Application Performance Monitoring That Keeps Apps Fast, Reliable, And Ready For Users
See when application issues affect performance, reliability, or user experience, so your teams can fix problems faster and protect every digital journey.
When application health connects to user experience, teams fix faster, reduce guesswork, and protect every digital journey
Catch application slowdowns, failed journeys, and service issues early, so teams can protect customers, employees, revenue, and SLAs.
Understand whether issues are tied to the app, infrastructure, network, cloud, Internet, or third-party services before teams lose time guessing.
Measure performance across locations, devices, networks, and journeys, so teams can spot the issues that internal checks alone might miss.
Connect user impact to application behavior so teams can prioritize the pages, APIs, regions, and journeys that affect satisfaction, conversion, and productivity.
Give DevOps, ITOps, SRE, and platform teams a shared view of application health, dependencies, and impact, so they can reduce handoffs and resolve issues faster.
Monitor apps, APIs, cloud services, and release paths across the DevOps lifecycle to catch regressions before they affect users, revenue, or SLAs.
Everything you need to monitor, trace, and optimize application experience
LM Envision brings application, infrastructure, user experience, and Internet performance signals together so teams can spot issues earlier, understand impact faster, and keep critical digital experiences running.
Follow every transaction to the source
Track transactions across services, APIs, cloud apps, PaaS, and hybrid environments so teams can see where performance breaks down and what depends on what.
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Follow requests across services See real-time slowdowns across microservices, APIs, queues, and distributed systems before small issues become user-facing problems.
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Instrument with open standards Use OpenTelemetry-powered tracing to collect application data quickly and avoid locking teams into closed instrumentation models.
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Connect traces to business impact Link spans and services to affected users, journeys, and business-critical experiences so teams know what to fix first.
See what users actually experience
Capture real user behavior across web and mobile apps to understand where journeys stall, which users are affected, and what deserves attention.
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Compare expected and actual experience Pair synthetic checks with real user sessions to separate potential risk from active user impact.
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Replay sessions with context See clicks, scrolling, errors, and latency alongside application and third-party signals so teams can troubleshoot without guesswork.
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Prioritize affected journeys Focus on pages, APIs, regions, and user cohorts tied to revenue, SLAs, and customer experience.
Test critical journeys before they break
Run synthetic and API tests from global and private locations to validate availability, latency, and workflows before users are disrupted.
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Validate complete user paths Test URLs, APIs, transactions, and multi-step workflows across browsers, regions, and devices.
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Measure from the right location Use public vantage points plus private, on-premises, or enterprise nodes to test behind the firewall, at the edge, and near key users.
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Catch regressions earlier Add proactive checks to release, staging, and production workflows so performance issues surface before they reach users.
Know if the problem is yours or external
Correlate application, infrastructure, network, user experience, and Internet signals so teams can identify root cause faster and avoid finger-pointing.
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Separate owned and external failure domains See whether issues originate in code, cloud, network, SaaS, routing, or third-party providers.
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Map real-time dependency impact Understand how services, APIs, Internet paths, and user journeys connect before a small fault becomes a major incident.
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Move faster with AI-guided context Use Edwin AI to trigger workflows, route incidents, and summarize root cause in plain language.
Integrations
Works with the tools your teams already use
Connect OpenTelemetry, OpenMetrics, CI/CD, ITSM, collaboration, alerting, and business systems with application, Internet, and user-experience telemetry. LM Envision helps teams ingest, correlate, and act without stitching together separate APM, NPM, DEM, and IPM tools.
100%
collector-based and API-friendly
3,000+
integrations and counting
AI AGENT FOR ITOPS
Edwin AI finds root cause across apps, users, and the Internet
Edwin AI connects traces, metrics, logs, events, synthetics, RUM, and Internet telemetry to explain what happened, who is affected, and where to act. Reduce alert noise, distinguish internal issues from external dependencies, and move from triage to remediation faster.
67%
ITSM incident reduction
88%
noise reduction
What teams unlock with connected application visibility
GET ANSWERS
FAQs
Get the answers to the top application monitoring questions.
How does LogicMonitor monitor application performance?
LM Envision collects traces, metrics, logs, custom metrics, synthetics, RUM, and Internet telemetry, then correlates them with infrastructure and service context so teams can detect issues, understand impact, and resolve root cause faster.
How is outside-in monitoring different from traditional APM?
Traditional APM shows what happens inside your code and services. Outside-in monitoring adds the user and Internet perspective, including DNS, CDN, ISP, BGP/routing, SaaS, cloud, edge, and third-party dependencies that can break experience even when your app looks healthy.
Learn moreCan I monitor internal and external applications?
Yes. Use global vantage points for public applications and private, on-premises, or enterprise nodes for internal apps, APIs, and services behind the firewall. That gives teams consistent measurements across environments instead of competing tool signals.
How do synthetic monitoring and RUM work together?
Synthetic monitoring proactively tests availability, latency, APIs, and user journeys before users are affected. RUM captures what real users experience in production. Together, they show both early warning signals and live user impact.
How does Edwin AI help application teams?
Edwin AI analyzes correlated signals across apps, infrastructure, users, and Internet dependencies to reduce alert noise, summarize root cause, prioritize business impact, and trigger workflows that speed remediation.



