Operations | Monitoring | ITSM | DevOps | Cloud

Today, organizations looking to build AI products and services using large language models (LLMs), agentic AI, and generative AI often start by investing in artificial intelligence as a service (AIaaS), also known as cloud AI. AIaaS provides a scalable, flexible, and cost-effective way for businesses of all sizes to access advanced AI technologies without the need for extensive in-house expertise or infrastructure.

The RED method is a streamlined approach for monitoring microservices and other request-driven applications, focusing on three critical metrics: Rate, Errors, and Duration. Originating from the principles established by Google's "Four Golden Signals," the RED monitoring framework offers a pragmatic and user-centric perspective on service assurance and service performance.

Reliable internet connectivity isn’t a convenience. It’s mission-critical infrastructure for modern organizations. Every organization today depends on high-speed, reliable internet access for daily operations—from cloud collaboration and data transfer to streaming, remote work, and customer engagement. As digital transformation accelerates, the rise of AI, large language models (LLMs), IoT, and device sprawl has massively increased bandwidth demand and network complexity.

AI is being adopted at a faster rate than ever across the business world. According to Stanford, 78% of organizations had implemented AI in some form by 2024. And if that’s not convincing enough, 92% of companies plan to expand their AI investment over the next three years. Practically everyone, including your competitors, is already using AI to gain a competitive edge. If you don’t act soon, there's a real risk of falling behind.

Large Language Models (LLMs) are transforming how businesses interact with users, automate workflows, and deliver insights in real time. But as powerful as these models are, running them at scale comes with unique challenges, from hallucinations and latency spikes to cost overruns and user trust issues.

As organizations modernize, most applications don’t fit neatly into one category—they span both traditional three-tier architectures and cloud-native microservices. To monitor these hybrid environments effectively, teams need APM tools that can seamlessly connect the two worlds.

In 2025, microservices are everywhere and Kubernetes is the de facto standard for operating cloud native applications. But not all apps are built in microservices architectures. For most enterprises, hybrid environments are the reality, with their business run on a mix of three-tier and cloud native applications.

Every day, IT teams are flooded with alerts—thousands of messages about performance issues, service outages, or suspicious activity. With so many notifications, it’s easy to get overwhelmed, miss critical problems, or waste time chasing false alarms. Correlating related alerts into groups can help reduce the noise and make sense of everything, but setting up those correlations takes time, experience, and a lot of both system and historic knowledge.