Tie infrastructure signals to logs, APM, and security with AI. Ask why infra is slow and get AI-backed root cause analysis. Predict failures and cut infrastructure waste—all in one unified view.
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You're juggling multiple dashboards for cloud, on-premises, and containers. By the time alerts fire, users are already impacted. Scaling costs spiral with every new service, and you're stuck manually fixing the same issues over and over.
See your infrastructure topology at a glance
Logify360 provides a unified view of your infrastructure topology with health rings, anomaly pulses, and clear node → pod → service relationships. Understand your cluster's state instantly.
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Visualize your entire infrastructure—nodes, pods, services, and their relationships—in an interactive 3D-like view. See how components connect and depend on each other.
Color-coded health indicators show the status of each node, pod, and service at a glance. Green for healthy, yellow for warning, red for critical.
AI detects anomalies and pulses them visually, drawing your attention to potential issues before they become incidents.
Understand the complete dependency chain. Click any component to see its relationships and trace issues across the stack.
Intelligent container orchestration observability
Go beyond basic Kubernetes metrics. Logify360 provides deep insights into your cluster's health, performance, and resource utilization with AI-powered predictions and recommendations.
Monitor CPU, memory, disk, and network pressure across all nodes. Get alerts when nodes approach resource limits before they impact workloads.
Example: Detect memory pressure 15 minutes before OOM kills occur
AI analyzes memory usage patterns and predicts OOM (Out of Memory) kills before they happen. Get recommendations to scale or optimize workloads.
Example: Predict OOM kills 1 hour in advance with 95% accuracy
Track pod creation, scheduling, running, and termination states. Identify patterns in pod restarts, crashes, and resource constraints.
Example: Identify pods restarting due to memory limits vs. crashes
AI analyzes traffic patterns, resource utilization, and performance metrics to recommend optimal HPA (Horizontal Pod Autoscaler) configurations.
Example: Recommend scaling payment-service from 3 to 5 replicas based on traffic spike
Monitor service-to-service latency across your cluster. Identify slow services, network bottlenecks, and performance degradation.
Example: Detect 200ms latency increase in checkout-service → payment-service calls
Intelligent automation and prediction
Logify360's AI engine understands your infrastructure patterns, predicts failures, and provides actionable recommendations to prevent incidents and optimize costs.
When a node fails or shows anomalies, AI automatically correlates logs, metrics, and traces to identify root causes. Get detailed analysis in seconds.
Example: Node-3 CPU spike → AI identifies memory leak in payment-worker pod → suggests restart or scale
Machine learning models continuously monitor your infrastructure metrics and detect unusual patterns that indicate potential issues.
Example: Detect unusual CPU spike pattern on weekends → identifies crypto-mining attack
Ask questions in plain English about your infrastructure. 'Which nodes are under memory pressure?' or 'Find the slowest services in my cluster.'
Example: Query: 'Why did node-3 CPU spike?' → Response: 'Memory leak in payment-worker pod, recommend restart'
AI analyzes historical patterns and predicts when you'll need to scale. Get recommendations for proactive scaling before traffic spikes.
Example: Predict Black Friday traffic spike 2 days in advance → recommend scaling 3x
Automatically identify underutilized resources, recommend right-sizing, and prevent cost overruns. Set budgets and get alerts when spending approaches limits.
Example: Identify 15 idle nodes → recommend downsizing → save $2,400/month
From anomaly to resolution in minutes
See how AI automatically correlates infrastructure anomalies with logs, APM metrics, and traces to provide root cause analysis and recommendations.
CPU spike on node-3 (85% → 95%)
Memory leak errors in payment-worker pod
Checkout-service P95 increased 245ms → 1.2s
Root cause: Memory leak in payment-worker v92. Confidence: 95%. Recommendation: Restart pod or scale horizontally.
Pod restarted. CPU normalized. Latency restored.
Keep your infra healthy and cost-efficient
AI recommends what to scale, not just what's broken. Automatically identify overprovisioned resources, optimize capacity, and reduce infrastructure waste by 20-35%.
AI analyzes resource utilization and suggests optimal node sizes and pod limits.
Automatically identify underutilized resources and recommend downsizing or consolidation.
Scale ahead of demand based on traffic patterns, not after incidents occur.
From alert to fix in minutes, not hours
Traffic spikes 10x during Black Friday sale. Need to predict capacity needs and scale proactively before checkout service degrades.
AI analyzes traffic patterns and predicts capacity exhaustion 2 hours in advance. Smart Search: 'Which pods will OOM in the next 2 hours?' AI identifies payment-worker pods at 85% memory. Recommends horizontal scaling from 5 to 12 replicas.
Zero service degradation during peak traffic. Checkout maintained 99.9% uptime.
Staging cluster experiencing OOM kill storms. Pods restarting every 30 minutes. Need to identify root cause and prevent production impact.
AI monitors memory usage patterns and detects memory leak in worker-service v92. Correlates with recent deployment. Smart Search: 'Why are pods OOM killing in staging?' AI identifies memory leak pattern, suggests rollback or memory limit increase.
OOM storms eliminated. Production deployment prevented similar issue.
After deploying checkout-service v92, node-3 CPU spikes to 95%. Need to quickly identify if it's the new deployment causing the issue and decide whether to rollback.
Smart Search: 'Why did node-3 CPU spike? Compare last deploy.' AI correlates deployment timeline with CPU metrics. Identifies memory leak in payment-worker pod (deployed with v92). Shows related traces and logs. Recommends rollback with 95% confidence.
Root cause identified in 2 minutes. Rollback decision made in 5 minutes. Service restored.
Infrastructure costs growing 30% YoY. Need to optimize capacity across AWS, GCP, and Azure regions without impacting performance.
Cost Guardrails analyze resource utilization across all regions. AI identifies 15 idle nodes in us-east-1, 8 overprovisioned nodes in eu-west-1. Recommends downsizing and right-sizing. Smart Search: 'Find overprovisioned nodes across all regions.'
Reduced infrastructure costs by 32% ($14,400/month savings) while maintaining 99.95% uptime.
Real results from real teams
Reduction in infrastructure-related incidents with predictive alerting
AI predicts failures 1 hour before they impact users
Average reduction in infrastructure costs through optimization
Average time to resolve infrastructure incidents
Average infrastructure availability
Fewer false positives with AI-powered alerting
Built for scale, reliability, and cost efficiency
See how Logify360 can help you predict failures, optimize costs, and resolve incidents faster with AI-powered infrastructure observability.