AI observability is a telemetry problem
AI systems generate a flood of telemetry across LLM apps, GPU infrastructure, and shadow AI. Cribl gives you one telemetry layer and one investigation surface, so every team sees the same AI behavior without five separate collection projects.
The Challenge
LLMs, GPU clusters, and shadow AI are rolling out faster than your ability to monitor it. Each interaction is at once a performance, cost, security, quality, and compliance event, but telemetry is scattered across tools, priced per gigabyte, or never captured at all. Teams either overspend to keep data everywhere or fly blind when the questions finally arrive.
The Solution
With Cribl, you can collect AI telemetry once, govern it in flight, route just the right telemetry it to every team, and investigate the full picture without rehydrating or duplicating data.
Cribl enriches AI telemetry with workload, team, and environment tags and emits pre-aggregated GPU and token metrics. FinOps and engineering can see which models, agents, and features are driving spend and turn chargeback into a query instead of a multi-month data engineering project.
Cribl Search is the AI observability application that federates queries across Cribl Lake, hot stores, and observability tools like Datadog, Splunk, and Elastic. Teams correlate cost, quality, performance, and security signals for AI systems from a single investigation surface instead of stitching partial views together.
In-flight redaction masks PII, PHI, credentials, and source code in LLM telemetry before it crosses any trust boundary, while egress correlation exposes shadow AI traffic that never touched your instrumentation. Security and compliance get the full AI footprint without creating new data-handling liability.
Cribl Stream and Edge collect LLM, GPU, and shadow AI telemetry from OpenTelemetry, GPU exporters, provider APIs, and network egress, then normalize, tag, and redact it before fan-out. Platform teams run one collection pass instead of four and stay insulated from fast-moving GenAI semantic convention changes.
Use Cribl Stream and Edge as the policy layer for AI data: normalize GenAI spans and GPU metrics, redact sensitive content, enrich with business context, and route one interaction to multiple destinations at the right fidelity and cost tier.
Cribl Search gives SRE, security, FinOps, and AI engineering a shared investigation surface with lakehouse and federated engines, so they can explore LLM traces, GPU metrics, and shadow AI activity together without moving or rehydrating data.
Cribl Lake stores complete LLM, GPU, and agent telemetry at object-storage economics, so you can answer long-tail questions about hallucinations, regressions, cost spikes, and compliance months after hot retention windows expire.
By combining instrumented application telemetry with network, CASB, and DLP signals, Cribl surfaces the delta between sanctioned and unsanctioned AI usage, with routing and redaction policies that keep sensitive prompts out of untrusted backends.
Guides
The AI race is being won or lost at the infrastructure layer. While AI ambitions are soaring, many organizations are discovering that their current telemetry infrastructure wasn’t built for the scale, cost, and complexity of agentic AI.
In this HBR-AS report, discover how global leaders are navigating the shift to agentic AI.
Resources
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