AI Observability in One Platform
Unify every signal across your AI environment so you move from reactive firefighting to strategic, data-driven decision making.
AI Observability Platform
Unified Visibility and True Control Over Your AI Systems
LogicMonitor Envision brings together metrics, events, logs, and traces from your AI systems, on-prem infrastructure, and cloud services into a single view. This AI observability helps you catch issues before they escalate, manage costs effectively, and keep your services reliable.
BY THE NUMBERS
Network monitoring that makes an impact
0
%
fewer tickets
0
%
fewer monitoring tools
0
%
faster MTTR
0
%
time savings
Get full-stack visibility into your entire AI and IT environment
From LLMs and GPUs to networks and databases—see it all in one unified view, so you can stop context-switching and start solving.
Catch problems and fix them before they impact your customers
When something goes wrong, you’ll know exactly where to look and how to fix it fast. LM Envision spots issues early, and Edwin AI helps you get to the root cause without the usual guesswork or swivel-chairing.
Control your AI budget without lifting a finger
Cut costs and stay on budget by automatically spotting idle resources and wasted compute before they drain your spend.
Grow your AI environment without more tools or headaches
New systems spin up, and LM Envision picks them up automatically, giving you instant visibility without extra licenses or manual setup.
Protect your AI stack from top to bottom
Stay on top of who’s accessing what, from where. If something’s off, you’ll catch it before it becomes an issue.
Generate executive-ready reports in minutes
Quickly turn complex metrics, like AI spend, uptime, and security posture, into clear, executive-ready dashboards that drive alignment and action.
Make your ITOps even smarter
Edwin AI: Agentic AIOps for Incident Management
Edwin AI helps you catch issues early, cut through noise, and resolve incidents fast. With built-in generative AI, it auto-correlates alerts, surfaces root cause, and gives step-by-step guidance to fix problems before customers ever notice.
Built for AI Workloads
Everything you need to monitor, manage, and optimize AI
Monitoring
Dashboards
Alerts
Observability
Cost Optimization
Security
Unify all your AI-related telemetry to eliminate blind spots.
-
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.
-
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.
-
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 a single pane of glass.
-
Single Pane of Glass Display LLM, GPU, and vector-DB metrics alongside existing server and network data in one scrollable view.
-
Prebuilt Templates Access ready-made AI-focused dashboards that ship with LM Envision.
-
Custom Dashboards Build and arrange widgets via drag-and-drop to tailor views for any team or role.
Edwin AI learns your environment’s baseline and only surfaces what’s important.
-
Anomaly Detection Engine Automatically flags unusual behavior across LLMs, GPUs, APIs, and pipelines, so you can catch issues early without manual thresholds.
-
Threshold-Based Alerts Set custom thresholds for any metric and receive notifications when values exceed or drop below defined limits.
-
Noise Suppression Suppress redundant or low-priority alerts automatically, ensuring only high-confidence incidents trigger notifications.
Trace every request to uncover root causes in seconds.
-
End-to-End Tracing Instrument inference pipelines (API call → LLM framework → GPU execution → return) to trace request paths and identify latency bottlenecks.
-
Service Chain Insights Capture and correlate metrics from Amazon SageMaker, AWS Q Business, Kubernetes pods, LangChain agents, and other middleware components.
-
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.
Stay ahead of costs with AI spend forecasting and budget recommendations.
-
Token Cost Breakdown Break down AI spend by model, application, or team using built-in cost dashboards.
-
Idle Resource Detection Identify idle or under-utilized GPUs and vector-DB shards to highlight opportunities for consolidation.
-
Forecasting & Budget Alerts Apply historical metrics to forecast next month’s token spend or GPU usage and configure budget-threshold alerts.
Combine service events and security logs to flag unauthorized activity and export audit-ready logs instantly.
-
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.
-
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.
We couldn’t see the whole picture. Since deploying LogicMonitor, we have one tool and one location where we can see across all our infrastructure. The time savings are huge. I can’t even calculate them, but I would say hundreds of hours.
Integrations
Open & Agnostic
Leverage LM Envision’s 3,000+ existing integrations (servers, networks, storage, APM, CMDB) to feed infrastructure and application telemetry alongside AI data. Explore integrations
ITSM Integration
Push enriched incident details—complete with GPU, LLM, and database context—to ServiceNow, Jira, and Zendesk; maintain two-way sync for status updates.
AI Connectors
Turn on plugins for OpenAI, AWS Bedrock, Azure OpenAI, GCP Vertex AI, Pinecone, ChromaDB, NVIDIA DCGM, and OpenLIT—each connector ingests the required metrics automatically.
See What AI Observability Can Do for Your Stack
See how LogicMonitor helps you monitor your AI systems in one place. Your team can move faster with fewer surprises.
GET ANSWERS
FAQs
Get the answers to the top network 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 an agentless model with built-in integrations. You can start monitoring your AI stack quickly, without complex setup.