How LeoLabs Secures Low Earth Orbit with InfluxDB

Overview

A Living Map of Low Earth Orbit

LeoLabs is a leading orbital intelligence provider that produces a continuously updated “living map” of Low Earth Orbit (LEO). Today, LeoLabs tracks more than 25,000 objects, from active satellites to space debris, in the region from the Earth’s surface to roughly 2,500 km above the planet.

Organizations rely on LeoLabs for:

• Space Domain Awareness (SDA): Identifying and monitoring objects that may threaten spacecraft.
• Space Traffic Management (STM): Predicting collisions and enabling safer launch and orbital operations.

To generate this map, LeoLabs operates a global network of radar installations, including active sites and locations in Western Australia, Alaska, New Zealand, and Maui. Because these installations are typically located in remote environments and run autonomously without full-time staff on site, real-time telemetry from these systems is the only way engineers can monitor system health and diagnose problems before they impact orbital safety.

Challenge

Managing telemetry from the edge

Each radar site produces telemetry from thousands of hardware components, including amplifiers and environmental sensors. Across the entire network, LeoLabs also operates 2,000 to 3,000 devices actually running an operating system, according to Jackson Ball, Radar Software Engineer at LeoLabs.

Operations teams use this telemetry to maintain what Ball describes as “high nines of uptime.”

At the same time, LeoLabs retains three years of full-resolution telemetry history. Engineering teams use it to investigate hardware failures, identify long-term performance degradation in radar systems, and analyze how radar behavior changes alongside seasonal atmospheric conditions.

Enter InfluxDB

Building an automated telemetry platform

As LeoLabs expanded its radar network, the scale of telemetry data grew rapidly. The large number of unique time series (high cardinality), combined with long-term data retention requirements, creates a demanding time series data challenge.

The team initially ran a self-hosted open source InfluxDB deployment, but as the radar network expanded, query performance and operational complexity began to limit the architecture. To support continued growth, the team migrated to InfluxDB Cloud Dedicated, adopting a new architecture built around a single source of truth for telemetry.

The new telemetry stack is highly automated to manage the global radar network:

• InfluxDB Cloud Dedicated: Central storage engine for all high-fidelity time series telemetry.
• Telegraf: Universal agent, with plugins, used to collect and ingest telemetry from radar infrastructure across the global network.
• NetBox + Ansible: NetBox serves as the source of truth for device configuration, whileAnsible automatically generates and deploys thousands of Telegraf configurations via GitHub Actions.

Together, these tools create a closed-loop automation process. When a maintainer replaces a device in the field, they update the record in NetBox. The automation pipeline propagates the change through the stack, generating and deploying the appropriate telemetry configuration automatically. As a result, the observability layer always reflects the live infrastructure with minimal manual intervention.

In practice, LeoLabs applies Infrastructure-as-Code principles to its observability platform,  allowing engineers to manage a global radar network through version-controlled configuration rather than manual operational processes.

Result

From reactive monitoring to proactive operations

The implementation of InfluxDB Cloud Dedicated transformed LeoLabs from reactive firefighting to a proactive, data-driven operational model. Engineers now have continuous visibility into the health and performance of radar systems across the global network.

The telemetry platform also enables advanced diagnostics using custom Python and Pandas tools. One example is the Waterfall Plot, built on data stored in InfluxDB. This visualization reveals complex failure patterns that would be difficult to detect through static alerts alone. In one instance, operators identified a cascading hardware issue where a power supply failure in a cooling fan bank caused eight amplifiers to overheat.

The system has also significantly improved operational efficiency. A lean, distributed team now manages over 1,000 alerts and more than 500 PDU outlets globally, all without dedicated staff at radar sites. Engineers describe the system’s greatest advantage as its “boring” reliability: the infrastructure simply runs in the background and scales automatically, delivering the uptime required for mission-critical operations.

With the telemetry platform operating reliably in the background, LeoLabs engineers can focus on expanding the radar network rather than maintaining database infrastructure.

Finally, the move to a hosted, S3-backed storage architecture also improved the economics of long-term telemetry retention. By separating compute and storage, LeoLabs can retain three years of high-resolution telemetry without the cost and performance constraints associated with traditional SSD storage.

What’s Next

The Future of Orbital Intelligence

By offloading the complexities of time series management to InfluxDB Cloud Dedicated, LeoLabs has built a unified telemetry foundation required to operate a global, autonomous radar network.

The company is now positioned for rapid expansion, with new radars in development in Maui and across the Indo-Pacific. As LeoLabs scales toward its goal of 100+ radar installations, InfluxDB provides the telemetry backbone needed to add new sensors without increasing operational complexity.

For organizations operating mission-critical infrastructure in space and other industrial environments, InfluxDB provides the scale, reliability, and long-term telemetry retention required for the next generation of orbital intelligence.