How Momentum minimizes the number of engineers on-call with Rootly AI.

Momentum wanted to scale reliability without scaling on-call pain

Momentum is growing fast, serves global customers, and needs 24/7 on-call coverage. But like many modern engineering teams, it also wants to be careful about how much operational burden it places on engineers as the company scales. The public story frames the challenge clearly: reliability is non-negotiable, but so is being thoughtful about team growth and the churn that on-call shifts create.  

That tension is what makes the Momentum story compelling. This is not just a company looking for a better incident workflow. It is a company using AI to rethink the economics and human cost of operating reliably at scale.  

“AI is evolving fast. And Rootly lets us get the latest on AI, fast. For us, it’s about how many engineers we hire, and how many we need to wake up at 3 am, and that makes the difference.” — Santiago Suarez Ordoñez, CEO and Co-Founder  

Rootly AI helped Momentum make leaner on-call possible

At the center of the story is a simple operating goal: keep reliability high without needing to keep expanding the number of engineers dragged into incidents and overnight escalation. Momentum found Rootly AI compelling because it was the most comprehensive incident management solution it saw, and because the team believed Rootly’s AI SRE vision was moving in the right direction quickly. The public page says Momentum is excited by how fast the product evolves week to week.  

That matters because Momentum is itself an AI-forward company. It wanted a partner that was not just shipping incident tooling, but actively pushing toward a future where incident response becomes more automated, more intelligent, and less labor-intensive.  

“I chose Rootly over FireHydrant due to the team’s vision, performance, and trajectory.”  — Santiago Suarez Ordoñez, CEO and Co-Founder

Rootly AI bridged the gap between developers and operators

One of the strongest angles in the Momentum story is how Rootly AI reduces the knowledge gap between the people who run systems and the people who write application code. The case study describes a familiar problem: the person holding the pager at night may understand infrastructure but not the application internals, while developers may understand the code but not always the operational steps required in production.  

Momentum says Rootly AI helps on both sides. Developers can ask operational questions, like how to restart a pod, while SREs can ask application questions, like how to spin up a development environment. In practical terms, that makes on-call more distributed and more resilient because fewer incidents depend on a narrow set of specialists being awake and available at the same time.  

This is an especially good fit for a refreshed Rootly story because it shows Rootly AI not just as an automation layer, but as an organizational leverage layer. It helps teams share context across traditional engineering boundaries.  

From parallel firefighting to one responder handling more

Momentum’s CTO is clear that the goal of AI in incident response is not to replace SREs. It is to remove toil so skilled engineers can focus on higher-value work. The case study says Rootly AI supports responders across the incident lifecycle, from LLM-based stakeholder communications to ML-driven root cause analysis. It also says responders can have AI execute runbooks that previously had to be followed manually with extreme care during tasks like rebooting servers or restoring databases.  

That leads to the strongest operational outcome on the page: Rootly AI enables the right person to handle an incident alone in situations where a team previously had to work in parallel for minutes or even hours. Even without a hard number, that is a major before-and-after story. It speaks directly to cost, speed, and engineer focus.  

“With Rootly AI, the right person alone can handle an incident, versus in the past, a team working in parallel, all of them taking minutes if not hours to complete their tasks.” — Santiago Suarez Ordoñez, CEO and Co-Founder  

Momentum bought into Rootly’s direction as much as the product

Another distinctive part of this story is that Momentum is not just buying a tool for today. It is using Rootly’s roadmap to inform how it expects its engineering organization to evolve. The case study says Momentum counts on the roadmap as it plans the future of its engineering team, which is a strong signal that the customer sees strategic value in the vendor relationship, not just feature value.  

That theme also comes through in Santiago’s comments about Rootly’s execution. For Momentum, the decision came down not only to current capabilities, but to confidence that Rootly had the right vision and could keep pace with the speed of AI development.  

“I bet on the team that is executing. I see them perform, I buy into their vision. Of course, you also want to see the customers and the trajectory, and Rootly has that going for them. I’m impressed by the Rootly team and their ability to get there.” — Santiago Suarez Ordoñez, CEO and Co-Founder  

Rootly gave Momentum a reliability strategy that matches its AI strategy

The final line of the public page is the sharpest summary of the story: Momentum relies on Rootly AI to minimize the number of engineers it needs to keep on call, while using Rootly’s roadmap to inform engineering strategy. Because Momentum’s broader business strategy depends on immediate access to the latest AI capabilities, it sees Rootly as a way to keep a competitive edge in reliability too.  

That makes this case study more strategic than tactical. The outcome is not merely a smoother incident process. It is a reliability model designed to help a fast-moving AI company stay lean, reduce overnight burden, and operate with more leverage as it grows.  

Quick fire round: Momentum rates Rootly

• 5/5: AI innovation
• 5/5: On-call leverage
• 5/5: Runbook automation
  
• 5/5: Cross-team knowledge sharing
• 5/5: Strategic partnership

Momentum’s story emphasizes Rootly AI’s speed of innovation, its ability to reduce the number of engineers needed on call, support for AI-assisted communications and root cause analysis, runbook execution, and Momentum’s confidence in Rootly’s roadmap and team. Those are unusually strong signals of both product satisfaction and long-term strategic alignment.