AI & GPU Monitoring Services
Monitoring for GPU clusters and AI applications: GPU health and usage, model latency, errors, token use and cost, in one place.
About our AI and GPU monitoring
AI systems fail in new ways. A GPU overheats and slows down. A model takes eight seconds to answer instead of two. Token costs jump overnight. Normal server monitoring misses most of this. Our AI and GPU monitoring adds the signals that matter for AI: GPU health and usage, model latency and errors, token use and cost per request. You spot problems early. And you know what each model really costs.
What's included in our AI and GPU monitoring
How we deliver AI and GPU monitoring
We build on the open tools we already use for infrastructure monitoring. The NVIDIA DCGM Exporter feeds GPU metrics into Prometheus. OpenTelemetry traces each AI request from the user to the model and back. Grafana brings it together in dashboards for operations, engineering and management. Then we tune the alerts. Your team hears about real problems, not noise.
Tools and technology for AI and GPU monitoring
Outcomes of our AI and GPU monitoring
- GPU problems caught before they slow down training or users
- A clear cost for every model and every request
- One view of your AI and the infrastructure under it
Frequently asked questions about AI and GPU monitoring
What should we monitor in an AI system?
Start with three layers. The GPUs: usage, memory, temperature and errors. The model: response time, error rate and throughput. The business side: token use and cost per request. Together they tell you whether the system is healthy and what it costs to run.
Can you monitor AI services we use through an API, not just our own models?
Yes. For hosted model APIs we monitor what your application sees: response times, errors, token use and spend. For models you host yourself, we add GPU and server metrics as well.
Will this work with our existing monitoring?
Usually, yes. If you already run Prometheus and Grafana, the AI and GPU metrics sit alongside your current dashboards and alerts.
Related Services
AI Infrastructure Services
GPU servers and Kubernetes clusters for AI training and inference, set up on-premises or in your cloud and handed over ready to run.
MLOps & LLMOps Services
Pipelines that take models from experiment to production and keep them healthy: versioning, automated deployment, rollback and drift checks.
Private LLM Deployment
Open-source large language models hosted in your own data centre or cloud account, with document search (RAG) so answers come from your data.
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