Lynceus
One Rust binary that replaces your entire observability stack — metrics, logs, traces, SIEM, LLM monitoring, GPU telemetry, and edge fleets.
Lynceus
One Rust binary that replaces your entire observability stack — metrics, logs, traces, SIEM, LLM monitoring, GPU telemetry, and edge fleets.
One Binary, Not a Stack
Collection agent, ingestion pipeline, query engine, storage, dashboards, and alerting — one Rust binary. No stitching together six open-source tools and a commercial SIEM.
Ten Domains, One Timeline
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.
Deployment Sovereignty
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.
Security Observability
87 SIEM capabilities, 500+ detection rules, 50+ SOAR actions, UEBA, threat intelligence, case management, and compliance mapping — built in, not bolted on.
AI/ML Observability
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.
Edge-First
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.
Ten Observability Domains, One Platform
Most observability stacks are Frankenstein monsters — a metrics tool here, a log analyzer there, a SIEM somewhere else, an APM tool that nobody looks at, and a GPU monitor that the ML team set up without telling anyone. Each tool has its own query language, its own dashboard, its own alerting, and its own bill. When something breaks, your team spends the first 30 minutes figuring out which tool has the answer.
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.
One Binary, Not a Stack
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.
Deployment Freedom
Your observability data is some of the most sensitive data in your organization. It contains every API endpoint, every error message, every user interaction, every security event, and every infrastructure topology detail. Sending it all to a SaaS vendor is a choice — not a requirement.
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.
Security Observability
Security teams and operations teams work from the same data — but they use different tools, different query languages, and different timelines. When a security incident happens, the SOC sees the alert in their SIEM. The SRE sees the metric spike in their monitoring tool. Nobody correlates them until the post-mortem. Lynceus puts security and operations on the same platform — because the data was never separate.
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.
AI/ML Observability
AI systems are production systems — but most teams monitor them with ad-hoc scripts, custom dashboards, and manual log inspection. When an LLM starts hallucinating, when a model's accuracy drifts, when GPU costs spike, and when inference latency degrades, the team finds out from user complaints — not from their observability platform. Lynceus brings AI/ML into the same observability substrate as everything else.
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.
Edge and Offline
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.
Query Everything
LQL is one query language for every domain. Metrics, logs, traces, security events, hardware telemetry, and AI/ML data — all queryable with the same syntax. No switching between PromQL for metrics, Lucene for logs, Splunk SPL for security, and custom DSLs for everything else. One language. One mental model. One skill to learn.
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.
Query Latency Distribution
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.
Deployment Modes
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.
Use-Case Surfaces
- Infrastructure MonitoringMetrics, traces, logs, APM, SLOs, topology, alerts, containers, and process monitoring — one platform, one timeline.
- Security OperationsSIEM, UEBA, threat intelligence, SOAR, case management, and compliance mapping — built in, not bolted on.
- AI/ML ObservabilityPrompt tracking, model drift, GPU health, inference latency, token cost, and guardrail logging for production AI.
- Hardware and DatacenterCross-vendor GPU, CPU, interconnect, thermal, power, SMART, NVMe, and environmental monitoring with predictive failure.
- Edge and Video PipelinesOffline-first edge agents, CV inference monitoring, video pipeline health, and fleet management for remote sites.
- Compliance and AuditPre-mapped compliance frameworks, evidence export, immutable retention for security logs, and audit-ready reporting.
Stop Correlating. Start Seeing.
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.
Proof points
- One binary handles collection, ingestion, query, storage, dashboards, and alerting.
- One query language (LQL) covers metrics, logs, traces, security, and hardware.
- One timeline shows every domain — no manual correlation across tools.
Objections answered
- Lynceus is not a Grafana skin or a metrics-only product.
- Lynceus does not require a separate SIEM, APM, or log analyzer.
- Lynceus is not SaaS-only — self-hosted and air-gapped are first-class deployment modes.
Operator relevance: An on-call engineer needs causality, affected services, evidence, and remediation in one view — not five browser tabs.
Core Commitments
- RuntimeRust-native single binary — collection, ingestion, query, dashboards, alerting
- Agent SizeUnder 20 MiB binary, under 30 MB RAM
- StorageClickHouse OLAP for warm data, Redis for hot cache, S3 for cold retention
- Query PerformanceRedis hot under 5 ms, ClickHouse warm under 500 ms, S3 cold under 5 s
- ProtocolsOTLP gRPC 4317, OTLP HTTP 4318, Syslog, REST, Protobuf/gRPC TLS 1.3
- Query LanguageLQL — one language for metrics, logs, traces, security, and hardware
- SIEM87 capabilities, 500+ detection rules, 50+ SOAR actions, UEBA, threat intelligence
- ComplianceSOC 2, ISO 27001, NIST 800-53, HIPAA, PCI-DSS, GDPR, FedRAMP mapped
- AI/ML128 parameters, 51 metrics — prompt, completion, token, drift, GPU, cost tracking
- Hardware113 parameters, 25 protobuf messages — GPU, CPU, interconnect, thermal, SMART, NVMe
- EdgeOffline-first with local WAL, 4 resource classes (FULL/STANDARD/CONSTRAINED/MINIMAL)
- Frontend42 widgets, 34 WebSocket channels, 168 REST endpoints consumed
- Integrations200 at launch, 500 year-one target — OTLP, Syslog, Fluentd, Prometheus, Jaeger, Zipkin
- SDKs6 languages with REST and gRPC APIs
- DeploymentSelf-hosted, air-gapped, managed SaaS, edge-constrained — same binary
Platform
- RuntimeRust-native single binary
- Agent SizeUnder 20 MiB, under 30 MB RAM
- DeploymentSelf-hosted, air-gapped, managed SaaS, edge
Storage and Query
- StorageClickHouse, Redis, S3
- QueryLQL — one language for all domains
- Performance5 ms hot, 500 ms warm, 5 s cold
Security
- SIEM87 capabilities, 500+ rules, 50+ SOAR actions
- ComplianceSOC 2, ISO 27001, NIST, HIPAA, PCI-DSS, GDPR, FedRAMP
- AuthRBAC + ABAC, mTLS, API key, JWT, session