Every major observability vendor just shipped AI monitoring. None of them gave you the complete picture.
Your LLM application returns HTTP 200 at 95ms. Every dashboard in your stack shows green. The model confidently returned the wrong answer to 23% of queries this week — and no one knows.
This failure mode — external outputs look healthy, internal state is broken — is the defining challenge of AI infrastructure in 2026. And it's not a language model problem. It's the same structural gap running through every dimension of your AI stack.
Your GPU cluster produces 500GB of telemetry daily, most of which nobody can usefully query. Your network egress logs capture AI activity your instrumentation never sees. Your cost dashboard tells you what you spent on AI. It can't tell you why. The signals that tell you your systems are up tell you almost nothing about whether they're working — or for whom.
This is the AI observability problem. The market response — more monitoring tools, more agents, more dashboards — is making it worse, not better. Each new tool adds another partial view. None of them gives you the complete picture.
Observability means something specific
The word gets used loosely. It's worth being precise.
Observability is the ability to understand the internal state of a system from its external outputs. It includes the ability to ask questions you didn't anticipate when the system was first instrumented — days, weeks, or months later, when you're debugging something you didn't know was happening.
Monitoring tells you if the system is up. Observability tells you what it's actually doing.
For traditional applications, the gap between these two is manageable. An HTTP 500 usually means something broke. Latency spikes usually point to a bottleneck. External signals map reasonably well to internal state.
For AI systems, that mapping breaks at every layer.
A language model returns HTTP 200 at 80ms while silently hallucinating. A GPU cluster runs at 40% utilization while burying 14% of your cloud compute budget in unattributed workloads. Shadow AI tools in your organization process customer data through personal accounts that never appear in a SIEM alert. None of these failures surface in external signals. All of them require understanding internal state — what the model actually did, which workload consumed which GPU capacity, which AI activity bypassed your controls.
That's AI observability. And the challenge isn't that you don't have enough tools. It's that each tool you add gives you one more partial view of a problem that requires a complete one.
LLM observability: where the gap is sharpest
LLM observability is the first-order problem — the dimension where the consequences of the gap are most immediate and most financially significant.
One LLM request is simultaneously a performance event, a cost event, a security event, a quality event, and a compliance event. Your SRE team needs latency and time-to-first-token to understand infrastructure health. Your FinOps team needs per-request token consumption and GPU utilization tagged by workload to do cost chargeback. Your security team needs prompt content, model access logs, and PII flags. Your ML engineers need full traces — prompt, completion, retrieval documents, and tool calls — to debug quality and agent failures. Your compliance team needs a multi-year queryable archive to demonstrate defensible data handling.
Standard APM was built for one team, one signal, one destination. That model breaks entirely when the same event has five consumers — each needing to understand different internal state, at different retention timelines, at different cost tiers.
The instinct is to route each team's data to the tool they already own. Route security events to the SIEM. Route traces to the eval platform. Route GPU metrics to the monitoring dashboard. That approach preserves existing workflows — but each tool still only sees what was routed to it. The SIEM has no view of what the eval platform found. The eval platform has no view of the GPU state during the inference. The FinOps dashboard has no visibility into which specific prompts drove cost spikes. Every team has a partial view of the same underlying events, and no one has the complete picture.
And then there's the forensic problem. The most important questions about LLM behavior rarely arrive when the data does. Did the model leak PII last quarter? Which prompts correlated with hallucinations after the model update? Did quality degrade after the RAG pipeline change? APM retains 7–30 days. SIEMs hold flagged subsets. Eval platforms see sampled traces. None of those tools was built for the forensic timeline these questions actually arrive on. By the time you're asking, the data either doesn't exist anymore — or it exists in one destination at a cost that makes long-term retention structurally untenable.
The same problem runs through GPU infrastructure
LLM observability is the entry point. The same architectural failure runs through GPU infrastructure — and it's getting more expensive by the month.
A 1,000-GPU cluster produces roughly 500GB of metrics daily. DCGM alone exposes over 100 hardware metrics per device per collection interval: thermal state, KV cache pressure, NVLink utilization, memory bandwidth, compute saturation. Add vLLM inference signals, Kubernetes events, and distributed traces, and a mid-sized AI deployment produces more telemetry in a day than many enterprises' full stacks do in a week.
In 2026, every major observability vendor shipped a GPU monitoring story. The premise is the same across all of them: deploy our agent, index your data, pay our per-GB rate.
This is a monitoring play. It captures external signals and routes them to one destination. It tells you the GPU is running. It doesn't tell you which team's workload is consuming it, whether that consumption is efficient, or whether the model running on it is producing accurate outputs. And it gives each team that deploys it a partial view — their slice of GPU telemetry, in their tool, disconnected from the LLM traces and shadow AI activity happening alongside it.
Five teams need GPU telemetry — SRE, FinOps, security, quality, compliance — each at different fidelity and different cadence. No incumbent GPU monitoring product unifies that view. They compete to be the destination. The more destinations you add, the more collection agents you run on every node, each demanding its own copy of the same 500GB.
