A Unified AI Data Center Management Platform Built for Infrastructure Operators
The Sensaka AI Data Center Management Platform connects AI infrastructure, heterogeneous compute resources, GPU clusters, training and inference workloads, networks, storage, liquid cooling systems, and operational processes in one platform. It transforms fragmented computing resources into business capabilities that can be monitored, scheduled, measured, and continuously optimized.
From liquid cooling systems, servers, and accelerator cards in the data center to resource pools, workload scheduling, alarms, service tickets, CMDB, cost metering, and operational analytics, the platform builds a unified operational loop on top of a shared data foundation.
From Infrastructure Delivery to Continuous Operations
Once an AI data center has been built, management priorities shift from infrastructure delivery and hardware quantity to compute utilization, workload success rates, service quality, operating costs, and business output.
Traditional monitoring platforms can usually tell operators whether a device is online. They cannot easily explain whether computing resources are being used effectively, why training jobs are waiting, whether low GPU utilization is caused by compute, network, or storage bottlenecks, or which workloads and business services will be affected by a hardware failure.
The Sensaka AI Data Center Operations Management Platform connects five operational dimensions: people, physical infrastructure, activities, compute resources, and business services.
It brings infrastructure status, computing resources, workload relationships, service delivery, staff responsibility, and operational workflows into one unified management environment, helping organizations move from an infrastructure delivery model to continuous AI data center operations.
One Platform for Unified AI Data Center Management
Unified Monitoring
Centralize the status, performance, capacity, and alarms of servers, accelerator cards, networks, storage systems, racks, power systems, liquid cooling equipment, and environmental infrastructure.
Unified Management
Use an AI infrastructure CMDB to establish real time relationships between data centers, devices, accelerator cards, clusters, workloads, projects, teams, and business services.
Unified Scheduling
Convert distributed physical compute resources into standardized resource pools that can be requested, allocated, released, and governed.
Unified Metering
Measure accelerator hours, resource consumption, energy usage, and operational data by project, tenant, and resource type.
Unified Operations
Connect monitoring, analysis, diagnostics, alarms, service tickets, approvals, and scheduling actions into a complete operational workflow.
Manage Multi Vendor GPU and NPU Resources in One Platform
AI data centers often contain accelerator hardware from different vendors, models, and architectures. Sensaka provides unified heterogeneous compute management across these environments.
The platform identifies accelerator types within each node, brings GPUs and NPUs from different vendors into a common inventory, and establishes relationships between accelerator model, quantity, physical node, operational status, and resource allocation.
Core Capabilities
- Automatic identification of multi vendor GPUs and NPUs
- Component level inventory for accelerator cards
- Unified management across regions and clusters
- Card level utilization, temperature, power, and health monitoring
- Abnormal accelerator detection and isolation recommendations
- Unified visibility for full card and partitioned resources
Business Value
Reduce dependency on separate vendor tools and manage heterogeneous compute resources through one consistent interface.
This creates a trusted data foundation for scheduling, capacity planning, infrastructure governance, and operational decision making.
Turn GPU Hardware into Allocatable Computing Resources
Sensaka abstracts physical accelerator cards into resource pools and manages training, inference, and development workloads through standardized specifications, tenant quotas, workload queues, and scheduling policies.
The platform supports dedicated GPU allocation, shared GPU partitions, resource weighting, node filling strategies, quota controls, and automatic release after workload completion. These capabilities help AI data centers reduce fragmentation, idle resources, and inefficient allocation.
Core Capabilities
- Heterogeneous compute resource pooling
- Resource specifications for training, inference, and development
- Training and inference workload classification
- Workload queue management
- Project and tenant quota controls
- Dedicated GPU and shared partition allocation
- Priority, resource weight, and node filling strategies
- Automatic resource release after completion or timeout
- Faulty node isolation and workload rescheduling
- GPU memory fragmentation analysis and consolidation recommendations
Business Value
Improve GPU utilization, reduce over allocation and idle capacity, and prioritize computing resources for the highest value workloads.
Trace Every Workload to the Accelerator Card
The platform establishes dynamic relationships between workloads, containers, GPUs, nodes, and resource pools. Operators can identify which containers are using a specific GPU and view which accelerator cards have been assigned to a particular container.
Sensaka also monitors requested GPU memory, resource limits, and actual peak consumption, while recording GPU binding and release events for resource analysis, incident investigation, and auditing.
Core Capabilities
- Bidirectional GPU and container lookup
- GPU UUID and container relationship mapping
- GPU binding and release timelines
- Requested, limited, and peak container memory analysis
- Identification of high allocation and low utilization workloads
- Shared GPU resource contention analysis
- Correlation between container failures and resource overcommitment
Analyze GPUs, Training Networks, and Storage on One Timeline
Low GPU utilization does not always indicate insufficient compute demand. Network packet loss, inadequate storage throughput, slow data loading, or hardware problems may force GPUs to wait and leave expensive computing resources idle.
Sensaka brings GPU utilization, network latency, RoCE or RDMA packet loss and retransmission, storage throughput, IOPS, read and write latency, and node health together on a shared operational timeline.
