It’s wild to think that just a decade ago, we were dipping our toes into containers and Kubernetes. Fast-forward to today, and cloud native architectures are the foundation for modern application development and infrastructure, now including AI workloads. While some organizations are still catching up, most enterprises have containerized 20 to 50% of their workloads. And of course, cloud-born companies are fully containerized from Day 1.The Realities of Hybrid Multicloud ComplexityContainerized microservices bring agility, composability and scale. But they also introduce challenges. Public cloud costs can spiral. Infrastructure outages can affect business continuity. That’s why so many organizations are now embracing hybrid, multicloud architectures, distributing workloads across multiple public clouds while maintaining private cloud deployments on premises.In theory, this gives us the best of both worlds: flexibility, resilience and cost control. In practice, it adds layers of complexity. Now we need to update, secure and observe distributed systems that span cloud and on-premises systems that are used by both operators and developers.The dream was always a “single pane of glass” to manage it all. Instead, we got tool sprawl, disjointed data and higher operational overhead.Why Standardizing Key Layers MattersIn conversations with organizations across the globe, I often find myself helping teams identify the layers in their architecture that are ripe for standardization. The goal? Improve security, reduce costs and simplify management without restricting developer freedom.The infrastructure should be an abstraction layer — available when, where and how developers need it. To get there, we need to make bold decisions about our architecture. That includes container orchestration, operating systems, automation and observability, among others.Because here’s the reality: We can’t deliver innovation at the pace the market demands while juggling dozens of OS versions or six different monitoring tools that don’t talk to each other.The Value of Standardization in ObservabilityLet’s hone in on observability, because it’s arguably one of the most strategic of those layers, and yet, one that too often flies under the radar.In modern, cloud native environments, microservices generate an avalanche of telemetry data: logs, metrics, traces, events — you name it. And with workloads scattered across clouds and data centers, visibility becomes fragmented fast.That complexity has led to skyrocketing observability costs, not to mention longer mean time to resolution (MTTR). Ironically, the tools designed to help us move fast are now slowing us down.Modern observability solutions are built to natively support Kubernetes across hybrid and multicloud deployments. The right solution doesn’t just collect data — it correlates it, simplifies it and helps you take action (without blowing up your budget).3 Ways Observability Unifies Hybrid Multicloud WorkloadsLet’s break this down into three practical benefits:1. Unified Visibility Across All EnvironmentsThe challengeIn hybrid or multicloud deployments, containerized workloads can live just about anywhere — on premises, in AWS, Google Cloud Platform, Azure or all of the above. Traditional monitoring tools create siloed dashboards that slow down incident response and frustrate engineers.The fixAdopt a Software as a Service (SaaS) observability platform built for cloud native environments. It should provide correlated, unified visibility across all telemetry types — logs, metrics, traces — regardless of where the data is generated. A true unified view means engineers and operators can detect and resolve issues faster. That single pane of glass? It’s not a fantasy — it’s finally achievable.2. Focus on the Most Useful DataThe challengeWith so much telemetry data, it’s hard to know what matters. Teams often store everything “just in case,” driving up storage costs and adding noise that slows down remediation.The fixA smart observability platform identifies high-utility data automatically — what’s queried, visualized, tagged or correlated. That means teams can retain only what they need, when they need it. Less clutter, better context and significantly lower costs. In some cases, we’ve seen organizations reduce telemetry volume (and cost) by up to 84% while improving MTTR by over 50%.3. Leveraging a Telemetry PipelineThe challengeTelemetry data is growing fast. Logs alone are up 250% in many organizations. Teams need a way to collect, process and route this data efficiently across diverse environments.The fixA telemetry pipeline centralizes data ingestion, processing and routing into a single interface. The ideal solution supports virtual machine (VM)- and container-based systems across on-premises and public cloud deployments. This gives teams full control over their observability data, while simplifying operations and reducing overhead.Final ThoughtsObservability is no longer just a “nice to have” across the hybrid cloud. It’s foundational for modern enterprises navigating the realities of hybrid and multicloud infrastructure. Standardizing your observability stack is one of the smartest moves you can make to:Accelerate innovation by unblocking developers.Increase uptime and improve user experience.Cut costs without sacrificing visibility.Simplify operations across fragmented environments.If you’re scaling hybrid multicloud and want to stay ahead, it’s time to treat observability like a first-class architectural layer.The post How Observability Unifies Workloads in a Hybrid Cloud World appeared first on The New Stack.