Cloud Native Observability >> AI Native Deep Observability
AI Native and AI Workloads demand Deep Observability:
From delivering “software-as-a-service” for people, to delivering “service-as-a-software” powered by AI Agents
The shift from SaaS to AaaS pushes SaaS companies to evolve from static software to AI-driven, autonomous agents, transforming their business models and user experiences. Users benefit from intelligent automation and proactive decision-making, reducing manual effort and enabling seamless, self-optimizing workflows.
Key Differences
From traditional monitoring to AI-driven full-stack observability – AI workloads require deep insights across GPUs, AI agents, workloads, and infrastructure.
From reactive to predictive observability – AI predicts anomalies and optimizes system performance in real-time.
From isolated monitoring to holistic AI observability – Correlates data across compute, storage, networking, and AI frameworks.
From static dashboards to autonomous self-healing – AI automates performance tuning and issue resolution.
How AI Native Observability Works
Full-stack AI workload monitoring – Tracks GPUs, ML models, AI agents, and workloads.
AI-driven anomaly detection – Identifies deviations in AI inference times, GPU utilization, and data pipelines.
Automated root cause analysis – Diagnoses bottlenecks across AI model training and serving environments.
Dynamic performance optimization – AI-based auto-tuning for workload efficiency.
Security and compliance monitoring – AI-driven threat detection across AI/ML pipelines.
Use Cases
AI Workload Observability – Monitors model performance, GPU load, and inference times.
Kubernetes AI Observability – Tracks AI workloads across containerized environments.
AI Agent Monitoring – Observes behavior, decision-making patterns, and efficiency of AI agents.
Multi-Cloud AI Observability – Unified monitoring across on-prem, cloud, and edge AI deployments.
Autonomous AI Performance Optimization – AI-driven tuning of resources and workload distribution
Key Players
AI-Driven Observability Platforms – Dynatrace, New Relic AI, Datadog, Splunk Observability Cloud.
GPU & AI Workload Monitoring – NVIDIA DCGM, Prometheus AI, Grafana Loki with AI insights.
Security & Compliance AI – Lacework AI, Palo Alto Cortex XDR, AWS GuardDuty AI.
Full-Stack AI Observability – Cisco AppDynamics AI, Google Cloud Operations Suite, Azure Monitor AI.