How to Simplify AI Observability Across Hybrid and Cloud Environments

As companies adopt more artificial intelligence (AI) to stay competitive and simplify operations, they’re hitting a snag they’ve seen plenty of times before: complexity. Those user-friendly chatbots and impressive predictive models aren’t magic—they run on powerful GPUs like NVIDIA’s and rely on cloud services such as Azure OpenAI or Amazon SageMaker. Keeping these sophisticated systems running smoothly, especially when they’re spread across hybrid environments, is a challenge well known to many IT professionals.

And legacy solutions aren’t built for the task. Most IT teams end up using several monitoring solutions, each covering just one piece of their setup. But juggling multiple platforms creates blind spots, slows your team down, and makes solving problems proactively almost impossible. That’s why having a clear, unified observability system makes all the difference. It helps you see everything in one place, so your team spends less time fixing things and more time building what’s next.

Unified Monitoring of Hybrid and AI Environments

LogicMonitor Envision gives your team one clear view of all your AI workloads and critical systems, no matter where they’re running—on-prem, in the cloud, or on the edge. Instead of jumping between separate monitoring solutions, you get all your essential metrics and alerts in one place, making it easy to spot and fix issues quickly.

Good observability for AI starts by knowing exactly what to watch. Metrics like how many requests you’re handling, how quickly your systems respond, and how hard your GPUs are working are a great place to start. To really understand performance, you need data from different sources—logs, user activity, and system metrics—pulled together to create a clear picture. By regularly looking at this data, your team can spot issues early and make quick adjustments. Plus, keeping clear records and sharing regular updates ensures everyone stays informed about how your AI systems are doing.

Dive deeper into observability for AI workloads on LM Envision

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Discovering AI Resources Across Infrastructure

LM Envision makes it easy for your IT team to find and track your AI resources—whether they’re running in your own data center or private and public clouds, or using services like Azure OpenAI, Amazon Bedrock, and Amazon SageMaker. With our resource discovery feature, your team can quickly see AI assets right alongside traditional infrastructure, all in one place. To get started, just select “AI and LLMs,” and LM Envision will walk you through it.

Select “AI and LLMs” to start discovering AI-related resources. 

Optimizing AI Resource Usage and Cost Control

Once your resources are discovered, LM Envision gives you detailed insights into how everything is performing. For NVIDIA GPUs, you’ll see exactly how much they’re being utilized, memory use, thermal load, and power consumption. For cloud-based AI services, you’ll be able to track things like token usage and API error rates. All these metrics help your team make smarter decisions around budgeting, sustainability, and model performance. Plus, you can set up customized alerts and access controls, keeping your systems secure and efficient.

Select “Learn how” under the AI resources you want to discover and add to monitoring. A discovery widget will open and walk you through the process. 

And we’re always expanding: soon we’ll support even more services like OpenLit, TraceLoop, and GCP AI Services.

Proactive AI Issue Detection and Resolution

When it comes to keeping your AI systems running smoothly, LM Envision lets your team set dashboards and thresholds so you know immediately when there’s an issue. Whether it’s a GPU that’s overloaded, latency in cloud services, or a sudden infrastructure problem, your team gets real-time alerts, so you can jump in fast, fix things quickly, and keep everything performing at its best.

AI Observability with LogicMonitor Envision

Bottom line: AI success depends on clearly seeing what’s happening across your entire system. LM Envision gives your team the straightforward insights needed to manage your AI workloads proactively. With better visibility and easier management, your IT team can simplify operations, stay sustainable, and confidently scale your AI-driven initiatives.