Collect once, tier by value, keep data ready for detection, compliance, and AI
Cloud, Kubernetes, and AI workloads generate more telemetry every quarter. A vendor-neutral control layer in front of your stack lets you collect once, normalize, and route data by value so it stays ready for detection, compliance, and AI without paying full-fidelity price for every event.
Why does telemetry volume outpace your architecture?
Telemetry grows faster than the platforms built to hold it. Cloud services emit verbose logs by default, Kubernetes adds metrics at every layer, and AI workloads produce training logs, inference traces, and model-drift metrics that older collection tiers were never designed for.
A pipeline sized for 50 GB per day behaves differently at 250 GB per day. Costs climb on a per-GB curve, indexers fall behind, and teams start dropping sources to stay inside budget rather than because the data has no value.
The fix is not more data or less data. It is deciding what each event is worth and routing it to the right tier, so detection and compliance stay intact while low-value noise stops driving the bill.
40-70%
Typical ingest reduction once telemetry is tiered by value
30-50%
Duplicate events commonly found across collection tiers
60-70%
Share of firewall volume that is allow logs (industry benchmark)
How do you control telemetry growth without losing data?
You put a vendor-neutral control layer between your sources and your platforms, then route data by value. High-value events go to your SIEM or APM at full fidelity, audit copies go to low-cost object storage, and noise is filtered or aggregated before it is ever billed. Cribl is one platform that builds this layer, alongside the destinations it feeds: Splunk, Microsoft Sentinel, and Datadog.
The Medallion idea applies to operational data the same way it applies to analytics. Raw telemetry lands once, gets normalized into a clean schema, and is then served in the shape each consumer needs, whether that is a detection engine, a compliance archive, or an AI model.
- Collect once at the edge across endpoints, containers, Kubernetes, and serverless, then route to many destinations from a single control layer.
- Normalize fields and schemas so the same event reads consistently in Splunk, Sentinel, and Datadog.
- Tier by value: full fidelity to the SIEM, aggregated metrics to the APM, full-fidelity copies to object storage for replay and audit.
- Process AI and ML telemetry (training logs, inference traces, model-drift metrics) through the same governed pipeline as security and infrastructure data.
- Keep clean, normalized data ready for AI so models train and reason on structured telemetry instead of raw noise.
How does Logmetry modernize your telemetry stack?
Zbigniew Gajuk, our Co-Founder and Chief Observability and Security Architect, has led telemetry modernization at Fortune 500 scale for 26+ years. We review the environment, recommend the platform mix that fits it, and build the pipeline in production at config-level depth, in phases tied to your highest-volume sources.
Frequently asked questions
Will tiering telemetry by value cause me to lose data I need?
No. A control layer keeps a full-fidelity copy in low-cost object storage while routing only high-value events to the SIEM or APM. You can replay archived data into any platform later, so detection and compliance stay intact while ingest cost drops. Your number depends on your environment.
How do you handle AI and ML telemetry?
AI and ML telemetry flows through the same governed pipeline as security and infrastructure data. Training logs, inference traces, and model-drift metrics are normalized into a clean schema, then tiered by value so models reason on structured data and your platforms are not flooded with raw noise.
What does collect-once mean for Kubernetes and cloud?
A lightweight edge collector gathers from endpoints, containers, Kubernetes, and serverless once, then a central control layer routes that data to many destinations. You stop running platform-specific forwarders in parallel and gain one place to normalize, filter, and tier every source.
What happens to the architecture when my volume doubles?
The control layer scales horizontally by adding workers, so doubling volume is a capacity and governance exercise rather than a redesign. We set performance baselines and capacity plans during implementation so growth stays predictable. Your number depends on your environment.
Ready to explore this further?
Let's discuss how this applies to your environment.
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