For many retail brands, peak season is the annual stress test of their digital infrastructure. It's also when often technical dashboards glow green, yet customer feedback, digital experience frustration, and conversion trends tell a different story entirely. Over the past several years, we've seen the same pattern across retail, financial services, travel, and media: internal application performance metrics fail to capture the true experience of users connecting over local broadband, mobile carriers, and congested networks using multiple devices across geographies ...

Traditional monitoring often stops at uptime and server health without any integrated insights. Cross-platform observability covers not just infrastructure telemetry but also client-side behavior, distributed service interactions, and the contextual data that connects them. Emerging technologies like OpenTelemetry, eBPF, and AI-driven anomaly detection have made this vision more achievable, but only if organizations ground their observability strategy in well-defined pillars. Here are the five foundational pillars of cross-platform observability that modern engineering teams should focus on for seamless platform performance ...

Every digital customer interaction, every cloud deployment, and every AI model depends on the same foundation: the ability to see, understand, and act on data in real time ... Recent data from Splunk confirms that 74% of the business leaders believe observability is essential to monitoring critical business processes, and 66% feel it's key to understanding user journeys. Because while the unknown is inevitable, observability makes it manageable. Let's explore why ...

Artificial Intelligence (AI) is reshaping observability, and observability is becoming essential for AI. This is a two-way relationship that is increasingly relevant as enterprises scale generative AI ... This dual role makes AI and observability inseparable. In this blog, I cover more details of each side ...

Application Performance Monitoring (APM) has long been the cornerstone of system reliability ... However, the landscape has evolved ... The question is no longer whether APM is important. The question is: What does observability need to become to support this new era? ...

While AI adoption is accelerating, concerns about reliability and trust make it challenging to transition initiatives from concept to production, according to the 2025 State of Observability Report from Dynatrace ...

New Relic's 2025 Observability Forecast ... found that with a median annual cost of high-impact IT outages reaching $76 million, organizations are investing in AI-strengthened observability to detect and resolve issues faster. Here are 5 key takeaways from this year's report ...

The observability landscape has transformed dramatically over the past decade. What began as traditional application performance monitoring (APM) has evolved into something more sophisticated and deeply essential to business operations. As we look at where the industry is headed, three themes have emerged that will define the future of how organizations monitor and manage their digital infrastructure ...

For years, IT Operations has been caught in a loop of reacting to incidents after they've already caused disruption. Even with monitoring, observability, and AIOps 1.0 (legacy AIOps solutions) in place, teams still face overwhelming alert volumes, fragmented data, and slow mean time to resolution (MTTR). The move to Predictive IT Operations offers a better path ...

Most teams collect observability data for the obvious reasons: uptime, latency, troubleshooting. It's the stuff we have to do to keep the lights on. But that mindset limits what this data is really capable of. When we treat logs like a transient utility instead of a long-term resource, we end up throwing away insight we can't get back. Losing that data isn't just a technical issue; it limits your ability to make smarter business decisions ...

In MEAN TIME TO INSIGHT Episode 17, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses cloud network observability ... 

In Part 12, the final installment in the series, the experts present some final predictions about AI's future impact on APM and Observability ...

What's in the future for APM and Observability? The experts have some ideas, and some of them even contradict each other. In the final installments of this series, the experts present their visions of the future for APM, Observability and beyond ...

AI plays a transformative role in both APM and observability by turning raw data into actionable insights, enabling faster, more accurate detection and resolution of issues ...

The story of the evolution of Observability to encompass APM and other IT performance management capabilities would not be complete without discussing the monumental impact of open source ...

So after all this discussion, what do the experts say about whether you need APM, observability or both? In today's complex digital landscape, organizations need both APM and Observability to not only react to issues but to anticipate and mitigate them proactively, ensuring robust performance and resilience ...

APM and Observability are often utilized by different teams within an organization, though there is considerable overlap ... In Part 7, the experts examine the different roles in IT and how they use either APM and Observability, or both ...

The experts say that APM and Observability serve fundamentally different use cases. Some of this was covered in earlier parts of this series, but the experts delve deeper into the differences of use cases here ...

APM remains a cornerstone in the toolkit for application performance management, crucial for pinpointing and resolving application-specific issues. Observability, however, is the evolution of this concept, expanding the scope to encompass distributed systems and cloud environments ...

Observability truly offers a wealth of capabilities that reach far beyond what we traditionally expect from APM. While APM excels at meticulously tracking application metrics and promptly alerting us when things go awry, observability empowers our teams to delve much deeper ...

While both aim to enhance system performance and reliability, observability offers a broader, more holistic approach and is designed for today's complex, distributed systems, as opposed to traditional, application-specific monitoring with APM ...

One of the key questions this APMdigest series seeks to answer: Is APM still relevant, or is it being replaced by Observability tools? APM remains a vital tool in the shed; it hasn't been replaced by observability ...

Application Performance Management (APM) and Observability are two of the most important tools in the ITOps and development toolboxes. Yet there seems to be confusion about them. What is the difference between APM and Observability? Does each offer different capabilities or serve different use cases? Do you need both, or is one enough? These are the questions this epic 12-part APMdigest series will attempt to answer over the next few weeks ...

The race toward AI maturity is on, but most enterprises are running uphill. According to new research from S&P Global Market Intelligence and Vultr, more than half of organizations expect to reach the "Transformational" stage of AI maturity by 2027 — a phase defined by widespread, embedded AI use across business operations. Yet as AI embeds deeper into real-time systems and mission-critical workflows, the gap between ambition and operational readiness is becoming harder to ignore ...

Adequately preventing and responding to disruptions has never been more important — or more possible. The growing ubiquity of AI has introduced more automated workstreams and increased productivity, while simultaneously creating a greater need for better data management. As customer expectations increasingly align with always-on services, the ability to prevent and recover from disruptions has direct ties to a business's bottom line ...