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Distributed Tracing for Microservices

Distributed Tracing

Troubleshoot microservices using AI to compress MTTR and control spend with dynamic sampling.

Challenges

Complex Microservices and High Trace Costs Drive Up MTTR

Isolating failures across microservices demands deep system knowledge, while heavy sampling forces a choice between blind spots and budget overruns.
Manual Span Inspections Slow Incident MTTR
Manual Span Inspections Slow Incident MTTR

Manual Span Inspections Slow Incident MTTR

Sifting through raw traces across microservices causes cognitive overload. Constructing complex queries under pressure delays root-cause isolation.
Tracing Expertise Gaps Hinder Team-Wide Triage
Tracing Expertise Gaps Hinder Team-Wide Triage

Tracing Expertise Gaps Hinder Team-Wide Triage

Distributed tracing requires specialized query knowledge and schema familiarity. Without expertise, engineers struggle to extract context quickly.
Static Trace Sampling Sacrifices Critical Data
Static Trace Sampling Sacrifices Critical Data

Static Trace Sampling Sacrifices Critical Data

To prevent budget overruns, teams apply heavy static sampling. This drops essential spans from key user flows, forcing triage with missing context.
Solutions

Control Trace Volumes Dynamically and Speed Up MTTR

Cortex® XCOR™ pairs chat-led AI workflows with dynamic controls to retain high-signal telemetry, accelerate MTTR, and keep costs predictable.

Pinpoint Root Cause with AI Workflows

XCOR Operator and AI investigations evaluate full context using natural language to guide engineers to root causes fast.

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Isolate State Changes with DDx

Compares degraded trace spans against healthy baselines to surface exact state changes, accelerating root-cause isolation.

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Optimize Trace Ingestion with Dynamic Controls

Dynamically adjust collection rules and prioritize by service flow without redeploying code—capturing critical traces while aligning spend to value.

Product tour
Key capabilities

How Cortex XCOR Distributed Tracing Works

Cortex XCOR pairs AI-driven insights with dynamic trace sampling to pinpoint issues quickly and control costs without losing critical visibility.
Accelerate Operations with AI Workflows

Accelerate Operations with AI Workflows

Replace static dashboards with conversational AI. Operator enables engineers of any skill level to execute tasks and isolate root causes fast.
Isolate System State Changes with DDx

Isolate System State Changes with DDx

Compares degraded trace spans against healthy baselines to surface exact state changes, accelerating root-cause isolation.
Configure Dynamic Head and Tail Sampling

Configure Dynamic Head and Tail Sampling

Dynamically adjust head and tail sampling rates server-side without redeploying code. Retain critical error spans while filtering telemetry noise.
Govern Sampling with Datasets and Behaviors

Govern Sampling with Datasets and Behaviors

Group trace workloads into Datasets and apply Behaviors. Dynamically dial up capture rates during incidents, then scale down to control costs.
Unify Service Telemetry in Context

Unify Service Telemetry in Context

Automate service discovery to deliver service-centric views. Unify metrics, traces, logs, and change events in context to assess health fast.
Investigate Visual Span Anomalies

Investigate Visual Span Anomalies

Drill down from visual anomalies and heatmaps directly into raw traces, maintaining context across complex, multi-service execution paths.
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Benefits

Control Trace Spend and Compress MTTR

Transform distributed tracing into a high-signal asset. AI-guided analysis and dynamic sampling eliminate wasted spend while accelerating MTTR.
Simplify Troubleshooting
Simplify Troubleshooting

Simplify Troubleshooting

Combine DDx baseline comparisons with AI investigations to simplify workflows and compress MTTR.
Reduce Tracing Costs
Reduce Tracing Costs

Reduce Tracing Costs

Adjust head and tail sampling in real time to capture critical trace signals and trim noise.
Keep Spend Predictable
Keep Spend Predictable

Keep Spend Predictable

Organize traces into Datasets to track consumption, govern sampling rates, and protect budgets.
Avoid Vendor Lock-in
Avoid Vendor Lock-in

Avoid Vendor Lock-in

Ingest trace data natively via OpenTelemetry standards, avoiding lock-in and keeping flexibility.

Frequently Asked Questions

DDx compares degraded trace spans against healthy baselines to surface exact state changes. AI Workflows leverage DDx alongside metrics, logs, and traces to evaluate full context, guiding engineers of any skill level to root causes fast to compress MTTR.
Cortex XCOR ingests trace data natively via OpenTelemetry standards and OTLP protocols. You can send spans directly from applications instrumented with the OpenTelemetry SDK to the Cortex XDOT Collector, a fully supported distribution of the OpenTelemetry Collector.
Yes. The Optimization Engine dynamically manages head sampling using JaegerRemoteSampler protocols. Platform leads can adjust sampling strategies, rate limits, and probabilistic rules across root services in real time without modifying application code or restarting containers.
Tail sampling evaluates complete execution paths after traces finish. Configured engine rules retain critical error spans, latency outliers, and key user flows, while dropping repetitive health checks and low-value spans before data is committed to storage.
Datasets group trace data by service or workload, while Behaviors dictate sampling rules. Teams set baseline collection rates by workload priority and dynamically scale sampling during deployments or incidents, dialing rates back down after resolution to keep spend predictable.