📊 CloudWatch-Style Monitoring for AI Infrastructure

CortexLogs Monitoring

Centralized logging and real-time monitoring system that aggregates data from all AICortex services with intelligent analytics, proactive alerts, and comprehensive observability.

Why AI Infrastructure Monitoring Matters

80% of AI projects fail due to infrastructure issues. Without proper monitoring, you're flying blind through complex distributed systems, GPU clusters, and model deployments. CortexLogs gives you the visibility you need.

The AI Monitoring Challenge

Traditional monitoring tools aren't built for the complexity of AI infrastructure

Fragmented Visibility

Logs scattered across Auth Modules, GPU instances, data pipelines, model training, and inference services. No unified view.

😵 Blind Spots Everywhere

Reactive Debugging

Finding out about failures after they happen. GPU crashes, training failures, and model deployment issues discovered too late.

⏰ Always Playing Catch-Up

Manual Investigation

Hours spent SSH-ing into servers, parsing logs manually, and correlating events across multiple systems to find root causes.

🔍 Detective Work Required

The Cost of Poor Monitoring

Real impact on AI teams and organizations

73%
of AI downtime is undetected for hours
$2.3M
average cost of AI system downtime per hour
6.5hrs
average time to identify and fix AI issues
40%
of engineer time spent on debugging
🔍 What is CortexLogs?

CloudWatch for AI Infrastructure

CortexLogs is a comprehensive monitoring and logging service designed specifically for AI infrastructure. It aggregates, analyzes, and alerts on data from all your AICortex services in real-time.

Centralized Aggregation: All logs from Auth, Instance Management, Data Streams, ZeroCore, Model Hub, and CortexFlow in one place
Real-Time Streaming: WebSocket connections provide instant updates as events happen across your infrastructure
Intelligent Filtering: Advanced log filters by service, severity level, time range, and custom queries
Proactive Alerting: Get notified before problems become critical issues affecting your AI workloads
Live Log Stream
Real-time
[2024-01-19 14:23:45.123] INFO [Auth Module] User authentication successful: user_12345
[2024-01-19 14:23:44.987] INFO [Model Hub] Model deployment completed: llama-7b-v2.1
[2024-01-19 14:23:44.654] WARN [Data Streams] S3 connection latency spike: 847ms
[2024-01-19 14:23:44.432] INFO [Instance Mgmt] GPU instance i-0a1b2c3d started: 4x A100
[2024-01-19 14:23:44.201] DEBUG [ZeroCore] Python sandbox initialized: session_789
[2024-01-19 14:23:43.987] INFO [CortexFlow] Training job queued: resnet-50-custom
[2024-01-19 14:23:43.765] INFO [CortexLogs] Log aggregation rate: 2,847/min
[2024-01-19 14:23:43.543] INFO [Auth Module] Session refresh for user_67890
[2024-01-19 14:23:45.456] INFO [Model Hub] Inference request processed: 247ms
Monitoring 7 services • 2,847 logs/min • 99.9% uptime

Why Choose CortexLogs?

Built specifically for AI infrastructure monitoring challenges

Reduce MTTR by 85%

From 6.5 hours to under 1 hour. Instant visibility into GPU failures, training issues, and model deployment problems across your entire AI stack.

Real Impact:
"CortexLogs helped us identify a memory leak in our model training pipeline that was costing us $50K/month in GPU costs." - AI Lead at TechCorp

Proactive Issue Detection

AI-powered anomaly detection identifies problems before they impact your models. Get alerted about GPU temperature spikes, unusual memory patterns, and performance degradation.

Smart Alerts:
🔥 GPU temp > 85°C • 🧠 Memory usage > 90% • ⚡ Inference latency +200% • 🔄 Training loss plateau detected

Unified Observability

Single pane of glass for all AICortex services. No more jumping between 7 different dashboards to understand what's happening in your AI infrastructure.

One Dashboard:
Auth • Instance Mgmt • Data Streams • ZeroCore • Model Hub • CortexFlow • System Health

Cost Optimization

Identify expensive GPU idle time, optimize training job scheduling, and reduce cloud costs through better resource utilization insights.

Savings Potential:
💰 30-50% reduction in GPU costs • ⏱️ Better job scheduling • 📊 Resource utilization tracking

How CortexLogs Works

Seamlessly integrates with your existing AICortex infrastructure

1

Automatic Log Collection

CortexLogs automatically collects logs from all AICortex services - Auth Module, Instance Management, Data Streams, ZeroCore, Model Hub, and CortexFlow. No manual configuration required.

2

Real-Time Processing

Logs are processed in real-time with intelligent filtering, anomaly detection, and correlation analysis. WebSocket connections provide instant updates to your monitoring dashboard.

3

Intelligent Alerts

Get proactive notifications via Slack, email, or webhooks when issues are detected. Smart correlation prevents alert fatigue while ensuring critical issues are never missed.

Monitors Every AICortex Service

Complete visibility across your entire AI infrastructure stack

Auth Module

  • • Login/logout events
  • • Failed authentication attempts
  • • RBAC permission changes
  • • Session management

Instance Management

  • • GPU instance lifecycle
  • • Resource utilization metrics
  • • Start/stop/restart events
  • • Scaling decisions

Data Streams

  • • S3/Drive connection status
  • • Kafka streaming health
  • • Data pipeline errors
  • • Vector DB performance

ZeroCore

  • • Notebook execution logs
  • • Sandbox security events
  • • Module installation status
  • • API endpoint performance

Model Hub

  • • Model deployment events
  • • Inference performance
  • • Version control changes
  • • API endpoint metrics

CortexFlow

  • • Training job progress
  • • ML pipeline execution
  • • Forward/backward prop logs
  • • Hyperparameter tuning

Start Monitoring Your AI Infrastructure Today

Join AI teams that have reduced their debugging time by 85% and infrastructure costs by 40% with CortexLogs comprehensive monitoring.

Trusted by AI teams at innovative companies

99.9% Uptime SLA
SOC 2 Compliant
Real-time Streaming
24/7 Support