“IT devops teams can learn from SaaS developers that observability isn’t just about monitoring systems after deployment—it’s about embedding real context into every stage of development,” says Noam Levy, founding engineer and field CTO at groundcover. “Modern observability tools, especially when paired with AI, help engineers anticipate regressions before they happen in production environments, guiding safer code changes and more reliable releases. This shift from reactive troubleshooting to proactive reliability mirrors how leading SaaS teams continuously refine and reinforce trust in their software.”
The importance of observability was a common theme among SaaS leaders, and many standardize it as a devops non-negotiable. But logging every bit of information can become expensive and complex, especially when AI agents log all interactions.
“As AI-driven systems generate exponentially more logs, metrics, and traces, tightly coupled observability stacks can’t keep enough data hot without driving up costs or offloading it into slow, hard-to-query cold storage,” says Eric Tschetter, chief architect at Imply. “With an observability warehouse as the scalable data layer, teams keep telemetry data accessible at scale without increasing costs.”
