Up to 70%
Lower Observability Cost
Chiron links related logs, metrics and traces as they arrive and keeps a compact, live model of your system, applying the policies you set. Your log platform indexes fewer, richer events, originals still rebuildable. Engineers and AI agents start from the model instead of raw records.
Runs inside your cluster, in front of the tools you already use.
Arrives
payments 502
payments 502
payments 502
+38 more, 40 s
checkout → payments
2.1 s
because of payments
To your log platform
One record to index, not 41. Originals rebuildable.To engineers and agents
GET /context/checkout at risk since 14:02 cause: payments
Lower Observability Cost
lower MTTR, with real-time detection
lower AI token usage, with hot context layer for Engineers & AI
Downstream, every dashboard, query and agent otherwise repeats it from raw records. AI and GPU workloads add telemetry, and AI agents read more of it, so the repeated work is where the cost grows.
A system, its dependencies, and what matters: API latency, availability, error budgets. Policies say what to consolidate, summarize or alert on.
A streaming engine built for repeated correlation across logs, metrics and traces evaluates contributing factors as telemetry passes, and keeps a compact state with its history.
Lossless keeps every original rebuildable. A rich summary keeps the operational meaning and drops the original lines. Events outside a policy pass through untouched.
policiesconsolidate · summarize · alert
What reaches your log platform
The pattern stored once, each event's own values kept. Every original can be rebuilt; some queries adapt, results stay equivalent.
One record per related activity with counts and context, built from the full stream before you sample, filter or route. Original lines not kept.
Measured on ORCA-bench
A public benchmark for AI incident investigation. The state Chiron keeps in flight stays useful at a fraction of the raw size, which is what its rich summaries rely on.
Telemetry replayed → state kept
12.5 GB27 MB
Investigation state Chiron kept, not a log-reduction figure. The raw lines still went to the store in full.
Tokens per incident trial
154Kvs1.43M
Claude Sonnet 4.6 with Chiron, against the figure the paper reports. About 9× fewer.
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.
lower MTTR, with real-time detection
Chiron consolidates telemetry in flight before it reaches Splunk and other downstream backends. Lossless consolidation preserves the same query output and source-event reconstruction, while semantic consolidation retains the diagnostic context that matters. Fewer bytes and events mean less indexing, storage, and compute.
Significantly reduce log volume while retaining the same query output and source-event reconstruction, with the user experience preserved.
Retain the same downstream query output, with lightweight expansion where needed.
Start with prebuilt consolidation for open systems, then extend to custom application telemetry with AI assistance.
Reduce what reaches Splunk, Datadog, New Relic, and other backends without replacing them.
less data indexed in Splunk
lower overall telemetry footprint
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.
Give agents compact, relevant context instead of large raw datasets, reducing token usage and inference cost. Up to 1,000× smaller context footprint in a customer evaluation.
lower AI token usage
including complex, multi-hour outages
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.
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.
lower trace storage cost
coverage, proven in a customer pilot
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 consolidates logs in flight before they reach Splunk, reducing indexed volume while preserving source-event reconstruction and downstream query behavior. In a large-scale customer POC, measured samples showed up to 55% lower data volume while retaining the existing routing, storage, and Splunk infrastructure.
Learn moreChiron 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 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.