AI Observability
AI Observability requires an up-to-date sensor and backend. See Installation & Updating.
Overview
AI Observability gives you full visibility into how your services use AI — models, cost, latency, prompts, agent behavior, and tool execution. All data stays in your infrastructure with BYOC (Bring Your Own Cloud); groundcover never processes your AI data outside your environment.
groundcover captures AI telemetry from two sources: eBPF auto-detection for immediate per-call visibility with zero code changes, and OpenTelemetry instrumentation for the full agent-level picture.
If your sensor is up to date and your services call a supported provider, you already have data. Open AI Observability in your console to see what's there.
For instrumentation setup, privacy controls, and troubleshooting, see Using AI Observability.
Monitoring AI coding tools (Claude Code, Claude Cowork, Codex)? Those integrations ship tool-usage telemetry (logs, plus metrics for Claude Code), not GenAI traces, and don't surface here. See AI Tools Observability for setup, dashboards, and verification queries.
Two Levels of Visibility
eBPF captures every AI API call automatically — model, tokens, cost, latency, and full prompt/response content, with zero code changes. The sensor auto-detects OpenAI, Anthropic, and AWS Bedrock traffic from anything running in your monitored environment: production services, CI/CD pipelines, development pods, staging. If a process runs on a monitored node and makes an HTTPS call to a supported provider, groundcover captures it.
For providers not yet auto-detected, use SDK instrumentation to send GenAI traces directly. Contact us if you need a specific provider — eBPF support is extended based on customer requests.
eBPF gives you the calls. To see the full picture — which agent triggered which call, how tool results shaped the next prompt, how a multi-step workflow reasons from start to finish — add OpenTelemetry instrumentation. SDK spans give you trace trees, agent workflows, tool execution chains, and conversation threading: not just what your AI costs, but how it thinks.
When both are active, the same call may appear twice — once from eBPF and once from the SDK. Both are correct; cost is not double-counted. When groundcover detects an SDK span for the same call, eBPF cost and token data is excluded from aggregations.
See Using AI Observability for setup instructions.
Cost Tracking
groundcover calculates cost per AI span at ingestion time using a maintained model pricing table that updates as providers release new models. Each span includes input tokens, output tokens, and cached tokens.
By default, every AI span is stored — no sampling, no dropping. GenAI calls are high-value and low-volume compared to typical service traffic. Every call matters for cost analysis and debugging. If you need to disable GenAI storage entirely, see Privacy Controls.
Cost and token data is available for filtering and sorting in the span list — find your most expensive calls by model, service, or agent. See Example Queries for cost analysis patterns.
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