AI Infrastructure CMDB and Impact Analysis
Connect every facility, GPU, workload, service and owner.
The Sensaka AI Infrastructure CMDB replaces disconnected asset lists with a live relationship model for AI data center operations. It connects physical infrastructure, compute capacity, operational activity, people and business services so teams can understand what an asset is, where it is, what it supports, who owns it and what may be affected when it fails.
A Static Asset List Cannot Explain AI Infrastructure
Traditional asset inventories record devices, serial numbers and locations. AI infrastructure operations require a broader and more dynamic view.
A single GPU can be part of a physical server, assigned to a resource pool, allocated to a container, consumed by a training or inference workload, associated with a project and connected to a service used by a business team.
When these relationships are maintained in separate tools, operators struggle to answer basic questions during an incident:
Sensaka builds the relationship context needed to answer these questions.
Model the Five Dimensions of AI Data Center Operations
The Sensaka CMDB organizes AI infrastructure around five connected dimensions.
People
Record responsible owners, operations teams, tenants, suppliers, roles and permissions. Associate infrastructure and services with the people accountable for them.
Physical Infrastructure
Manage facilities, rooms, racks, rack units, servers, accelerator cards, networks, storage systems, power equipment and liquid cooling infrastructure.
Operational Activity
Connect scheduling activity, training and inference workloads, alarms, service tickets, changes, inspections and maintenance records.
Compute Capacity
Model clusters, nodes, resource pools, specifications, quotas, assigned capacity and available capacity.
Business Services
Associate model services, inference instances, applications, agents, projects and business departments with the infrastructure they consume.
Together, these five dimensions create an operational model that is more useful than a device inventory alone.
Automatically Keep Infrastructure Data Current
AI infrastructure changes continuously. New equipment is installed, accelerator cards are replaced, workloads move, containers restart and resource assignments change.
Sensaka uses infrastructure discovery and configuration synchronization to reduce dependence on manual updates. Supported device, BMC, Kubernetes and infrastructure interfaces can provide current configuration and relationship data. The platform can record:
Authority rules and conflict handling should be agreed during implementation so teams know which source is trusted when automated discovery and manual records differ.
Manage Physical and Logical Relationships Together
AI infrastructure includes both physical and logical dependency chains.
Physical Relationships
Facility, rack, server, slot, accelerator, network port, storage connection, power path and cooling location.
Logical Relationships
Cluster, resource pool, container, workload, model service, application, project and owner.
Sensaka brings these relationships into a common graph so operators can move between the physical and logical views of the same service. For example, an operator can begin with a slow inference service, locate its instance and container, identify the GPU and node, review network and storage dependencies, and then examine the physical server and hardware health.
Understand Business Impact Before Taking Action
When an accelerator, node or infrastructure component reports a problem, Sensaka can use the relationship graph to help determine the likely impact scope.
Look Upward to Services and Business Context
Identify affected model services, inference instances, applications, projects and business departments. Where service commitments are recorded, the impact view can include the relevant service level context.
Look Downward to Infrastructure Evidence
Review accelerator, node and component status, then correlate network, storage, temperature and hardware health data around the event.
Look Across to Responsibility and Workflow
Identify the responsible owner or operations team and create a response ticket with the affected objects and investigation context attached.
This creates a clearer transition from an infrastructure alarm to a business aware operational response.
Track Changes and Reconstruct the Past
Configuration changes can explain performance problems and failures, but only when the history is available. Sensaka records changes with the previous and new values, the operator or discovery source, and the effective time. Historical snapshots help teams reconstruct the state of an asset and its relationships at an earlier point.
Where AI Infrastructure CMDB Fits
GPU Failure Impact Analysis
Start with an accelerator alarm. Identify the node, container, workload, model service, project and owner connected to the affected card. Review related temperature, power, ECC, network and storage information before assigning the response.
Slow Inference Service Investigation
Begin with the affected service and move downward through the instance, container, GPU and physical node. Determine whether the service degradation aligns with resource, hardware or infrastructure conditions.
AI Infrastructure Asset Reconciliation
Compare discovered server and accelerator configuration with the CMDB record. Identify missing assets, configuration changes or location inconsistencies.
Change and Audit Review
Review who changed an asset or relationship, when it changed and how the change was discovered. Reconstruct the configuration state at the time of an incident.
Responsibility Mapping
Associate clusters, resource pools, projects and services with owners and operations teams so incidents can be routed without searching through separate contact lists.
Delivery to Operations Handover
After a server is provisioned, register it in the CMDB, add it to monitoring and connect it to the correct facility, cluster and resource relationships.
Everything the Relationship Model Needs
Deep Physical Infrastructure Visibility, Built In
Sensaka combines CMDB capabilities with deep physical infrastructure visibility. The platform can use device management interfaces, BMC data, accelerator telemetry, Kubernetes information, network data and operational records to keep the model connected to the running environment.
This is particularly important in AI data centers because the dependency chain spans facilities, hardware, software platforms and business services. A relationship model built only from cloud or application data may miss the physical component, power, cooling and location context required for complete impact analysis.
A Useful CMDB Depends on Governance as Much as Technology
During implementation, Sensaka and the customer should define:
Frequently Asked Questions
What makes an AI infrastructure CMDB different from a traditional CMDB?
It includes accelerator cards, clusters, resource pools, containers, AI workloads, model services and their relationships with physical data center infrastructure. It must represent both fast changing logical allocations and long lived physical assets.
Can Sensaka discover GPUs automatically?
Sensaka can collect accelerator inventory and node relationships from supported hardware and software interfaces. The exact fields and update frequency depend on the vendors and management interfaces available.
Does the CMDB replace our existing enterprise CMDB?
It can operate as the AI infrastructure data foundation or integrate with an existing enterprise CMDB. The correct architecture depends on data ownership, system boundaries and the customer's current processes.
Can the platform show which business service is affected by a GPU failure?
Yes, when the required relationships from GPU to node, workload, model service, project and business service are complete. Impact analysis quality depends on the completeness and accuracy of those relationships.
Can we review historical configurations?
Sensaka supports change history and point in time snapshot reconstruction for managed configuration data. Retention and detail should be defined during implementation.
Can an impact analysis create an ITSM ticket?
Sensaka can associate the responsible team and initiate a service ticket through the available workflow or ITSM integration. The exact routing and approval process is configured for the customer environment.
Turn Infrastructure Data into Operational Context
Know what every asset supports, who owns it and what will be affected before the next action is taken.
Related: AI Infrastructure Observability, GPU Usage Metering, Liquid Cooling Monitoring