Chiron correlates your M-E-L-T streams in-flight, with no store-and-query step — root cause is computed as the data moves, not reconstructed from a backend later. The same pass drops what's left to forward, cutting downstream Datadog and Splunk volume by up to 80%.
When something breaks, Chiron delivers the full causal cascade, from root cause to downstream impact, inside a single alert. Instead of searching multiple dashboards and querying multiple data stores, teams get the full picture in one go, with MTTD under 2 minutes.
Delivers root-cause insight, blast radius, and end-to-end system context in a single real-time alert.
Significantly reduces manual triage and cuts mean time to recovery from hours to minutes.
Enables continuous, complex monitoring without store-and-query dependencies, lowering infrastructure cost.
Chiron's streaming architecture enables massive in-flight telemetry correlation and consolidation: related logs for log-centric backends, or all telemetry for unified backends, are consolidated before any sampling or aggregation. This preserves diagnostic intelligence while significantly reducing what reaches the backend.
Telemetry is consolidated without discarding the information it contains. Chiron preserves the intelligence, dimensions, and relationships needed for downstream analysis.
Start with prebuilt consolidation for common open systems, then use AI-assisted setup to extend consolidation to custom application telemetry for further TCO reduction.
Reduce the telemetry footprint reaching Splunk, Datadog, New Relic, or any existing backend, without replacing what teams already rely on.
Eliminating redundant patterns and consolidating telemetry drastically reduce volume for both log-centric and unified telemetry backends while preserving full diagnostic intelligence.
Most agentic workflows spend heavily retrieving raw data and rebuilding causal context. Chiron adds a compact, live contextual data layer between raw telemetry and the agent, enabling more efficient workflows at much lower inference cost.
System state and dependencies continuously mapped from in-flight data.
Agents start with correlated signals and causal relationships, not raw telemetry and fragmented dashboards.
Validated in internal testing, with better diagnostic outcomes.
The efficient data layer reduces processing overhead and speeds up inference, enabling wider deployment of AI workflows.
Chiron correlates every trace with the rest of your MELT in-flight. So a slow span resolves straight down to the physical node behind it. No storage, no manual digging.
Processes traces 24/7 in-flight. No data storage required.
Proven in a customer pilot while retaining 24/7 trace coverage.
Maps end-to-end flow dependencies in minutes. Built for agentic and GPU-intensive workflows.
Correlates traces with full MELT in-flight, cutting MTTR from hours to minutes.
Connects the slow span to its physical cause: App & data - a slow payment gateway traced to a database vacuum job. AI & infra - rising LLM inference latency traced to GPU memory saturation and thermal throttling.
Chiron's stream-native platform powers diverse observability use cases for customers ranging from major streaming providers and high-growth cybersecurity firms to genomic life-sciences unicorns, demonstrating its versatility and impact without naming specific organizations.
Chiron combines compile-time intelligence and always-on in-flight correlation across telemetry types, applications and infrastructure to surface a single causal alert with its blast radius and root cause attached. Teams go from symptoms to diagnosis in seconds.
Learn moreChiron is an observability-first streaming platform that correlates metrics, events, logs and traces in flight, compiles a live dependency graph and exposes rich state for AI agents. Organizations can build agentic workflows that act on live context rather than querying a data lake.
Learn moreChiron's observability-first streaming combines high-cardinality metrics, events, logs and traces into a unified views of entities, akin to materialized views, reducing storage and query compute costs by up to 70% while preserving cardinality and diagnostic accuracy. Raw telemetry can still be stored in lower-cost systems, reduced through Chiron's in-stream aggregation and sampling.
Learn moreDistributed tracing delivers deep insight into dependencies and performance, but the ingest and storage costs deter many teams from turning it on. Chiron makes tracing practical by correlating every span with metrics, events and logs in flight and aggregating them before storage. By retaining only high-value span summaries and linking them to other telemetry, Chiron surfaces root causes and blast radius from traces without escalating costs. Customers who previously disabled tracing saw materially faster resolution times and a 90-95 % reduction in trace processing costs.
Learn moreUnified storage, in-memory correlation and real-time alerting combine to reduce ingest, compute and AI costs across all use cases. Customers see significant savings while maintaining or improving diagnostics and alert fidelity.
Learn moreFast to deploy, easy to adopt, and built for enterprise scale — with the integrations and compliance your team expects.
Works alongside your existing stack, with no new monitoring agents.
Use the AI agents and workflows you already have to configure Chiron, investigate issues, and act on live system context without learning another tool.
Lower storage and AI costs while reducing the time from alert to root cause.
SOC 2 Type II compliant. View our Trust Center
Streaming infrastructure + observability experience across hyperscalers, unicorns, and deep-tech startups.
Join Engineering leaders already detecting, correlating, and resolving incidents at streaming speed - with deterministic precision.