms like Salesforce, ServiceNow, Snowflake and Workday.
These tech leaders verticalize their offerings over time because it’s a high-quality experience for customers and end users when a technology vendor deeply understands the industry, has sales and support reps attending the same conferences as users and is rapidly evolving the product to suit customer needs.
The AI category is rapidly evolving but developing into three layers: foundational models, AI infrastructure and AI applications.
With the AI platform shift upon us, we believe that the next logical iteration of vertical SaaS will be vertical AI – vertically focused AI platforms, bundled alongside workflow SaaS, built on top of models that have been uniquely trained on industry-specific datasets.
Why vertical AI?
The AI category is rapidly evolving but developing into three layers: foundational models, AI infrastructure and AI applications.
Examples of AI stack startups. (Index Ventures is an investor in Causaly, Cohere, Scale, ServiceTitan and Weaviate.) Image Credits: Index Ventures
Foundational models are the bedrock of the AI stack. Leaders in this space include Anthropic, Cohere and OpenAI. It’s likely there will be a limited number of vendors in the foundational LLM space given the high capital requirements to build and train models.
The “picks and shovels” of AI sit at the infrastructure layer, a catch-all that includes a variety of categories including data enhancement, fine-tuning, databases and model-training tools. For example, vector databases like Pinecone and Weaviate are gaining significant adoption.
Other companies like Scale are being used for data generation, labeling and training. Hugging Face has emerged as a leader for model discovery and inference. Weights & Biases is widely recognized within MLOps. LangChain is an open source development framework used to simplify the creation of new applications using LLMs. These are a few of many companies that are helping companies transform models and data into products.
Foundational models and infrastructure are enabling an explosion of AI business applications. These AI-powered applications could be used by any end user, in any industry, to accomplish an array of tasks.
