Introducing: The Managed-Service-as-Software (M-SaS) Startup

A technology disruption like AI is especially powerful for startups when paired with a business model disruption.

In November of last year, I first wrote about the new paradigm of AI agents becoming the fundamental technology driving a new era of software: Services-as-Software, where startups provide service-oriented, outcome-driven solutions to their customers using AI agent technology. With this change in how startups would deliver value came many new potential business considerations such as pricing which I discussed in detail here. 

One interesting aspect that is still overlooked in this new paradigm, however, is the potential to build according to a very different business model blueprint. If we quickly review the history of how software business models functioned pre-AI:

On-Premise: As software was installed on-premise at the customer site, software was sold in the form of ELAs (Enterprise License Agreements) with an attached support contract. The ELA was essentially a large dollar amount that allowed customers to use the software into perpetuity while the maintenance/support contract was annually recurring in nature. Traditionally, the breakdown between the perpetual license and support was a 90% / 10% split. For example, a VMware vSphere license, illustratively, would be $900K with a $100K support contract, for a total contract size (TCV) of $1M. The customer could use that version of the VMware license forever, but of course, every 2-3 years there would be a refresh cycle which would allow VMware to charge for their latest version. Since a service would be associated with maintenance, only 10% of the ELA would be annually recurring. 

Cloud: In the era of cloud, we were able to deliver software directly to the customer without coming on site for installation. Since everything ran and still runs through the cloud, the way we charge for software also changed to a fully recurring model. No longer did we split up the perpetual license against an ongoing support license. Support and license became one, charged annually, and thus SaaS was born. Many new SaaS companies emerged as incumbents struggled to keep up, often due to the challenges of replatforming to the cloud and their entrenched ways of selling software. 

With every new technology development comes a new business model disruption. With AI at our fingertips, a new path has been forged for startups to consider when building the next generation of companies, which I call Managed Services as Software. 

Consulting and Software: An “It’s Complicated” Love Story

“Managed” typically implies a human labor component for service delivery, and this is no different. Many large product companies began as consulting businesses, gradually shifting as they achieved product-market-fit with a particular product they initially provided through white-glove service for clients before transitioning to SaaS. FreshBooks, Mailchimp, and UIPath are just a few examples of giants that made that transition. Turning a service business into a SaaS business is now a textbook strategy, provided you can find the right product to build your software company around. 

However, a few companies have taken a different approach, remaining in a traditionally labor-intensive, consulting-led services world, but using technology to drive better margins over time. Expel and Arctic Wolf are both cybersecurity players that have embraced a software-enabled service business model. In a way, both have effectively outsourced the role of Security Operations Center (SOC) and delivered it back to customers through a managed experience, known in the security world as a managed detection and response (MDR) service. 

Internally, however, they have continued to innovate and automate their operations to drive towards SaaS-like margins. Through rigorous operations and automation, these businesses, traditionally with 20-30% gross margins due to high labor costs, have willed themselves to 70-80% gross margins over 8-12 years. The MDR product space became so lucrative that even product companies like CrowdStrike, which originally focused on endpoint agent technology, entered the market with their own MDR offering, CrowdStrike Falcon Complete. 

Sell AI or use AI yourself? Introducing the Managed-Service-as-Software Startup

Philosophically, the biggest change is that instead of trying to sell AI to customers, startups should think about delivering value by using the AI they built themselves. You might ask, “Hey, doesn't that make me a consulting business?” – and the answer is partially yes. M-SaS businesses are AI powered services businesses that over time drive from low (20-30%) gross margins, while labor intensive, to high / SaaS-like (70-80%) gross margins, when AI intensive. The service delivered remains unchanged to the customer throughout the journey.

Following the principles of the Managed Detection and Response market, there is an opportunity for the new generation of startups to mimic the operational prowess of Expel and Arctic Wolf. However, in this time frame these new companies can use their era-appropriate technology of LLMs, agents, and GPUs instead of business logic software, static automation, and CPUs. The net result could mean that accomplishing 70% gross margins as is typical with software companies could be achieved in maybe less than half the time than it took the previous generation, all pending the cost of GPU declining at a faster rate as the cost of labor (which is really only increasing). 

Starting with a labor-intensive service delivery model requires upfront investment in both automation scaffolding for scale, as well as a competent human service desk. In a way, a M-SaS company never has to pass the Turing test because in its initial stage of service delivery, it is largely human. The goal of the engineering team would be to supercharge the human service desk employee and to enable them to service an increasing amount of customers over time, leveraging automation & AI tooling. On the other side, the customer experiences consistent service without necessarily knowing or caring if AI is used in the process. The graphic above demonstrates this (with completely illustrative timeline and figures): initially, each service operations employee can service a small number of customers, but over time, this scales up significantly to ten and then hundreds of customers. Since labor required per customer is decreasing while automation & AI is increasing (assuming falling compute GPU costs), gross margins would improve. The following summarizes the basic building principles for a M-SaS business. 

1. Labor-First Approach: Initially, focus on delivering an excellent client experience through a labor-intensive model. Although gross margins will suffer initially, this sets the stage for long-term success.

2. Build AI Tooling and Automation: Develop AI tools and automation to make your operations & service delivery desk more efficient, aiming to serve more clients per unit of labor over time. This will gradually reduce the reliance on labor and increase efficiency.

3. Increase GPU Utilization: As technology and automation improve, the ratio of labor per client will decrease, allowing for higher margins and better service delivery.

4. Operational Monitoring: Maintain rigorous operational monitoring to ensure uptime and efficiency. High inference costs from GPUs may initially be higher than labor costs, but these are expected to decrease over time, justifying the investment long term.

What this means in practice for M-SaS businesses is that it is acceptable to start with low or even negative gross margins due to upfront investments in both labor and compute costs, especially when customer count is low or non-existent at the beginning. However, the path from 0% to 70% gross margins should be closely monitored, and M-SaS founders should maintain full operational intelligence at every step. This requires a fundamental mindset shift from the typical software business model. You are not just delivering software but a full-fledged service, which involves managing not only engineering, product, sales, and marketing, but in particular operations.

Conclusion

The market is increasingly willing to pay for outcomes rather than traditional software tooling. Whether your AI tools are used internally to drive efficiency or sold to external customers, the key is their usage. Modern startups can potentially do both, making the question more about sequencing rather than where you start and end up. Leveraging the new paradigm of AI and decreasing GPU costs should eventually lead to the creation of more M-SaS driven companies. These companies will transform from labor-intensive operations to technology-enhanced services with SaaS-like margins and, consequently, SaaS-like valuations. 

As always, if you are interested or working on an idea on this topic please reach out to me or join our Founder Catalyst Community!

Special thank you to Amil Naik who helped me clarify my thoughts and pull this piece together.