One Rust binary that replaces your entire observability stack — metrics, logs, traces, SIEM, LLM monitoring, GPU telemetry, and edge fleets.
One Rust binary that replaces your entire observability stack — metrics, logs, traces, SIEM, LLM monitoring, GPU telemetry, and edge fleets.
Collection agent, ingestion pipeline, query engine, storage, dashboards, and alerting — one Rust binary. No stitching together six open-source tools and a commercial SIEM.
Infrastructure, logs, traces, SIEM, LLM, ML, GPU, CPU, edge fleets, and video pipelines — all on the same temporal substrate. When something fails, you see the full picture.
Self-hosted on your servers. Air-gapped in classified environments. Managed SaaS if you prefer. Edge-constrained for factories and field sites. Your data stays where you want it.
87 SIEM capabilities, 500+ detection rules, 50+ SOAR actions, UEBA, threat intelligence, case management, and compliance mapping — built in, not bolted on.
Track prompts, completions, token costs, model drift, GPU health, and inference latency on the same platform that monitors your infrastructure. One view of your AI stack.
Offline-first edge agents with local write-ahead logs. When the network drops, telemetry buffers locally. When connectivity returns, it syncs. No data lost, no gaps in the timeline.
Lynceus is a single-binary observability platform built in Rust. It replaces the fragmented stack of metrics agents, log forwarders, APM tools, SIEM platforms, and dashboard tools that most teams stitch together — and it does it with one deployable artifact, one query language, and one source of truth.
Ten observability domains run on the same engine: infrastructure monitoring, logs, distributed traces, security information and event management, LLM and ML observability, GPU and CPU hardware telemetry, computer vision pipeline monitoring, edge fleet management, video pipeline observability, and analysis engines. When a service fails, the metric spike, the log entry, the trace span, the security alert, and the hardware anomaly are all on the same timeline — not scattered across five tools that nobody correlates.
Metrics, distributed traces, APM, service topology, SLO tracking, alerting, network monitoring, container health, and process monitoring — all native, all on the same engine. When a service's latency spikes, the trace, the metric, the log, and the topology dependency are on the same view — not four browser tabs.
Service maps show dependencies in real time. SLO burn rate alerts fire before your error budget is gone. Synthetic checks probe external endpoints from multiple regions. When a CDN degrades in Asia, you know before your users do.
The observability market sells you pieces. A metrics tool. A log tool. A tracing tool. A SIEM. An APM. A synthetic monitor. Each has its own agent, its own query language, its own dashboard, its own pricing model, and its own silo of data that cannot talk to the others. Your team becomes an integration team — wiring tools together, correlating alerts manually, and paying six vendors for what should be one platform.
Lynceus is one Rust binary. Collection, ingestion, analysis, query, storage, dashboards, and alerting are all in the same artifact. One query language. One data model. One timeline. When a service fails, the metric, the log, the trace, and the security event are on the same view — because they were never separated.
Run Lynceus on your own servers, your own Kubernetes cluster, or your own bare metal. Your data never leaves your network. Your queries run on your hardware. Your retention policies are enforced by your policies. No vendor has access to your telemetry.
500+ detection rules out of the box — correlation rules, threshold rules, anomaly rules, ML-based rules, and causal rules that connect events across time. When a brute-force attack correlates with a successful login followed by a data exfiltration attempt, the single alert tells the full story — not three separate alerts that nobody connected.
Custom rules in LQL or SPL-compatible syntax. When your environment has a unique attack pattern, your security team writes a rule — not a ticket to a vendor to add a signature.
Every prompt, every completion, every retrieval context, every model version, every latency breakdown, and every token cost — tracked and queryable. When a user reports a bad response, you find the exact request, the exact model version, the exact retrieval context, and the exact latency — in seconds.
Guardrail decisions logged. When a content filter blocks a prompt, the block decision, the filter rule, and the prompt context are in the same log — not a separate system the ML team set up without telling security.
Cloud-only observability does not work for edge fleets. When a factory floor loses its network, a ship goes beyond satellite range, or a remote site drops its backhaul, cloud-only tools go blind. The telemetry is generated but never collected. The incident happens but nobody sees it until connectivity returns — by which point the evidence is gone.
Lynceus edge agents run offline-first. A local write-ahead log buffers telemetry on the edge device. When the network is down, the agent keeps collecting, keeps analyzing, and keeps alerting locally. When connectivity returns, the buffered data syncs to the central platform — with original timestamps preserved. No gaps. No lost data. No blind spots.
Three-tier storage keeps query performance fast without breaking the budget. Hot data in Redis serves in under 5 ms — the last hour of metrics, the most recent logs, active alert state. Warm data in ClickHouse serves in under 500 ms — the last 30 days of telemetry. Cold data in S3 serves in under 5 s — everything else, down to your retention policy.
Retention policies per data type. Security logs kept for 7 years. Debug logs expired after 30 days. Metrics downsampled over time. You control the cost without losing the data that matters.
99% of queries complete under 2ms. The long tail is where the interesting problems live — the 0.1% of queries over 50ms are the ones that tell you what is broken.
Most monitoring tools average their latency and hide the tail. Lynceus shows you the full distribution so you can find the outliers before they become incidents.
One binary, four deployment modes. From dev laptop to 99.999% production. Self-hosted on your metal, managed in our cloud, bring-your-own-cloud, or hybrid edge.
Same engine. Same query language. Same dashboards. Different deployment boundaries for different security and scale postures.
Every observability vendor sells correlation. Correlate your metrics with your logs. Correlate your logs with your traces. Correlate your security alerts with your infrastructure events. The promise is that if you buy enough tools and wire them together, you will finally see the full picture.
Lynceus takes a different position. The data was never separate — your tools made it separate. When a request fails, the metric, the log, the trace, and the security event are generated by the same system at the same time. They do not need to be correlated. They need to be collected, stored, and queried together — on one platform, in one timeline, with one query language.
That is what Lynceus is. Not another tool to add to your stack. A replacement for the stack itself.
Operator relevance: An on-call engineer needs causality, affected services, evidence, and remediation in one view — not five browser tabs.