Gain visibility into infrastructure and storage performance, from on-premises arrays to cloud resources, with AI-powered forecasting and optimization. Predict growth, prevent bottlenecks, and align resources with business demand, intelligently and cost-effectively.
Key Highlights
Motadata ObserveOps applies AI-driven forecasting and cost optimization intelligence to on-premises and cloud infrastructure and storage, helping teams prevent bottlenecks before constraints hit.
Monitor storage health and performance for each platform in your estate.
Coverage for Dell EMC, HPE, Hitachi, Huawei, and NetApp storage systems.
SAN, NAS, and RAID performance monitoring from one platform.
Volume, LUN, and disk-level health tracking.
Storage array availability and fault detection as it happens.
Measure the performance signals that determine storage impact on workloads.
IOPS, latency, throughput, and disk utilization for storage tiers.
Read/write performance breakdown per volume and array.
Disk health scoring and early warning for degrading drives.
Historical performance trending for capacity planning and SLA reporting.
Predict infrastructure saturation before it constrains performance.
Machine learning models for compute, memory, storage, and network utilization.
Capacity saturation date prediction based on consumption trends.
What-if scenario modeling for planned growth and workload changes.
Forecast visualization with configurable planning horizons.
Understand how resources are consumed throughout the full infrastructure estate.
Utilization trend analysis spanning compute, storage, and network resources.
Peak utilization identification for right-sizing and scheduling decisions.
Comparative utilization reporting covering sites, environments, and time periods.
Underutilization and over-provisioning identification for reclamation.
Connect infrastructure capacity to the services and SLOs that depend on it.
Storage and compute utilization mapping to business service performance.
Identification of infrastructure constraints approaching SLO impact thresholds.
Capacity investment prioritization aligned with business service criticality.
KPI correlation linking resource behavior to service delivery outcomes.
Eliminate waste and justify infrastructure investment with data.
Identification of over-provisioned and underutilized assets throughout the estate.
License and resource reclamation recommendations.
Infrastructure cost modeling aligned with actual consumption patterns.
Rightsizing guidance for compute, storage, and cloud resource allocations.
Intelligence
Most infrastructure teams discover capacity problems only when performance degrades: storage latency spikes, CPU saturation delays transactions, or a disk fills and a service goes dark. Teams lack the forward-looking data to act before the constraint materializes.
Motadata ObserveOps combines historical utilization data, real-time performance signals, and AI-driven forecasting into a capacity intelligence layer that tells teams what will happen, extending visibility beyond current conditions. With performance-to-business mapping and cost optimization intelligence, capacity decisions align with service commitments and investment efficiency.
How It Works
Collect utilization and performance telemetry from compute, storage, and network.
Normalize consumption data into Motastore's unified model.
Apply ML forecasting models to predict saturation and bottleneck timelines.
Map capacity constraints to dependent business services and SLOs.
Identify optimization opportunities: reclamation, rightsizing, reallocation.
Deliver forecasts and recommendations through planning dashboards.
Intelligence that turns infrastructure data into capital planning confidence.
Role-Based Value
Eliminate unplanned outages from capacity exhaustion and give leadership evidence-backed investment justifications that connect resource needs to service impact.
Eliminate unplanned outages from capacity exhaustion and give leadership evidence-backed investment justifications that connect resource needs to service impact.
Eliminate unplanned outages from capacity exhaustion and give leadership evidence-backed investment justifications that connect resource needs to service impact.
Eliminate unplanned outages from capacity exhaustion and give leadership evidence-backed investment justifications that connect resource needs to service impact.
Get early warnings before capacity constraints affect availability and use utilization trend data to prevent performance-related incidents.
Get early warnings before capacity constraints affect availability and use utilization trend data to prevent performance-related incidents.
Correlate infrastructure resource availability with application performance and use AI-driven forecasts to prevent capacity constraints from causing service degradation.
Correlate infrastructure resource availability with application performance and use AI-driven forecasts to prevent capacity constraints from causing service degradation.
From Visibility to Control
Earlier capacity interventions through AI forecasting before performance impact.
Reduced infrastructure overspend through utilization optimization and rightsizing.
Higher SLA adherence by preventing resource-driven service degradations.
Stronger infrastructure investment ROI through evidence-based planning.
Operational consistency through proactive capacity management rather than reactive fixes.
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