Many enterprise devops teams struggle to deploy frequently, increase test automation, and ensure reliable releases. What can they learn from SaaS companies, where developing and deploying software for thousands of customers is core to their revenue and business operations?
SaaS companies must have robust testing, observability, deployment, and monitoring capabilities. One bad deployment can disrupt customer operations, unravel sales opportunities, and attract negative media coverage.
What’s most challenging is that many SaaS platforms are configurable and have low-code development capabilities. These platforms require robust test data sets and real-time monitoring to ensure that deployments don’t break functionality for customers. Impacting even a tiny fraction of customers is an unacceptable outcome.
Validating data entry forms and end-to-end workflows is a combinatorial problem that requires building robust data sets and testing a statistical significance of input patterns. Further, developing, integrating, and deploying AI agents and language models adds new complexities. In enterprises, testing open-ended AI agents with non-deterministic responses becomes a greater challenge as more organizations use third-party AI agents and move AI experiments into production.
I asked SaaS providers to share some of their devops secrets. As more enterprise devops teams develop, deploy, and support mission-critical apps, integrations, and data pipelines, how can they improve resiliency? Look to the practices of SaaS providers.
Aim for smart ‘customer’ upgrades
If you deploy an upgrade, will end users take advantage of the new capabilities, or will they be frustrated by defects that leaked into production? Enterprises must aim for smart customer upgrades that are seamless for end users, are deployed frequently, have low defect rates, avoid security issues, and drive adoption of new capabilities.
To consistently meet these objectives, enterprise devops teams must embrace a shift in mindset, away from legacy IT norms. They must recognize that:
- End users are customers and disrupting workflows affects business operations. Improving deployment frequency but shipping with defects is not a win for SaaS, nor should it be for enterprise IT.
- Deploying capabilities that few people notice, try out, and adopt is highly problematic. It implies that the team invested time and resources without delivering business value and likely introduced new technical debt.
- Deploying software is never a one-time effort, and agile teams should communicate their release management plans.
“The most successful devops teams realize that their internal platform is actually a specialized SaaS product where the developers are the primary customers,” says Sergio Rodríguez Inclán, senior devops engineer at Jalasoft. “By replacing rigid project deadlines with a commitment to continuous reliability and self-service automation, IT shifts from being a corporate bottleneck to a competitive advantage.”
One transformation many enterprises are undertaking is a shift to product-based IT, which helps group applications into products and assigns product managers to oversee their roadmaps. Most SaaS companies assign product managers to communicate product vision, define user personas, understand customer needs, prioritize features, and measure business outcomes.
Esko Hannula, SVP of robotics at Copado, says, “Modern enterprise IT should adopt the SaaS mindset. Software isn’t a project with an end date but a continuously improving product delivered through frequent, incremental releases.”
Hannula recommends reviewing the devops practices used by SaaS teams, including advanced CI/CD, continuous testing, canary releases, A/B testing, and data-guided product management, to be able to release whenever needed. “These practices matter because they create the confidence, agility, and quality necessary for rapid response to business change—outcomes that naturally follow from treating software as a long-lived product rather than a one-off project,” Hannula says.
Code less and test more
Developers in enterprise IT are using AI code generators and experimenting with vibe coding tools. Research shows that these tools can improve developer productivity by 30% or more. But will productivity translate into devops teams deploying features faster and more reliably?
Enterprise IT has a long history of underfunding testing and targeting big-bang deployments. SaaS companies do the opposite. They apply analytics in test automation, build synthetic test data sets, and use feature flagging to reduce the risks of deploying more frequently. The more advanced SaaS companies adopt continuous deployment, but this may be challenging to implement for many enterprises.
“Test automation may feel like an upfront cost, but it pays off quickly because more resilient services lead to fewer incidents, fewer support tickets, and lower operational overhead,” says Nikhil Mungel, Director of AI R&D at Cribl. “SaaS teams often de-risk launches by releasing features to small groups first and using observability to watch system vitals and user experience before broad release, typically via feature flags and bucketing. IT devops teams can mirror this by enabling ‘power users’ to opt in early, improving satisfaction while reducing support burden.”
Secure in design, development, and deployment
The productivity improvement from code generators may come at a cost. The same study noted above, which showed improved developer productivity and code maintainability, also found that 23.7% more security vulnerabilities were introduced in the code generated.
Shifting security left sounds straightforward, but in reality, it’s a broad agenda for enterprise devops teams that want security on equal grounds with dev and ops priorities. To become devsecops, agile teams must address security and compliance beyond application security, including cloud infrastructure hardening, identity management, data security, and AI model bias.
“SaaS teams win when they embed security into their codebase with dependencies that upgrade seamlessly,” says David Mytton, CEO of Arcjet. Mytton recommends these four practices:
- Test suites with automated security and privacy checks that flag when dependencies break.
- Standardized observability with structured traces and context-rich logs.
- Privacy-aware data management and in-app PII redaction with CI/CD gates.
- Feature flags and canary rollouts to move fast without breaking customers or compliance.
Priya Sawant, GM and VP of platform and infrastructure at ASAPP, adds that modern SaaS teams shift left by baking security, testing, access control, and observability into the design and CI/CD pipelines rather than patching them in at the end. “Automating permissions, enforcing golden-path pipelines, and delivering built-in observability removes friction, improves quality, and accelerates delivery. IT and devops teams that adopt this model move faster and scale more reliably than those stuck in manual approvals and reactive workflows,” Sawant says.
Start planning for resilient operations from Day 0
Back when I was a CIO, I once was asked what our Day 2 model was for a new application we were building. Day 2 is legacy terminology for when an application is deployed to production and requires support, as opposed to Day 1 (development) and Day 0 (planning).
SaaS teams have a very different mindset around operations, and they start planning for scalability, security, performance, and incident management from the outset in the architecture design.
For example, SaaS companies place a lot of emphasis on their developer experiences. Developers who are too busy tinkering with cloud configurations, manually patching components, or handling data management tasks can lose focus on customer needs.
“SaaS engineers use technologies that don’t overcomplicate things and let them move fast without wrestling with upgrade paths,” says Alejandro Duarte, developer relationships engineer at MariaDB.
Duarte recommends choosing infrastructure that doesn’t slow down developers. For example, at the data layer, Duarte prioritizes systems that support native replication, vector storage, fast analytics, and automatic node recovery.
Define an observability strategy, then implement it
Another SaaS-inspired mindset shift is from Day 2 monitoring of applications as black boxes to Day 0 observability, providing ops teams with the details needed to aid incident management and root cause analysis. In enterprises, establishing observability standards is essential because operations teams track alerts and incidents across hundreds to thousands of applications.
“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.”
Ang Li, director of engineering at Observe, shares a good rule that SaaS teams use to decide what information to include in their standards. “SaaS engineering teams design observability around users and workflows, not just whether systems are up or down. IT devops can apply the same thinking, moving beyond uptime monitoring to instrumenting critical business transactions to better understand user impact, limit blast radius, and recover faster,” says Li.
Key takeaways
We can distill two key takeaways for enterprise devops teams from the recommendations our experts shared above. First, apply product management practices, focus on features that matter, and develop robust testing. Second, shift left in practices, not just culture, by considering observability, security, and resiliency as part of the solution’s architecture.
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