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March 08.2025
3 Minutes Read

Why APM Alone Cannot Meet the Demands of Modern DevOps: The Rise of IPM

Hands typing on laptop illustrating Internet Performance Monitoring.

The Paradigm Shift in Application Monitoring

In an era where digital transformation is accelerating rapidly, the limitations of Application Performance Monitoring (APM) tools have become increasingly evident. Originally designed to manage traditional application architecture, APM systems struggle to keep pace with today’s dynamic environments where cloud computing and distributed services reign supreme. This scenario necessitates new approaches to monitoring that ensure comprehensive visibility across the multitude of services utilized—enter Internet Performance Monitoring (IPM).

Why APM is Insufficient in Today’s Internet Infrastructure

We live in a world where cloud technologies aren't just supplemental—they are core to operations. With applications hosted in multiple data centers and dependencies spread across various services, organizations find themselves facing complexity like never before. APM tools, once suitable for monolithic applications operating on local area networks, now hinder more than help. They focus primarily on application code, leaving organizations blind to external internet factors that impact user experiences.

Understanding Internet Performance Monitoring (IPM)

IPM emerges as a comprehensive solution designed to fill this gap. Unlike APM—which targets code-level performance—IPM emphasizes overall network performance as experienced by users. By leveraging strategies such as real user monitoring (RUM) and synthetic testing from multiple vantage points around the globe, IPM solutions, like those offered by Catchpoint, allow organizations to analyze how internet infrastructures and services influence application performance.

As highlighted in various industry reports, only IPM can bridge the gaps left by APM. It empowers organizations to understand latency variations, unearth root causes of user experience issues, and provide real insights into application performance from the user's perspective. This level of insight is crucial today as businesses depend on a wide variety of internet-facing services, APIs, and third-party solutions to achieve their operational goals.

A New Monitoring Paradigm: Blending APM and IPM

It's important to note that APM is not entirely obsolete; rather, it works exceptionally well in concert with IPM. This combination can yield the robust monitoring strategies organizations need to thrive in today’s intricate digital landscape. For instance, while APM offers insights into application efficiencies, IPM ensures that those efficiencies translate into a satisfactory user experience by measuring real-world internet interactions.

Consider a company like SAP, which utilizes Catchpoint for its Commerce Cloud and APM for its application stack. They ensure user experiences are optimized not just by the performance of their application but by the entirety of their internet stack.

The Future of Monitoring: Skills for Tomorrow’s Network Engineers

As organizations evolve from traditional infrastructures towards Internet-centric approaches, so too must the skill sets of their network engineers. No longer can they rely solely on routers and switches; proficiency with IPM tools is essential for today’s engineering professionals. Companies like Catchpoint have identified this need and are fostering training initiatives to equip engineers with the necessary skills for this transition.

Take Action: Embrace IPM for a Resilient Future

In conclusion, the digital landscape demands that organizations adopt innovative monitoring strategies to ensure optimal user experiences. By embracing both APM and IPM, entities not only enhance operational resilience but also align their IT efforts with user expectations. The call to action is clear: organizations must explore integrated monitoring solutions, such as IPM, in order to stay ahead of performance challenges, improve digital experiences, and ultimately drive business success.

Agile-DevOps Synergy

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01.13.2026

How AI Will Transform DevOps in 2026: Embrace the Change!

