The GPU Shortage Is Partly Self-Inflicted
Last quarter I helped a large enterprise size a GPU cluster for real-time LLM inference. We profiled the workload and found a glaring inefficiency: the H100s hit 92% utilization for about 200 milliseconds per request during prompt processing, then cratered to 30% for the next 3–9 seconds while generating output tokens. Those GPUs were expensive … continue reading
Distributed Systems as a Strategic Tool for Solving Complex Business Problems
Most people think of distributed systems as an engineering concern. Load balancing. Replication. Partition tolerance—latency management. But in reality, distributed systems are often the invisible backbone behind major business breakthroughs. When designed intentionally, they do more than scale traffic. They unlock new capabilities, reduce operational risk, and simplify business challenges that would otherwise be unmanageable. … continue reading
Credential Management: The Hidden Production Bottleneck for Agentic AI on Kubernetes
Agentic AI has moved from experimental curiosity to a production imperative. Organizations are deploying AI agents that don’t just answer questions but take actions: querying databases, updating records, orchestrating workflows, and provisioning infrastructure. These systems are no longer confined to innovation labs and are increasingly embedded in core business operations. The question is no longer … continue reading
Observability Without Code Changes: The Promise of eBPF‑Native Architectures
There’s a problem with modern observability that almost nobody talks about openly: your monitoring stack might be hurting the systems it’s supposed to protect. I don’t mean in a theoretical sense. I mean that the agents and SDKs most teams rely on for visibility impose real overhead on the applications they instrument. CPU, memory, throughput. … continue reading
Why Gateway API Is the Front Door for AI Workloads
The AI boom is reshaping application architectures. Large Language Model (LLM) inference has fundamentally altered the requirements of the Kubernetes networking stack. Kubernetes is now the default environment for scheduling GPU-accelerated workloads, but the last mile of delivery — connecting a user request to the optimal model instance — is increasingly a bottleneck. Traditional ingress … continue reading
Identity Debt: The New Source of Privilege Sprawl Overlooked by Security Teams
Let’s talk about debt. For years, enterprises have made decisions that help them move faster in the moment – taking shortcuts, postponing cleanup, or accepting imperfect visibility – knowing it will create technical debt they’ll eventually have to unwind. Many leaders accept this trade-off. While they know it will be a pain to deal with … continue reading
Zero-Trust Architecture for AI Pipelines: Why Your Security Model Needs to Evolve
Security teams have spent decades building defenses around network perimeters. AI pipelines make those perimeters meaningless. Data moves constantly between training environments, model registries, inference endpoints, and third-party services. A fraud detection system I worked on in a large healthcare setting illustrates why: the workflow relied on governed clinical and claims data, real-time event signals, … continue reading
The Next Evolution of Observability: Why Your Telemetry Needs to be AI-First
The DevOps and Platform Engineering landscape is undergoing a massive shift. As AI-driven automation accelerates, the volume of machine-generated telemetry data is growing exponentially. Consequently, traditional observability platforms are struggling to provide the context and speed necessary for AI-scale operations. Existing tools, built for humans reading logs, are failing to keep up with intelligent agents … continue reading
The Modern PC Refresh Mandate: Replace Strategically, Repurpose Selectively
For years, IT leaders treated the PC refresh cycle as a fixed rule. Every three to five years, endpoints were replaced wholesale and the cycle reset. That approach worked when component pricing was predictable and supply chains were stable. Today’s market looks very different, and it demands a more intentional strategy. Rising memory costs, inconsistent … continue reading
De-Risking AI Means New Infrastructure Security Patterns
Anthropic’s October research showing an AI model reproducing a real intrusion drew mixed reactions. Some questioned the framing and others questioned the intent, but most platform teams did not find the result surprising. Many are already expecting a significant security adjustment as AI workloads grow. AI systems are scaling faster than the security properties of … continue reading
AI Governance is the Next IT Battleground
AI has moved decisively from experimentation to execution. It now sits at the core of enterprise transformation strategies, reshaping how organizations think about performance, resilience, risk, and accountability. As AI becomes operational rather than exploratory, governance has emerged as the defining priority for IT and cloud infrastructure leaders tasked with running the digital backbone of … continue reading
IT Operations and Management predictions for 2026
Advancements in artificial intelligence in 2025 marked a seismic shift in how organizations can use the tools to automate IT processes, predict network interruptions and identify and remediate issues that could lead to poor performance, or worse, cybersecurity breaches. This article includes the thoughts of industry leaders as to what we might expect moving into … continue reading
Meet the Unsung Hero of Digital Success
When outages hit, everyone blames the app, the cloud, or the firewall because that’s what users can see. Often, the real culprit is the network, specifically core network services such as DNS. Every service is reliant on DNS and a well-defined IP address space. When core services aren’t resilient and centrally managed, organizations struggle to … continue reading
When “Good Enough” Isn’t: The Danger of Sampling in Observability
Modern observability is meant to give engineering teams a clear view into their systems. That’s not actually happening. Instead, many can only see fragments of what is happening inside their applications, yet they’re paying more than ever for that “privilege.” It starts to make sense when you understand how much data is now created and … continue reading
