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Advanced AI Observability

Full-stack observability from infrastructure to AI. Every signal in one place. Every cost tied to the workload that caused it.

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Centralized Dashboard

Infrastructure to Application Correlation

Predictive Alerts and Incident Management

Blind Spots That Kill AI in Production
Advanced Observability closes the gap between signals and action. You get coverage, routing, and a run workflow your teams can sustain.

Alert Fatigue

190 alerts are ignored. One real outage is buried. We alert on service health, not raw spikes. Your phone stays silent unless the app breaks.

Signal Fragmentation

Metrics in one tool. Traces in another. You can't see the link. We correlate every signal. If latency spikes, you know if it’s the model or the GPU.

No Ownership Routing

An alert fires. Nobody moves. We route incidents to the exact team who can fix them. Platform, App, or AI.

No Path to Excellence

Stop firefighting. We build a specialized Center of Excellence to own observability across your organization.

Full-Stack Observability Infrastructure to AI
Full Coverage
One map for your hardware and your code. Track signals from routers to apps. See how Kubernetes affects your database in one view.
Actionable Response
Alerts for service health. Stop the midnight noise from 1% spikes. Manage your on-call schedules and escalation chains in the same dashboard.
AI Observability
Track token spend, GPU thermals, LLM response times, and VectorDB performance. Every request is categorized by type. Every cost visible.
On-Prem AI Observability
GenAI Visibility. Inside Your Perimeter. Your AI workloads don't leave your environment. Your observability shouldn't either.
01 Model Request Visibility
02 Token and Request Categorization
03 Cost Monitoring
04 RAG and VectorDB Monitoring
Featured Case Study
Government Ministry
500 TB Migrated 300 Servers 5 Months Zero downtime

"The OmniOps team demonstrated exceptional commitment… Their expertise and dedication to building a secure and reliable Google Cloud Platform environment were key to the project’s success "

Sovereign AI Lead
Sovereign AI Lead General Manager
 PMO & Technical Delivery, Saudia Airlines
Read the full story
Scoping to Value in Weeks
Scoping
Weeks 1-2
Map what you have. Find what's missing.
Instrumentation
Weeks 3-8
Collect telemetry signals. Instrument infrastructure and AI workloads in parallel.
Implementation
Weeks 9+
Deploy dashboards and alerts. Train your team to operate the stack.
Handover Train your team to operate the stack. Full documentation and knowledge transfer included.

Stop Guessing. Start Seeing.

Tell us what you're running. We'll tell you what it takes.

Request Observability Assessment
Not ready for a demo? Talk to Engineering

Frequently Asked Questions

First dashboards and alerts typically land within weeks of instrumentation. Full implementation depends on scope and access cycles.

Yes. We instrument on-prem, cloud, hybrid, and air-gapped workloads.

Yes. LLM response times, token costs, GPU utilization, and VectorDB performance. For on-prem and air-gapped deployments, coverage includes model request visibility, token categorization, cost monitoring, and RAG performance. If you run Bunyan or Rekaz, the AI observability layer integrates directly

Managed service is available by contract. Coverage depends on scope and delivery model.

One incident example. Current dashboard access. Ownership contacts. We scope from there.