AI Observability for Infrastructure, Workloads, and Pipelines in One Platform
Unify every signal across your AI environment so you move from reactive firefighting to strategic, data-driven decision making.
When your team has unified visibility into AI systems and infrastructure, they can act faster, prevent disruptions, and optimize costs, turning complexity into control and insight into impact.
See the big picture without the busywork
Stop chasing data across tools. With full-stack visibility across your AI and IT environments, your team can move faster, troubleshoot smarter, and stay focused on what matters.
Stay ahead of issues, not behind them
Proactively catch anomalies before they disrupt services. With early detection and root cause insights, you reduce downtime and eliminate the guesswork.
Keep AI costs under control automatically
Avoid budget surprises with built-in visibility into idle resources, underused GPUs, and runaway compute before they impact spend.
Scale AI without scaling complexity
As your stack evolves, LogicMonitor keeps up. New systems are discovered automatically with no manual overhead and no gaps in coverage.
Stay secure from edge to API
Monitor access, usage, and system behavior across your entire AI footprint. Spot irregular activity before it becomes a breach or a service disruption.
Show executives exactly how AI is performing
Turn complex metrics—like AI cost, uptime, and system performance—into clear, actionable dashboards that drive alignment, investment, and smarter decisions.
OBSERVABILITY POWERED BY LOGICMONITOR ENVISION AND EDWIN AI
Everything you need to monitor, manage, and optimize AI systems and workloads
With real-time visibility, automated discovery, and AI correlation, LM Envision helps you monitor every layer of your AI infrastructure, so you can prevent downtime, manage spend, and move fast.
Unify
Visualize
Detect
Trace
Track
Secure
Unify your AI telemetry in one platform
Bring GPU metrics, LLM performance, and vector database stats into a single view, so you can eliminate blind spots and monitor every layer of your AI stack.
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GPU & compute metrics Collect utilization, memory usage, temperature, and power-draw data for NVIDIA GPUs—both on-prem and in the cloud—with automatic discovery of new clusters.
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LLM & API telemetry Ingest token counts, API call latency, error rates, and cost-per-request from OpenAI, AWS Bedrock, Azure OpenAI, and GCP Vertex AI.
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Vector database visibility Gather query volume, read/write latency, and index-size metrics from Pinecone and ChromaDB clusters, directly, out of the box.
See every AI and infrastructure metric in one view
Display GPU, LLM, vector-DB, and infrastructure metrics side by side using prebuilt dashboards or build your own with drag-and-drop customization.
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Prebuilt templates Access ready-made AI-focused dashboards that ship with LM Envision.
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Custom dashboards Build and arrange widgets via drag-and-drop to tailor views for any team or role.
Reduce alert noise and surface what matters
Catch unusual behavior early with anomaly detection, set metric-based thresholds, and suppress low-priority alerts, so teams can focus on high-confidence incidents.Trace every AI request from API to GPU
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Anomaly detection engine Automatically flags unusual behavior across LLMs, GPUs, APIs, and pipelines, so you can catch issues early without manual thresholds.
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Threshold-based alerts Set custom thresholds for any metric and receive notifications when values exceed or drop below defined limits.
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Noise suppression Suppress redundant or low-priority alerts automatically, ensuring only high-confidence incidents trigger notifications.
Trace every AI request from API to GPU
Map out inference pipelines, trace service relationships, and visualize cloud/on-prem topology, so you can pinpoint latency and troubleshoot faster.
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End-to-end tracing Instrument inference pipelines (API call → LLM framework → GPU execution → return) to trace request paths and identify latency bottlenecks.
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Service chain insights Capture and correlate metrics from Amazon SageMaker, AWS Q Business, Kubernetes pods, LangChain agents, and other middleware components.
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Hybrid-cloud topology mapping Auto-discover and map relationships between on-prem hosts, cloud VMs, and container clusters—updating maps as new resources spin up.
Track spend, cut waste, and stay on budget
Break down token usage and GPU costs, detect idle resources, and set budget alerts using built-in forecasting tools purpose-built for AI workloads.
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Token cost breakdown Break down AI spend by model, application, or team using built-in cost dashboards.
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Idle resource detection Identify idle or under-utilized GPUs and vector-DB shards to highlight opportunities for consolidation.
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Forecasting & budget alerts Apply historical metrics to forecast next month’s token spend or GPU usage and configure budget-threshold alerts.
Secure your AI stack and simplify audits
Ingest AI-specific and infrastructure logs to flag anomalies, track access patterns, and export audit-ready logs for compliance with standards like HIPAA and SOC 2.
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Unified security events Ingest security logs and alerts (firewall, VPN, endpoint) alongside AI-service events—flagging unauthorized API calls, unusual container launches, and data-store access anomalies.
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Audit logging Store and export logs and metric snapshots for any point in time to support compliance (e.g., HIPAA, SOC 2) and audit reporting.
INTEGRATIONS
Connected to everything that powers AI
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
Let Edwin AI detect, explain, and help resolve issues automatically
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
"LogicMonitor is a valuable partner, constantly innovating and adapting to our business needs."
"LogicMonitor helps us succeed by being a true partner."
"Capital Group has 1,000+ alerts/ day. LogicMonitor will eliminate that noise."
"The sheer power of LogicMonitor’s monitoring capability is amazing."
By the numbers
AI observability that delivers real results
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fewer tickets
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fewer monitoring tools
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faster MTTR
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time savings
Get anwers
FAQs
Get the answers to the top AI monitoring questions.
What is AI observability?
AI observability is the ability to monitor and understand how AI systems behave in production. It helps teams detect model drift, spot latency, and catch silent failures by combining insights from infrastructure, models, and apps into one view.
How is AI observability different from traditional monitoring?
Traditional monitoring watches CPU, memory, and uptime. AI observability connects those signals with model behavior, like output changes, performance slowdowns, and unusual agent behaviors.
When should I implement AI observability?
Ideally before production. It’s much easier to track your AI systems from day one than to fix visibility gaps later.
Can LogicMonitor detect issues like drift or latency?
Yes. LogicMonitor watches for unusual patterns in system and model behavior, like slow responses, unexpected output spikes, or shifts in usage that often indicate deeper AI issues.
Do I need agents or custom instrumentation to get started?
No. LogicMonitor uses a collector-based model with built-in integrations. You can start monitoring your AI stack quickly, without complex setup.
Own your AI performance
with LM Envision
with LM Envision
