# How GoComet Runs Fully Air-Gapped Observability at up to 90% Lower Cost

## Metadata

- **Canonical HTML**: https://ctrlb.ai/customers/gocomet
- **Customer**: GoComet (logistics and supply chain)
- **Use case**: On-prem, air-gapped observability

## Behind GoComet's Air-Gapped Observability with CtrlB

GoComet's platform covers a shipment from the first quote to the final invoice. More than 230 global brands across 35 countries run their logistics on it, including pharmaceutical manufacturers such as Sun Pharma and Glenmark. Observability had to run on GoComet's own infrastructure, with no telemetry leaving its network, and it had to cost a fraction of what it did.

**This is where CtrlB came in.**

## The challenge: Full observability without letting telemetry leave the network

GoComet's engineers needed application and infrastructure logs, distributed traces, dashboards and alerts, all fast enough to use mid-incident — and every byte of telemetry had to stay on-prem in a fully air-gapped environment.

SaaS observability platforms ship data to the vendor's cloud. Assembling a self-hosted stack from several tools brings its own problems: separate stores for logs and traces, schema work every time a service adds a field, and engineers stitching results together by hand while an incident is still open.

At the same ingest volume, GoComet's previous observability stack cost roughly 10x what it costs on CtrlB today.

## The solution: One air-gapped data lake for all of GoComet's telemetry

GoComet now ingests its application and infrastructure telemetry into CtrlB, deployed on-prem and fully air-gapped. Monitoring, debugging and root-cause analysis all run on that single data lake, and nothing leaves GoComet's network.

## The results: Up to 90% lower spend, and every byte stays on-prem

- **Telemetry spend**: up to 90% lower at the same ingest volume (roughly a tenth of the previous bill)
- **100% on-prem**: application logs, infrastructure logs and traces are stored, searched and analyzed inside GoComet's network; AI Insight Engine and MCP connectors work on that same data
- **One lake** for application logs, infrastructure logs and traces — log-to-trace pivots happen in one place

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