LLM Observability in One Platform
Keep your large language models fast, cost-efficient, and reliable with complete visibility into what’s driving their behavior.
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LLM Observability Platform
Unified Visibility with Deep Context into Every LLM Request and Response
LogicMonitor Envision gives you real-time visibility into LLM performance, token usage, and failure points, so you can resolve issues faster, optimize performance, and deliver more reliable AI experiences.
Resolve LLM issues before they disrupt users
Avoid latency spikes, broken responses, or bot outages by spotting anomalies early and acting fast before customers notice anything’s wrong.
Improve the reliability of every AI interaction
Whether you’re powering customer service chatbots or internal tools, you’ll deliver faster, smarter responses by keeping your LLMs healthy and performant.
Control LLM costs without constant monitoring
Stay on budget as usage grows. With visibility into token spend and API inefficiencies, you can cut waste without sacrificing quality or scale.
Move faster without sacrificing control
As your AI footprint expands, you won’t need new tools or teams to manage it. LM Envision grows with you, automatically monitoring new endpoints and services as they come online.
Get ahead of AI risk and governance
Avoid unintentional exposure, data misuse, or shadow AI. With visibility into access patterns and workload behavior, you can enforce responsible use and stay compliant.
Keep execs aligned with clean, credible data
Transform usage trends, spend reports, and model performance into clear, real-time dashboards that drive informed decisions across teams.
MAKE YOUR LLM OBSERVABILITY 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
Comprehensive LLM Monitoring
Monitor every layer of your LLM stack, from API calls to the infrastructure behind them.
- LLM API Telemetry
Collect token usage, latency, error rates, and cost-per-request from OpenAI, Azure OpenAI, AWS Bedrock, and Vertex AI. - Inference Infrastructure Metrics
Track GPU utilization, memory pressure, temperature, and power draw using NVIDIA DCGM across both cloud and on-prem environments. - LLM Framework & Middleware Visibility
Surface key metrics like API call rate, memory use, and workflow execution time from LangChain, Traceloop, and LangSmith. - Vector Database Monitoring
Monitor query volume, read/write latency, and index size from Pinecone and ChromaDB to optimize context retrieval.
Dashboards & Visualizations for LLMs
Bring every LLM-related signal into focus.
- Unified Dashboards
Visualize LLM performance, token trends, and supporting infrastructure in one scrollable view. - Out-of-the-Box Templates
Start fast with prebuilt dashboards for popular models and services—no manual setup required. - Custom Visualizations
Build your own views with flexible widgets, tailored for platform teams, MLOps, or executive stakeholders.
Alerts & Anomaly Detection
Detect and prioritize LLM issues before they impact users.
- Anomaly Detection on LLM Metrics
Use anomaly detection to baseline normal usage patterns and catch token, latency, or cost anomalies early. - Threshold-Based Alerts
Configure thresholds for key metrics like API failure rate, token spikes, or high response time. - Noise Suppression for LLM Pipelines
Automatically suppress repetitive or low-confidence alerts to reduce alert fatigue and focus attention.
Request Correlation & Service Tracing
Understand how every LLM request flows across your system.
- End-to-End Inference Tracing
Trace each request from API through LangChain agents, vector DBs, and down to GPU execution. - Service Chain Insights
Correlate metrics across SageMaker, Kubernetes, AWS Q Business, LangChain, and other connected services. - Topology Mapping
Auto-discover and map relationships across hybrid cloud environments. Vvisualize where requests flow and where issues start.
Cost Optimization & Capacity
Keep token costs predictable and usage efficient.
- Token Spend Breakdown
View spend by model, endpoint, team, or application to pinpoint cost drivers. - Idle Resource Detection
Identify underused endpoints, GPU resources, or stale vector DB shards for consolidation. - Forecasting & Budget Alerts
Project next month’s usage based on historical metrics and get alerted before budgets are breached.
Security & Compliance Logging
Support responsible AI with visibility and auditability.
- API & Access Anomaly Detection
Flag unusual usage patterns, unauthorized API calls, or access spikes across your LLM stack. - Audit-Ready Logging
Store and export snapshots of LLM metrics and logs to support compliance efforts like SOC 2 or HIPAA.
Open & Agnostic
ITSM Integration
AI Connectors
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.
Frequently Asked Questions
- What is LLM Observability, and why does it matter?
LLM observability gives you visibility into how large language models behave in production, across API calls, token usage, latency, vector database queries, and the supporting infrastructure. It helps teams detect issues early, reduce time to resolution, and manage usage and cost with precision.
- How is this different from standard AI or ML observability?
Traditional AI observability focuses on models, pipelines, and infrastructure. LLM observability goes deeper into prompt-to-response behavior, token consumption, and drift. It’s built for teams running generative AI apps in production, not just training ML models.
- Can I monitor OpenAI, Claude, Bedrock, or other LLM APIs?
Yes. LM Envision integrates with OpenAI, Azure OpenAI, AWS Bedrock, and Vertex AI to monitor metrics like token usage, request latency, error rates, and API cost. Claude is on the roadmap for future development.
- How does Edwin AI help with LLM observability?
Edwin AI applies intelligent correlation across your LLM stack—connecting token anomalies, API performance, and infrastructure signals. It helps surface likely root causes and provides next steps, cutting down on manual triage.
- What kind of data can I observe?
You can monitor:
- Prompt activity and latency
- Token and request volume
- Model errors and version changes
- Vector database health and retrieval metrics
- Related infrastructure and API performance
- Can I track usage and costs across teams?
Yes. You can segment LLM API usage by team, app, or environment to stay ahead of billing spikes and optimize usage patterns.
- How does this integrate with the rest of my observability stack?
LLM observability is built into the same LM Envision platform you use for infrastructure, cloud, apps, and networks—no need to spin up a new tool or context-switch.