AI System Monitoring & APM
AI in production needs different observability. Model drift, inference latency, pipeline failures, and cost per query, alongside traditional application performance.
AI Systems Fail Differently
Traditional APM catches server errors and slow pages. AI systems fail in ways that don't trigger alerts: model drift degrades answers over time, inference costs spike without warning, and pipeline failures produce wrong results instead of no results. You need monitoring designed for both.
We implement observability that covers the full stack: AI model performance, data pipeline health, inference latency, cost tracking, and traditional application metrics. When your AI system starts giving bad answers at 2 AM, you'll know before your users do.
Monitoring Services
AI Model Monitoring
Track model accuracy, drift, and degradation over time. Detect when your AI starts giving worse answers before users notice. Automated alerts for quality thresholds.
Pipeline Observability
Monitor data ingestion, embedding generation, vector store health, and inference pipelines end-to-end. Know exactly where a failure occurred and what data was affected.
Cost & Latency Tracking
AI inference costs can spike fast. We track cost per query, token usage, latency percentiles, and throughput so you can optimize spending without degrading quality.
Application Performance
Traditional APM alongside AI monitoring: server health, error tracking, database performance, and user experience metrics across your full application stack.
Key Capabilities
AI + Application Visibility
One dashboard for both AI system health and traditional application metrics. Trace a request from user input through AI inference to response delivery.
Drift Detection
Automated detection of model performance degradation. Compare current outputs against baselines and alert when quality drops below thresholds you define.
Custom AI Dashboards
Dashboards built for AI operations: model accuracy trends, token usage, cost per query, pipeline throughput, and error rates by model version.
Intelligent Alerting
Alerts that understand AI-specific failure modes. Not just 'server is down' but 'model accuracy dropped 15% in the last hour' or 'inference cost doubled since Tuesday.'
Our Monitoring Approach
Assessment
Evaluate your current monitoring capabilities and identify gaps. We analyze your architecture, performance requirements, and business objectives.
Implementation
Deploy comprehensive monitoring solutions tailored to your stack. We handle instrumentation, configuration, and integration with your existing tools.
Optimization
Analyze performance data to identify optimization opportunities. We help you tune your application and infrastructure for peak efficiency.
Continuous Improvement
Ongoing monitoring and refinement ensure sustained performance. We provide regular reviews, recommendations, and support as your needs evolve.
Why AI Systems Need Dedicated Monitoring
Catch Silent Failures
AI systems can return confidently wrong answers without triggering traditional error alerts. Dedicated monitoring catches quality degradation, hallucination spikes, and drift before users lose trust.
Control AI Costs
Inference costs are the new server costs. Without tracking cost per query, token usage patterns, and model efficiency, AI spending grows unchecked. We make AI economics visible and controllable.
Ship AI with Confidence
Production AI needs the same operational rigor as any mission-critical system. Monitoring gives your team confidence to ship AI features knowing they can detect and respond to issues fast.
Running AI in Production?
Tell us about your AI systems and we’ll design a monitoring strategy that covers model health, pipeline reliability, cost tracking, and application performance.