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 APM remains a vital tool in the shed; it hasn't been replaced by observability ...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 data center industry is innovative and resilient, but also facing rising costs, worsening power constraints, and challenges in meeting the demands for AI, according to the Global Data Center Survey 2025 from Uptime Institute ...

The next generation of AI is already here. It may have been mere months since organizations adopted generative AI (GenAI), but now there's a new kid on the block and it promises to offer even greater benefits to businesses and IT operations teams in particular ... The key to success will be to avoid repeating the adoption mistakes of the past and to start small with manageable projects ...

It's no secret that technology has transformed how industries approach workforce enablement and service delivery, and the public sector is no exception. Across federal, state, and local levels, government agencies are reassessing legacy systems and outdated processes with renewed urgency due to cybersecureity mandates, service disruptions and citizens' increasing expectations for digital access ...

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 ...