See how every layer of your AI service stack performs, from GPUs, LLMs, and infrastructure to internet performance and digital experience, so teams can resolve issues faster, control costs, and deliver more reliable AI services.
Give teams one connected view of AI performance, infrastructure health, LLM behavior, and digital experience so they can move faster, troubleshoot smarter, and focus on what matters.
Improve GPU performance, reduce idle capacity, and avoid wasted spend by understanding when resources are healthy, saturated, overheating, underused, or overextended.
Catch runaway token usage, provider issues, latency spikes, and inefficient compute early so teams can protect budgets without slowing AI innovation.
Detect performance degradation before users feel it, reduce manual troubleshooting, and keep critical AI experiences running smoothly across infrastructure, applications, and digital touchpoints.
Quickly determine whether issues come from your infrastructure, the internet, cloud services, or third-party providers so teams can resolve disruptions faster and avoid wasted effort.
Prioritize incidents by user impact, service importance, region, and business risk so teams can cut through noise and act on what actually affects the business.
What you can do with AI Monitoring
LogicMonitor gives teams unified visibility across the AI service stack from infrastructure, GPUs, LLMs, APIs, vector databases, and cloud platforms to the Internet stack and digital experience, so they can understand how AI services are performing.
Bring GPU metrics, LLM performance, vector database stats, infrastructure health, internet performance, and digital experience signals into one view.
Display GPU, LLM, vector database, infrastructure, internet performance, and digital experience data side by side. Give teams a shared view of AI service health, cost, performance, and user impact.
Catch unusual behavior across AI workloads, GPUs, LLM APIs, infrastructure, internet dependencies, and user journeys.
Map inference pipelines, service relationships, cloud and on-prem topology, and internet delivery paths to pinpoint latency across the full AI transaction.
Break down token usage, GPU utilization, cloud spend, and external delivery performance to identify waste and protect AI investments.
Monitor AI-specific logs, API usage, infrastructure behavior, access patterns, and internet-facing dependencies to detect unusual activity and support audit readiness.
INTEGRATIONS
LM Envision integrates with 3,000+ technologies, from infrastructure and ITSM tools to AI platforms and model frameworks. Ingest metrics from GPUs, LLMs, vector databases, and cloud AI services while syncing enriched incident context with tools like ServiceNow, Jira, and Zendesk automatically.
100%
collector-based and API-friendly
3,000+
integrations and counting
AI agent for ITOps
Edwin AI applies agentic AIOps to streamline ITOps by cutting noise, automating triage, and driving resolution across even the most complex environments. No manual stitching. No swivel-chairing.
67%
ITSM incident reduction
88%
noise reduction
By the numbers
Get anwers
Get the answers to the top AI monitoring questions.
AI observability gives teams end-to-end visibility into the full AI service stack, from infrastructure, GPUs, APIs, and models to vector databases, data pipelines, and digital experience. It helps teams connect performance, reliability, cost, and user experience signals, so they can detect issues faster, optimize resources, and keep AI services running reliably at scale.
AI workload monitoring gives teams visibility into the full stack behind production AI applications, from infrastructure, GPUs, and containers to APIs, models, vector databases, and data pipelines. It helps ITOps teams connect performance, reliability, and cost signals across these systems, so they can detect issues faster, optimize compute, control costs, and keep AI services running reliably at scale.
AI workload monitoring focuses on the infrastructure, GPUs, containers, APIs, models, vector databases, and pipelines that power AI applications. AI observability is broader. It connects those workload signals with reliability, cost, model and provider behavior, and digital experience data, helping teams understand how issues across the AI service stack affect application performance and user experience.
AI services depend on many moving parts, including infrastructure, GPUs, APIs, models, vector databases, cloud platforms, internet paths, and user-facing applications. When those signals live in separate tools, teams lose time piecing together what happened. Unified AI observability brings those signals into one view, helping teams troubleshoot faster, reduce blind spots, control costs, and understand how backend performance affects the user experience.
LogicMonitor helps teams monitor production AI systems across infrastructure, GPUs, containers, Kubernetes, cloud AI services, APIs, LLM providers, vector databases, data pipelines, and internet-facing user experiences. This gives teams a connected view of the systems that power AI applications, from backend compute to end-user experience.
LLM monitoring gives teams visibility into the performance, reliability, and cost of large language model services. By tracking availability, latency, token usage, error rates, provider behavior, and response quality, teams can understand how LLMs affect AI application performance and deliver more reliable AI experiences.
LogicMonitor monitors GPU utilization, memory, temperature, power draw, and related infrastructure metrics across on-premises and cloud environments, helping teams spot saturation, idle resources, and bottlenecks.
Yes. LogicMonitor helps teams trace AI service performance across the full request path, from user interaction to API gateway, LLM framework, vector database, GPU execution, external provider, and response. This helps teams pinpoint where latency, errors, or reliability issues appear across complex AI service chains.
Internet Performance Monitoring improves AI workload observability by extending visibility beyond internal infrastructure and application telemetry. It help teams understand whether AI service issues are caused by internet paths, DNS, CDN, cloud platforms, third-party APIs, provider performance, or real user experience, so teams can isolate root cause faster and prioritize the issues that actually affect service delivery.
Yes. LogicMonitor monitors AI infrastructure, workloads, and API telemetry, along with third-party APIs, cloud services, DNS, CDN, and internet routes affecting AI performance. The platform helps teams understand whether performance issues come from internal systems, external providers, or the delivery paths between them.
AI applications rely on APIs, cloud platforms, CDNs, networks, and user workflows. Digital experience monitoring shows how users experience AI services in the real world, helping teams identify degradation earlier, validate service performance, and prioritize incidents by user impact.
AI monitoring helps teams identify where spend is being wasted across GPUs, token usage, cloud resources, vector databases, and external delivery paths. By tracking idle resources, usage spikes, inefficient compute, and provider performance, teams can optimize AI services without slowing innovation or compromising reliability.
AI observability helps teams connect alerts to service impact, user experience, and business risk. Instead of treating every infrastructure, GPU, API, or model issue the same way, teams can prioritize incidents based on what’s actually affecting users, regions, services, and critical AI workflows.
LogicMonitor helps teams monitor AI-specific logs, API usage, infrastructure behavior, access patterns, and internet-facing dependencies. By bringing security events, audit logs, and performance signals into one operational view, teams can detect unusual activity, investigate risk, and support compliance reporting.
AI observability is useful for ITOps, CloudOps, DevOps, platform engineering, SRE, and infrastructure teams responsible for keeping AI services reliable, performant, and cost-efficient. It also helps IT leaders understand how AI investments are performing across infrastructure, applications, providers, and user experience.
Own your AI performance
with LM Envision