Core Capabilities
- Time aligned analysis of GPU, network, and storage metrics
- RoCE and RDMA network bottleneck analysis
- Combined analysis of packet loss, retransmission, latency, and port health
- Storage throughput, IOPS, and latency analysis
- Data loading and checkpoint write bottleneck identification
- Combined temperature, power, ECC, and hardware health analysis
- A four stage operational loop covering monitoring, analysis, diagnostics, and scheduling
- Recommendation generation, authorized execution, and operational auditing
Business Value
Quickly identify whether a performance problem comes from compute, network, storage, or hardware infrastructure.
Reduce cross team investigation time and lower the risk of long running training jobs being interrupted, restarted, or left consuming resources without productive output.
Unified Liquid Cooling and Facility Monitoring for High Density AI Infrastructure
As GPU server power consumption and rack density continue to increase, liquid cooling becomes a critical part of reliable AI data center operations.
Sensaka monitors liquid cooling system status, inlet and outlet temperature, pressure differences, and related environmental indicators. These metrics are correlated with rack power density, temperature, humidity, leak detection, and power distribution data to help operators identify cooling failures and thermal risks.
Core Capabilities
- CDU and liquid cooling system status monitoring
- Inlet and outlet temperature monitoring
- Pressure difference monitoring
- Temperature and humidity monitoring
- Liquid leak event monitoring
- Rack level power density analysis
- Identification of constrained racks and thermal hotspots
- Power distribution and PDU circuit monitoring
- Combined analysis of rack space, power, and cooling capacity
Business Value
Bring liquid cooling monitoring into the broader AI infrastructure operations environment.
Identify temperature, pressure, leakage, and capacity risks before they affect high density GPU clusters.
Turn the Asset Inventory into a Real Time Relationship Data Foundation
The Sensaka CMDB is more than a static list of devices. It continuously records relationships between people, physical infrastructure, operational activities, computing resources, and business services.
This common data foundation supports monitoring, scheduling, metering, alarm management, service tickets, and business impact analysis.
Core Capabilities
- Automatic device and component discovery
- Data center, rack, and rack unit relationship management
- Unified inventory for servers, GPUs, networks, storage, and liquid cooling systems
- Workload, container, node, and accelerator relationship tracking
- Tenant, project, team, and owner associations
- Configuration change tracking and historical snapshots
- Automatic impact analysis through relationship mapping
- One shared configuration source for monitoring, scheduling, metering, and service management
From Infrastructure Monitoring to Closed Loop Operations
The Sensaka AI Data Center Operations Management Platform connects resource discovery, device onboarding, resource pooling, workload requests, intelligent scheduling, metering, analytics, and operational optimization. Operational insights can be used to adjust resource specifications, scheduling policies, quotas, and management scope, creating a continuous optimization loop.
Resource Readiness
Automatically discover devices, manage heterogeneous compute resources, and establish standardized resource pools and specifications.
Workload Delivery
Allocate computing resources to training, inference, and development workloads through queues, project quotas, and scheduling policies.
Operational Optimization
Continuously optimize resource allocation and operational policies using utilization, workload status, compute, network and storage performance, hardware health, and cost data.
Built for Every AI Infrastructure Environment
Enterprise AI Data Centers
Manage GPU servers, networks, storage, liquid cooling infrastructure, and workload scheduling in one platform to improve internal resource utilization.
Public Computing Centers
Establish multi tenant resource pools, project quotas, workload queues, and usage reporting to improve resource delivery and commercial operations.
Financial Services and High Availability Industries
Use in band and out of band collection, component level monitoring, failure impact analysis, and auditable operational processes to protect critical training and inference services.
Telecommunications and Multi Site Environments
Centrally manage regional data centers, distributed clusters, infrastructure resources, and operational data across multiple locations.
Universities and Research Organizations
Manage GPU requests, quotas, allocation, usage, and resource release across research teams, departments, and projects.
What Changes When Every Accelerator Is Managed
Gain Complete Compute Visibility
See GPUs, NPUs, servers, networks, storage, liquid cooling equipment, and rack resources through one platform.
Improve Resource Utilization
Reduce compute waste through resource pooling, specification management, workload scheduling, and idle resource identification.
Accelerate Problem Resolution
Use GPU and container correlation together with compute, network and storage analysis to identify bottlenecks and business impact more quickly.
Protect Training and Inference Stability
Detect hardware, network, storage, temperature, and liquid cooling risks before they interrupt workloads.
Build a Trusted Operations Data Foundation
Connect resources, workloads, projects, staff, costs, and business services to support operational and management decisions.
Explore Sensaka AI Infrastructure Solutions
Make Every Accelerator Visible, Manageable, Schedulable, and Measurable
The Sensaka AI Data Center Operations Management Platform helps organizations manage AI infrastructure and heterogeneous compute resources through one unified environment. It creates a complete operational loop from device monitoring and GPU resource scheduling to compute, network and storage coordination, liquid cooling monitoring, CMDB, metering, and operational optimization.