Cloud computing has revolutionized how businesses operate, but with this transformation comes complexity. Today’s IT environments are no longer simple—they are dynamic, distributed, and constantly changing. Applications run across multiple clouds, use microservices, and rely on containers for flexibility. It is widely acknowledged that the volume, velocity, and variety of telemetry data generated by such complex and distributed systems often exceed human capacity to analyse or triage it in real-time. This is where AI steps in, by learning normal system behaviour, predicting issues before they escalate, and automating first-line responses, thereby significantly enhancing observability capabilities compared to traditional monitoring tools, which only track logs and basic metrics.
Industry analysts highlight the urgency: Gartner predicts that, by 2029, half of all cloud computing will support AI and machine learning workloads, demanding far deeper visibility. IDC reports that 60% of enterprises need major cloud modernization to enable rapid AI adoption. As data signals multiply and multi-cloud setups grow, businesses need more than monitoring—they need cloud observability, a smarter approach that helps teams understand system behaviour, anticipate failures, and prevent costly disruptions.
In a digital-first world, real-time insight isn’t optional—it’s a competitive advantage. So, what makes modern and hybrid applications so different, and why does this matter?
Breaking down the differences between modern and hybrid applications
Modern applications are built entirely for the cloud, using small, independent components called microservices and tools like containers to deliver speed and scalability. They power platforms such as Netflix and Shopify, enabling rapid innovation. Hybrid applications, on the other hand, combine old and new—keeping critical systems on-premises while using the cloud for customer-facing apps or analytics. This mix offers flexibility but adds complexity, making visibility across both environments essential. Cloud observability bridges this gap by providing a unified view, helping businesses predict issues, control costs, and maintain performance.
What are the key challenges of monitoring hybrid and modern applications?
Managing modern and hybrid applications is complex because they span multiple clouds, on-prem systems, and dynamic architectures. This creates blind spots that delay issue detection and affect service levels, ultimately hurting customer trust.
Distributed architectures
Applications run across on-prem, cloud, and edge environments, making it hard to see the full picture. Without unified visibility, teams struggle to detect problems early and maintain performance.
Multi-cloud complexity
Using multiple cloud providers often leads to fragmented data and compliance risks. Without automation and cost governance, businesses face unpredictable expenses and gaps in security.
Microservices and containers
Modern apps rely on microservices and Kubernetes, where workloads are short-lived and constantly changing. This makes anomaly detection harder and increases alert fatigue when teams juggle multiple tools.
Lack of real-time insight
When monitoring is reactive, issues surface only after users are affected—potentially leading to revenue loss and reputational damage. Organizations need predictive analytics to prevent disruptions and ensure compliance.
Why is this important to the business and not just IT?
Application health and uptime is critical to most businesses. When you add the need to innovate and constantly adapt, inaction is not a viable option. Therefore, taking proactive steps is essential. Aside from the direct cost to IT to find and remediate problems, it also affects revenue — with lost customers, productivity, and orders — and damages longer-term customer and employee satisfaction. In a complex, evolving environment, it becomes critical to reduce mean time to detect and recover, while being precise on finding causality.
The retail and hospitality sectors are particularly susceptible to the effects of downtime. Research by Dynatrace, FreedomPay, and Retail Economics shows that payment system failures cost U.K. businesses £1.6 billion annually, with an average of five severe outages each year. Alarmingly, 61% of these incidents occur during peak trading periods, amplifying revenue losses and harming customer satisfaction.
The partnership between Wipro and Dynatrace has enabled enterprises to achieve significant improvements in reliability, efficiency, and cost optimization by combining advanced observability with automation and deep domain expertise. This collaboration helps businesses move beyond reactive monitoring to proactive, predictive management—reducing downtime, improving customer experience, and accelerating innovation.
How does Wipro approach hybrid and modern application monitoring?
Applied Observability Automation (AOA) is Wipro’s approach to making cloud observability smarter and more actionable. It focuses on turning raw data into meaningful insights and automated responses. Built on four principles—observe, detect, AI-driven insights, and intelligent remediation—the framework helps organizations move from simply tracking issues to predicting and preventing them.
- Observe: Collect data from applications and infrastructure to understand what’s happening in real time.
- Detect: Identify anomalies and patterns by connecting this data with enterprise systems for situational analysis.
- AI-driven insights: Use artificial intelligence to turn data into actionable insights for root-cause analysis, forecasting, and performance optimization.
- Intelligent remediation: Automate fixes using advanced tools like robotic process automation to resolve issues quickly and efficiently.
This approach addresses three major challenges facing businesses today:
- Tools span: Data scattered across multiple environments and tools, making it hard to get a unified view.
- Lack of real-time insights: Limited ability to predict issues or understand their impact on critical business processes.
- No single source of truth: Disconnected data between business operations and IT operations, leading to slow decision-making.
By combining these principles with automation and AI, enterprises can achieve faster root-cause analysis, proactive anomaly detection, and automated remediation—reducing downtime and improving resilience without adding complexity. This approach has delivered measurable impact across industries:
- A leading telecom provider eliminated siloed monitoring and improved visibility across over 1,000 apps and 85,000 hosts, doubling team productivity and enhancing customer experience by 90%.
- In energy and utilities, zero-touch automation reduced false alerts by 60%, halved MTTR, and boosted customer satisfaction by 40%.
- A global healthcare organization achieved zero downtime during migration, cut MTTA to five minutes, and saved $1.7M through licensing optimization—all while strengthening monitoring maturity across thousands of servers.
These outcomes highlight how intelligent observability drives resilience and tangible business value.
The path forward: Building resilient monitoring for modern and hybrid applications
As organizations modernize their digital ecosystems, resilient monitoring becomes essential to keep pace with distributed architectures. Building this foundation requires capabilities that address the realities of multi‑cloud, hybrid, and cloud‑native environments.
- Modern IT environments demand more than basic monitoring—they need capabilities that solve real-world challenges.
- End-to-end visibility across hybrid setups ensures teams can see both cloud-native and on-prem systems in one view, reducing blind spots and speeding up issue resolution.
- Deep monitoring of microservices and containerized applications helps maintain performance in dynamic Kubernetes environments, preventing disruptions before they affect users.
- Multi-cloud observability brings data from Amazon Web Services, Microsoft Azure, and Google Cloud together, enabling cost control and compliance across providers.
- Full-stack insights—from infrastructure to user experience—allow proactive detection of anomalies, while linking technical health to business transactions ensures teams prioritize fixes that protect revenue and customer trust.
The Wipro and Dynatrace partnership supports these capabilities, helping organizations move from reactive monitoring to predictive, business-driven observability. This partnership brings together the Dynatrace AI-powered, unified observability and security platform with Wipro’s domain expertise, global delivery, and innovative frameworks to accelerate digital transformation. By combining Dynatrace technologies like Davis® AI, Smartscape®, and Grail® with Wipro’s Cloud Studio™ and Applied Observability Automation, this alliance delivers unified visibility, intelligent automation, and measurable business outcomes.
Together, Wipro and Dynatrace provide tailored, end-to-end solutions that reduce operational costs, mitigate risk, and drive innovation.
To learn more about our joint offerings, visit our website and contact us.
