During the November 13 Federal Aviation Administration outage, the agency's internal telecommunications network generated 16,000 problem alerts, compared with roughly 8,000 on a typical day, according to a person briefed on the disruption who spoke to The Washington Post. The person described problems with the network's timing system that could prevent messages from reaching their destinations. The underlying cause remained unclear at the time of the report. The ground stop itself lasted more than four hours and ended shortly after 8 p.m. Eastern.

A surge of that size raises questions about how teams correlate related alerts and identify the initiating fault. The reporting does not establish whether alert overload delayed the FAA's response, how many operators were on duty, or what the dashboards looked like. Alert count and telemetry volume are different measures. Twice as many alerts does not establish twice as much stored data or a larger bill.

My concern is the cost of architectures that charge for storing low-value telemetry and then add a separate investigation layer to make that data useful. CIOs should test whether the second layer delivers enough operational value to justify its cost. Most IT leaders face an excess of tickets, alerts, and log files, and they want a clean way to prioritize the most critical ones first. Where an incident increases billable telemetry volume, usage-based ingestion and processing costs can rise. The FAA reporting does not describe the agency's observability vendors, contracts, or ingestion costs.

Four layers for controlling telemetry cost and incident workload

My evaluation priority is four overlapping capabilities that can reduce what enters paid storage or reduce the work that follows. They are not four separate products every organization needs, and only the first necessarily operates before downstream indexing charges.

Telemetry pipelines filter and route before downstream indexing or retention charges

Tools such as Cribl Stream, Mezmo, and Edge Delta sit between infrastructure and storage backends. They filter routine health checks, deduplicate repetitive events, and can route compliance logs to object storage that meets the applicable retention, integrity, and access requirements. Palo Alto Networks acquired Chronosphere and uses related optimization inside Cortex XCOR. The company says the launch announcement claimed an average data optimization of 89 percent. I covered the XCOR launch on October 17: Palo Alto Networks Ships Cortex XCOR to Diagnose Alerts in Under Three Minutes. XCOR itself describes charges for data processed before optimization and data persisted afterward.

Cross-domain event correlation groups related alerts into one incident

Platforms such as BigPanda, PagerDuty AIOps, and ServiceNow ITOM sit above multiple monitoring tools. They use service topology and timing to group related alerts. Elastic is pursuing automated investigation across security and observability. Attack Discovery investigates and validates potential security threats before presenting them as attacks. Separately, Elastic completed its acquisition of Deductive AI on August 24, 2026, to advance production-incident investigation in Elastic Observability. That earlier coverage is here: Elastic's Attack Discovery Now Investigates Alerts Before It Flags Them.

Autonomous investigation agents start queries when an alert fires

Newer capabilities launch queries against logs, traces, runbooks, and recent deployments when an alert arrives. Palo Alto Networks says its Cortex XCOR agent completes investigations and recommended actions in under three minutes on average and claimed a 75 percent root-cause analysis success rate in the launch announcement. The company has also described a typical manual investigation as taking about twenty minutes. Automatic investigation can run alongside human notification. Elastic is developing production-incident investigation through Deductive AI's technology in Elastic Observability. Vendors such as Sherlocks AI also offer alert-triggered investigation. Dynatrace has reported more than 1,100 customers using its agents for autonomous operations as it closed the Arize deal on October 1; that context is in the November 8 post: 1,100 Customers Run Dynatrace Agents as $915 Million Arize Deal Closes.

Systems of record remain necessary for deep querying and forensics

Core platforms such as Datadog, Dynatrace, Splunk, New Relic, and Grafana Cloud collect, index, and store metrics, logs, and traces. They layer built-in machine learning anomaly detection on top of their own data stores. These systems stay essential provided you govern what enters them. Feeding these platforms unfiltered telemetry can inflate the storage and analysis bill.

Buying more storage capacity alone does not address the work of correlating related alerts or isolating an initiating fault. Resilience comes from controlling what enters paid systems and from automating the first minutes of investigation. The earlier resilience review that used the ground stop as one of four scenarios is here: FAA Ground Stop Hands CIOs Four Resilience Questions for Monday.

About Shashi Bellamkonda

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