Observability for AI Workloads
Hybrid monitoring to build and scale AI-driven workloads.
Get deep visibility into AI infrastructure, models, and applications to troubleshoot faster, reduce downtime, and optimize performance.
Benefits
Confidence to move quickly
- Optimize and maintain AI workloads from proof–of-concept to production, with visibility from underlying hardware to AI-driven applications
- Automatically discover new resources as workloads and business initiatives change
- Use predictive analytics to proactively identify system interruptions and maintain service availability
Innovate with your modern data center
- Observe components of sophisticated AI workloads in public clouds, private environments, edge locations, GPU clusters, and on-premises hardware
- Track power consumption of AI hardware to reduce energy costs and lower carbon footprint
- Proactively manage computing requirements and mitigate cost overruns
Observability across AI Infrastructure, Models, & Applications
- Comprehensive monitoring for every layer of your AI operations, including infrastructure, vector databases, machine learning, and large language models (LLM)
- Ensure your models, data pipelines, and compute resources (such as GPUs, TPUs, and CPUs) are operating efficiently, securely, and cost-effectively
- Track, analyze, and optimize the hardware and cloud environments that support AI workloads
Highly available AI-powered workloads and services
- Real-time anomaly detection to identify deviations from normal behavior, helping IT teams identify issues before they impact performance.
- Correlated alerts to filter redundant or non-critical notifications to help focus on the most important alerts
- Connect log insights with performance data to provide a comprehensive view of system health
Features
Infrastructure monitoring
Get metrics from on-prem and cloud AI hardware across Nvidia, AWS, Azure, and GCP. Visualize and alert on GPU metrics such as memory utilization, temperature, and power consumption.
Middleware insights
Enhance model performance by tracking AI platforms, vector databases, frameworks, and large language models (LLM) from OpenAI, Amazon Bedrock, Amazon SageMaker, Amazon Q Business, and Ollama.
Application observability
Integrations with OpenLIT and Traceloop provide AI application observability by delivering real-time monitoring and in-depth analytics to ensure performance and reliability.
Cost intelligence
Identify underutilized or idle AI compute resources and LLM token usage to prevent overspending with cost savings opportunities.