Introducing Cortex XCOR: Observability, reimagined. Discover now.
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AI Workload Observability

Observability for Demanding AI Workloads

Automate root cause analysis, sustain extreme telemetry scale, and control spend

Challenges

Operational Bottlenecks of Production AI Systems

Operating AI at scale creates operational friction and blind spots that threaten performance, reliability, and spend predictability.
Slow Root Cause Analysis Across Complex Stacks

Slow Root Cause Analysis Across Complex Stacks

Isolating whether bottlenecks stem from app code, orchestration layers, or GPU clusters requires manual correlation across fragmented systems.
Legacy Observability Buckles Under AI Scale

Legacy Observability Buckles Under AI Scale

High-throughput AI workloads create massive telemetry spikes. Legacy tools choke, causing query lag, frozen dashboards, and critical blind spots
High-Cardinality Telemetry and Volume Overload
High-Cardinality Telemetry and Volume Overload

High-Cardinality Telemetry and Volume Overload

High-throughput AI apps generate high-volume, high-cardinality telemetry. Paying for low-value noise drives overages that erode margins.
Solutions

Extreme Scale Performance with Predictable Spend Control

Automate root cause analysis across complex stacks, sustain extreme telemetry scale, and keep observability spend predictable.

Automated Investigations Across the AI Stack

Investigations responds autonomously when system alerts trigger, delivering evidence-backed root cause analysis to help teams resolve incidents fast.

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Sustain Extreme Scale with Low Latency

Process high-throughput AI workloads with a data engine proven to sustain over 3 billion data points per second with millisecond query latency.

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Eliminate Telemetry Waste

Control observability spend with the Optimization Engine. Filter low-value data in real time, reducing volumes by 89% on average.

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Key capabilities

Purpose-Built Capabilities for AI Workloads

From automated triage to real-time telemetry shaping, Cortex® XCOR™ provides the visibility, scale and control needed for demanding AI workloads.
Automate Root Cause Analysis for AI Workloads
Automate Root Cause Analysis for AI Workloads

Automate Root Cause Analysis for AI Workloads

Automate incident triage when alerts fire. AI Investigations evaluates topology, telemetry, and saved context to isolate root causes fast.
AI-Guided Operations Driven by Chat

AI-Guided Operations Driven by Chat

Replace static dashboards with conversational AI. Operator understands requests and directs specialized agents to execute complex operational workflows.
Real-Time Ingestion Spend Governance

Real-Time Ingestion Spend Governance

Filter low-value noise at ingestion with the Optimization Engine, helping teams achieve an average 89% reduction in telemetry volume.
Enforce Capacity Guardrails & Budgets
Enforce Capacity Guardrails & Budgets

Enforce Capacity Guardrails & Budgets

Allocate telemetry capacity via credit pools. Consumption Budgets trigger Optimization Engine rules automatically before capacity limits are breached.
Deep Infrastructure and GPU Visibility
Deep Infrastructure and GPU Visibility

Deep Infrastructure and GPU Visibility

Track VRAM utilization, compute allocations, and I/O bottlenecks across GPU clusters to prevent compute stalls and maximize throughput.
Sustain Extreme Scale with Low Latency
Sustain Extreme Scale with Low Latency

Sustain Extreme Scale with Low Latency

Process high-throughput AI workloads with a data engine proven to sustain over 3 billion data points per second with millisecond query latency.
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Benefits

Scale Production AI Systems with Confidence

Drive high system availability, maximize GPU utilization, and eliminate telemetry waste with observability built for high-throughput AI workloads.
Compress MTTR
Compress MTTR

Compress MTTR

Automate incident response with Investigations to isolate root causes and accelerate remediation.
Maximize GPU Compute Throughput
Maximize GPU Compute Throughput

Maximize GPU Compute Throughput

Isolate hardware bottlenecks and idle compute stalls. Monitor VRAM to keep GPU pipelines running fast.
Sustain Extreme AI Scale
Sustain Extreme AI Scale

Sustain Extreme AI Scale

Process over 3B data points per second with millisecond query speed across massive GPU clusters.
Enforce Long-Term Cost Control
Enforce Long-Term Cost Control

Enforce Long-Term Cost Control

Eliminate telemetry waste before storage fees accrue to cut data spend by an average 89%.

Frequently Asked Questions

When an alert triggers, Investigations launches automatically and reasons over Cortex XCOR’s Operational Fabric—combining system topology, custom and standard telemetry, and operational knowledge—to deliver evidence-based root cause analysis to compress MTTR.
Cortex XCOR uses OpenTelemetry distributed tracing to map request flows across the microservices backing your AI stack. SREs isolate latency spikes and failures across high-throughput workloads, correlating service calls directly with GPU and host metrics without proprietary agents.
Cortex XCOR is proven to sustain extreme-scale workloads exceeding 3 billion data points per second with millisecond query latency. The platform provides a 99.9% SLA and has historically maintained >99.99% uptime for high-throughput AI environments.
The Cortex XCOR Optimization Engine applies fine-grained Optimization Rules directly at ingestion before storage fees accrue. By filtering out low value telemetry data, organizations reduce telemetry volumes by 89% on average while preserving high-cardinality detail.
Yes. Cortex XCOR supports a Bring Your Own Evals framework. While engineering teams maintain their specialized evaluation models and quality scoring pipelines, Cortex XCOR ingests and correlates those evaluation outputs alongside full-stack system metrics, traces, and infrastructure logs.