Measured at a Fortune 100 streaming giant, a security unicorn, a global genomics enterprise and more. Explore each proof-of-concept outcome below.
Alert storms scatter context across dozens of tools, so on-call engineers chase symptoms instead of causes. Chiron replaces that noise with a single causal signal: the affected SLA, initiating layer and downstream blast radius are attached before anyone opens a dashboard. Pilots at a major live-streaming provider and a genomic life-sciences unicorn saw an 80 %+ reduction in storm pages and time-to-context measured in seconds.
Unified paging signal - one alert with blast radius and cause.
Topology-aware context - see upstream and downstream impacts immediately.
Faster first response - engineers act on a diagnosis instead of hunting through dashboards.
Chiron's platform correlates metrics, events, logs and traces in flight, pre-compiles a live dependency graph and performs stateful, zero-shuffle correlation. This rich state powers agentic workflows: AI SRE co-pilots can access the live context map to triage incidents or even remediate automatically. In high-growth cybersecurity environments, triage time dropped from 20-30 minutes to under five, with no changes to the existing stack.
Pre-compiled RCA - causal relationships are computed continuously.
Cross-layer correlation - metrics, logs, events and traces linked across layers.
Agent-ready context - AI agents can query the live dependency graph instead of a slow data lake.
High-cardinality metrics and verbose logs inflate ingest, storage and downstream AI token costs. Chiron's observability-first streaming combines metrics, events, logs and traces into a single view of entities—akin to a materialized view—storing only high-value incident state and a configurable hot window of raw data. This entity-centric view reduces storage volume and query compute by up to 70% while preserving operational cardinality and diagnostic accuracy and enables fast cross-signal queries. Raw telemetry can still be landed in low-cost storage tiers, but its volume is reduced through in-stream aggregation and sampling; logs are further compacted by linking events that share context (such as the same session or user). Open formats (e.g., Iceberg, Parquet on S3) allow you to land this state anywhere without vendor lock-in or duplicate pipelines.
Compact causal state - store high-value incident artifacts and a short hot window of telemetry while preserving high-cardinality dimensions.
Context-aware compression - in-stream sampling and aggregation shrink traces and logs; duplicate metadata is eliminated by linking events that share context.
Open formats & no duplicate pipelines - emit the unified state to your lake in open Iceberg/Parquet formats and use the same state for alerts, agents and analytics.
Distributed 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 in customer deployment.
In-flight span aggregation - summarize spans and link them to metrics, logs and events before storing to cut ingest volume.
Cross-layer RCA - trace context is combined with other signals to surface the initiating layer and downstream blast radius.
Cost-aware tracing - high-value span summaries replace the raw trace firehose, making always‑on tracing affordable.
Chiron's unified storage, in-memory correlation and real-time alerting drastically reduce ingest, compute and storage costs across all customer environments. By collapsing noisy alerts into a single signal, teams can rationalize what they store and index in downstream tools such as Datadog or Splunk. Customers see significant savings within months while maintaining or improving diagnostics and alert fidelity.
Compact state - less data stored means lower bills.
AI-ready context - the same state powers agents and analytics.
Integrated approach - unified storage and streaming correlation work together to cut total cost.
Tell us which outcome matters most — we'll tailor a proof-of-concept to your stack.