There's also a spec problem compounding this. The OpenTelemetry GenAI semantic conventions — the emerging standard for LLM instrumentation — are still in Development, not Stable. Schema churn is expected for at least another 12 months across 14 LLM providers, 7 vector databases, and 8 major frameworks. Every time the upstream spec evolves, every downstream consumer built directly against it breaks simultaneously. An infrastructure layer that absorbs that volatility isn't optional. For teams running production AI workloads across multiple providers and GPU stacks, it's what makes observability durable.
Shadow AI: the dimension instrumentation can't see
There's a third dimension of the AI observability gap that every instrumentation-based approach misses entirely.
90% of enterprises claim AI visibility. 59% confirm or suspect shadow AI — employees using personal accounts, unauthorized tools, or consumer AI services that completely bypass enterprise controls (Purple Book Community, 2026). 47% of GenAI users access tools through personal accounts that never touch the enterprise network (Netskope, 2026). Shadow AI was a factor in 1 in 5 data breaches last year, adding roughly $670K to the average breach cost (IBM, 2025).
Instrumentation-only observability can't see what it hasn't instrumented. Every eval tool, every APM agent, every GPU monitoring deployment relies on code running through your instrumented application layer. If the AI activity doesn't go through that layer — and more than half of it doesn't — you have no signal.
The full AI footprint in your organization is only visible when you correlate instrumented application telemetry with network egress data: what CASB, DLP, NGFW, and proxy logs capture. OTel sees what's instrumented. Network egress sees everything. No single point tool sees both. Closing the shadow AI gap requires an architecture that ingests both sources and correlates them — not a new detection tool, not a policy change. An architectural one.
The answer is infrastructure control and a unified investigation surface
The framing of AI observability as a tool problem assumes the right combination of monitoring agents closes the gap. A better eval platform for LLM quality. A dedicated GPU monitoring product for infrastructure. A shadow AI discovery tool for security.
Each of these addresses one consumer's slice of one dimension. Each gives that consumer a partial view of what their AI systems are actually doing. None can see the data in the others. And none addresses all three dimensions — LLM applications, GPU infrastructure, shadow AI — from a single place.
The answer is two things together.
First: an infrastructure layer that governs how AI telemetry is collected, normalized, and routed. This layer needs to ingest from every source — OTel spans via OTLP, GPU metrics from Prometheus-compatible endpoints on vLLM and Triton, DCGM metrics at the cluster edge, and network egress from CASB, DLP, and NGFW. It needs to normalize across providers and schemas — one consistent view regardless of LLM provider (Bedrock, Azure OpenAI, Anthropic, Vertex) or GPU stack (DCGM, vLLM, Triton, TensorRT-LLM) — and remain stable even as the OTel GenAI spec evolves. It needs to apply policy before data crosses any boundary: redact PII substrings in-flight, enrich GPU metrics with workload and team attribution at ingest, and route data to the tools teams already use — giving enterprises the choice, control, and flexibility to send telemetry wherever they need it.
But routing data to your existing tools doesn't give you complete observability. It gives each tool a better-governed slice of the data it already owned. The SIEM still only sees the SIEM's data. The eval platform still only sees what was routed to it. Each team still has a partial view.
Second: a unified investigation surface where the complete picture is achievable. Not another destination. The place where every silo comes together — where SRE, FinOps, security, and ML engineering can all query the same underlying AI telemetry without moving data, without rehydration, and without stitching together exports from five separate tools.
Cribl Stream and Edge are the infrastructure layer: Stream ingests, normalizes, and routes AI telemetry from every source; Edge enforces redaction and normalization at on-premises GPU clusters and regulated environments before data crosses any boundary. Cribl Lake retains full-fidelity LLM and GPU telemetry at object-storage economics — every prompt, completion, retrieval document, tool call, and hardware metric.
Cribl Search is the AI observability application where complete visibility is achieved. Two engines work together: a lakehouse engine for direct ingest and near-real-time investigation of the data you want to centralize; and a federated engine that queries AI telemetry in place across siloed proprietary stores — Datadog, Splunk Software, Elastic, New Relic, S3, Azure Blob, and beyond — without moving or rehydrating data first. Every team — SRE, FinOps, security, ML engineering — investigates AI behavior, builds dashboards, and acts from one interface. Copilot translates natural language to KQL. No data engineering project. No specialist required.
One collection pass. Every consumer served. Every silo queryable. The complete picture, finally in one place.
The gap is real and widening
Only 7% of organizations have LLM observability in production. Over 80% run LLMs in their environments (Grafana Labs, 2025). GPU costs represent 14% of cloud compute spend — and 44% of Fortune 1000 companies have no coherent GPU utilization strategy. Shadow AI is a confirmed factor in 1 in 5 breaches. Gartner projects 30% of enterprise AI projects will be abandoned by 2026 due to data quality and trust failures.
The AI observability market is filling up with products. None of them give you the complete picture. None of them address all three dimensions — LLM, GPU, shadow AI — from a single unified investigation surface. And none of them can, as long as they're designed to be one destination among many rather than the place where every silo comes together.
Effective AI observability requires governing how telemetry flows across every dimension — and having one place where you can actually see all of it. Not a partial view from each team's tool of choice. The full picture, queryable on demand, on the timeline the questions actually arrive.
That's a telemetry problem. And the answer is Cribl.
Want to see what complete AI observability looks like? Talk to us.