Update AI's Impact on DevOps: A Necessary Evolution As organizations prepare for 2026, one undeniable trend emerges: AI is set to profoundly transform DevOps, extending its influence into software development, security, and operations at an unprecedented rate. The core principle of DevOps, which emphasizes speed, automation, and collaboration, is now being redefined by artificial intelligence. With AI agents actively participating in coding, testing, and incident management, the challenge for DevOps teams becomes clear: how do they maintain control and reliability in a landscape where machines increasingly perform critical tasks? The Rise of AIOps in DevOps In navigating these uncharted waters, the concept of AIOps, or AI for IT Operations, stands out as a game-changing development. AIOps platforms use machine learning to analyze vast streams of operational data. They provide predictive capabilities that allow teams to avert issues before they escalate—a shift from the traditional reactive tools that dominated the field. According to research, a staggering 67% of DevOps teams have ramped up their investments in AI over the past year, and this trend is only expected to grow. Generative AI Tools: Revolutionizing the Development Pipeline Among the most exciting advancements are generative AI tools, such as GitHub Copilot and Datadog, which can automatically generate code suggestions based on natural language prompts. This technology not only speeds up the development process but enhances collaboration and efficiency, allowing teams to focus on strategic initiatives rather than repetitive tasks. Companies are already witnessing a notable decrease in coding errors, ultimately leading to faster deployment cycles, which is critical in today's fast-paced market. DevOps Security Reimagined With AI's incorporation into DevOps, security will undergo substantial changes as well. AI-driven security tools detect vulnerabilities dynamically, ensuring that potential threats are neutralized before they can compromise system integrity. This integration of AI and security (termed DevSecOps) is quickly becoming essential as the malware landscape grows more sophisticated. Many organizations struggle with the traditional security model; placing security at every level of the DevOps pipeline ensures that safety measures are not an afterthought but rather baked into the entire development process. The Cultural Shift Required Embracing AI in DevOps also necessitates a shift in team culture. Engineers will need to transition from hands-on management of every incident to adopting more of a strategic oversight role. This paradigm shift emphasizes trust in AI systems, which requires training and upskilling to ensure a smooth integration between human and machine efforts. The concern over the potential for AI systems to operate as ‘black boxes’—tools that produce results without explanation—calls for a disciplined approach in validation and oversight of AI outputs. Challenges and Concerns Ahead Despite the promising potential, companies must address several hurdles when implementing AI in their DevOps practices. Costs of integration can be high, and the necessity for skilled personnel presents a significant barrier. Furthermore, as AI plays a larger role in core operations, privacy and ethical concerns rise, necessitating compliance with stringent regulations. Ensuring that AI models operate free of bias is crucial as outcomes in DevOps teams increasingly rely on AI-generated insights. Looking to the Future The road to 2026 sees DevOps moving toward greater interaction with AI technologies, with the need for clarity in operational procedures more important than ever. The successful integration of AI will not merely involve adopting new tools; it will hinge on organizations’ willingness to evolve their practices, training protocols, and cultural approaches to problem-solving in the technology landscape. Conclusion: Readiness for AI-Driven DevOps As we approach this pivotal moment in tech, one thing is clear: DevOps will not be the same by 2026. The transformation driven by AI offers opportunities and challenges alike. Companies looking to leverage this technology must prepare proactively for the integration into their workflows to avoid falling behind in an increasingly AI-driven competitive landscape.

01.11.2026

Free and Affordable AI Certifications: Gain Skills Without Spending a Fortune

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01.11.2026

DeepSeek’s AI Model Set to Revolutionize Coding in February

Update The Future of AI in Coding: A Game Changer on the Horizon In an industry marked by rapid technological advancements, the impending launch of DeepSeek’s AI model with enhanced coding capabilities is creating significant anticipation. Set to debut in February, this innovative model promises to reshape coding practices, enhancing productivity and efficiency in software development processes. What Sets DeepSeek Apart? DeepSeek’s new AI model aims to allow developers to automate various aspects of coding, enabling more streamlined workflows. By integrating machine learning algorithms, the model harnesses historical data to predict and suggest programming solutions in real time. This aligns with current trends emphasizing DevOps practices, where agility and responsiveness are paramount. The Impact on Agile DevOps Practices One of the standout features of the upcoming AI tool is its potential to enhance Agile DevOps methodologies. As teams embrace iterative development, the model’s capacity to offer insightful coding suggestions can lead to shorter development cycles and quicker deployments. This is vital in today’s fast-paced digital landscape where businesses strive to remain competitive. Security Meets Coding: DevSecOps Integration Moreover, the integration of DevSecOps principles into the model ensures that security is not an afterthought. By automating security checks during the coding process, DeepSeek’s model addresses vulnerabilities from the start. This proactive approach equips teams to deliver secure code at a faster rate, significantly bolstering overall project outcomes. Broader Implications for Developers The release of this AI solution could dramatically redefine the role of developers. While automation might spark fears of job displacement, it can also empower developers to focus on more strategic tasks, such as architecture and innovative problem-solving. As mundane coding responsibilities are automated, developers could allocate more time to creative endeavors that drive technological advancement. Looking Ahead: Predictions for the AI-assisted Coding Era As we anticipate DeepSeek’s model rollout, it’s crucial to reflect on its broader implications. Integrated AI tools may become a standard in the software industry, promoting a culture of continuous learning and adaptation. As AI evolves, the expectation is not merely about efficiency but elevating the caliber of software produced, ultimately enhancing user experiences. Conclusion: Embracing Change and Innovation With innovations like DeepSeek’s AI model on the horizon, the landscape of coding is set to experience a remarkable transformation. Developers and businesses alike must prepare for this shift, embracing the technologies that can boost their productivity while fostering a stronger security posture in each line of code written.

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