Find bottlenecks, not just symptoms
Distributed tracing, golden signals, and AI-driven root cause analysis. Connect traces to logs and metrics in one click — no manual correlation needed.
The Limitations Every Team Faces
Traditional APM tools show you symptoms but leave you guessing about root causes. You see latency spikes but can't trace them to specific code paths, database queries, or service dependencies.
Latency spikes appear but you can't pinpoint which database query, API call, or service is the culprit. Manual investigation takes hours.
Hidden performance issues like N+1 queries go undetected until they cause production incidents. No automated detection or alerts.
New deployments cause performance degradation, but you can't quickly compare before/after metrics or trace the regression to specific changes.
Complex microservices architectures make it impossible to understand service dependencies and identify cascading failures.
Traces, metrics, and logs live in separate tools. Manual correlation across signals is time-consuming and error-prone.
Traditional APM requires manual analysis. No automated anomaly detection, root cause suggestions, or intelligent correlation.
AI-first architecture built for modern observability
Logify360 APM combines distributed tracing, metrics, and AI-powered analysis to give you complete visibility into application performance — with intelligent insights that save time.
Built on ClickHouse for sub-second query performance. Search billions of traces and metrics in milliseconds, not minutes.
Benefit
Get answers instantly, even at massive scale.
Example
Query 1B+ trace spans in under 200ms.
AI automatically correlates traces, metrics, and logs to tell the complete story. No manual investigation needed.
Benefit
Understand the full context of performance issues automatically.
Example
AI connects latency spike → slow database query → related log errors → root cause identified.
Smart sampling and retention policies automatically control APM data ingestion costs without losing critical performance insights.
Benefit
Reduce APM costs by 20-40% while maintaining full visibility.
Example
Automatically sample routine traces while keeping 100% of error traces and slow requests.
AI analyzes complex flamegraphs and trace spans to provide plain-English summaries. Understand performance bottlenecks instantly.
Benefit
No need to manually analyze flamegraphs. Get insights in seconds.
Example
AI identifies 'Database query in checkout-service taking 2.3s (95th percentile)' from flamegraph analysis.
The four signals that matter, intelligently connected
Latency, traffic, errors, and saturation — the four golden signals of observability. Logify360 APM tracks them all and uses AI to correlate anomalies across signals.
Track P50, P95, P99 latency across all services. AI identifies slow endpoints, database queries, and external API calls automatically.
AI Correlation
AI correlates latency spikes with error rates, traffic patterns, and trace anomalies to identify root causes.
Example
P95 latency spike in checkout-service → AI traces to slow payment-gateway API call → suggests timeout increase.
Monitor request rates, throughput, and traffic patterns across services. Identify traffic anomalies and capacity issues.
AI Correlation
AI connects traffic spikes to latency degradation, error rate increases, and resource saturation.
Example
Traffic spike at 2 PM → AI correlates with database connection pool exhaustion → suggests scaling recommendation.
Track error rates, exception types, and failure patterns. Get real-time alerts on error rate anomalies.
AI Correlation
AI links errors to specific traces, identifies error patterns, and suggests likely root causes.
Example
Error rate spike → AI analyzes traces → identifies memory leak in payment-service v92 → suggests rollback.
Monitor resource utilization — CPU, memory, database connections, queue depths. Identify capacity bottlenecks before they cause issues.
AI Correlation
AI predicts saturation based on traffic trends and correlates resource exhaustion with performance degradation.
Example
Database connection pool saturation → AI correlates with slow queries → suggests connection pool tuning.
Everything you need for modern application performance monitoring
End-to-end request tracing across microservices. See the complete request path from user to database and back.
Benefit:
Understand how requests flow through your system and identify bottlenecks in the request path.
AI analyzes complex flamegraphs to provide plain-English summaries of performance bottlenecks. No manual analysis needed.
Benefit:
Get instant insights from flamegraphs without spending hours analyzing stack traces.
Track latency, traffic, errors, and saturation across all services. AI correlates anomalies across signals automatically.
Benefit:
Get complete visibility into application health with intelligent correlation.
Automatically detect slow endpoints, N+1 queries, database contention, and external API latency issues.
Benefit:
Find performance issues before they impact users. Get proactive alerts on bottlenecks.
Track slow queries, connection pool usage, query patterns, and database performance metrics. Identify database bottlenecks.
Benefit:
Understand database performance impact on application latency and identify optimization opportunities.
Compare performance metrics before and after deployments. Automatically detect regressions and suggest rollbacks.
Benefit:
Catch performance regressions immediately after deployment. Make data-driven rollback decisions.
Visualize service dependencies, identify critical paths, and understand cascading failure scenarios.
Benefit:
Understand your architecture and identify single points of failure before they cause incidents.
AI automatically connects traces, metrics, and logs to tell the complete story of performance issues.
Benefit:
Get the full context of performance problems without manual correlation across tools.
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See how teams use Logify360 APM to solve performance issues
Challenge
After deploying checkout-service v92, P95 latency spikes from 245ms to 1.2s. Need to quickly identify the root cause and decide whether to rollback.
Solution
Use Smart Search: 'why did p95 spike for /orders? compare last deploy'. AI analyzes traces, identifies slow database query in payment-service, correlates with v92 deploy, and suggests rollback with 95% confidence.
Result
MTTR reduced from 2 hours to 15 minutes
Challenge
Application latency increasing over time. Suspect database issues but can't pinpoint which queries or services are causing the problem.
Solution
AI analyzes traces and metrics, identifies top 5 slowest database queries, shows query patterns, and suggests index optimizations. Cross-signal correlation reveals connection pool exhaustion in reporting-service.
Result
Identified root cause in 10 minutes vs. 3 hours previously
Challenge
Multiple services experiencing errors and latency spikes. Need to understand service dependencies and identify the root cause of cascading failures.
Solution
Service dependency map shows payment-gateway failure cascading to checkout-service and order-service. AI traces reveal payment-gateway timeout causing downstream failures. Smart Search identifies 'which service caused the slowdown?' → payment-gateway.
Result
Full incident understanding in 20 minutes vs. 4 hours
Challenge
User-service experiencing slow response times. Suspect N+1 query problem but can't prove it or identify the exact location.
Solution
AI analyzes traces and detects N+1 pattern: 1 query fetches users, then 50 queries fetch profiles. Flamegraph AI summary identifies the exact code path. Suggests eager loading optimization.
Result
Performance issue resolved in 1 hour vs. 1 day
Challenge
Checkout latency spiking intermittently. Suspect external payment API but need to correlate API latency with application metrics.
Solution
AI correlates traces showing slow payment-gateway API calls with metrics showing checkout-service latency spikes. Cross-signal storytelling reveals: 'Payment gateway API latency increased 500ms → checkout-service P95 increased 600ms'.
Result
Root cause identified in 5 minutes
Challenge
Need to understand resource utilization trends and predict capacity issues before they cause incidents.
Solution
AI analyzes saturation metrics (CPU, memory, database connections) and traffic trends. Predicts capacity exhaustion in 2 weeks based on growth patterns. Suggests scaling recommendations.
Result
Proactive capacity planning prevents incidents
See the difference AI-powered APM makes
Real results from real teams
Faster incident resolution with AI-powered root cause analysis
Sub-second trace analysis with ClickHouse architecture
Reduced APM ingestion costs with smart guardrails
AI root cause analysis accuracy
Average P95 response time across services
All critical performance data retained
Built for scale, performance, and reliability
See how Logify360 APM can help you reduce MTTR, find bottlenecks faster, and gain complete visibility into application performance with AI-powered insights.