LLM observability: measure, trace, and govern AI in production

Shipping an LLM use case to production is now easy: an endpoint, an API key, and an application integration. Operating it is not. When quality drops, costs spike, or a leakage risk appears, you need reliable signals.

LLM observability is the discipline that makes those signals visible and actionable—just like logs, metrics, and traces for a “classic” service.

Why “classic” logs are not enough

LLMs introduce failure modes that are hard to diagnose with traditional observability:

• Non-deterministic behavior (different answers for the same prompt).
• Perceived quality (relevance, hallucinations) instead of a simple HTTP status.
• Variable costs (tokens, models, context windows).
• Security risks (prompt injection, exfiltration, sensitive data exposure).

Without dedicated instrumentation, incidents often look like: “it responds, but it’s bad.”

The key signals to instrument

A solid baseline covers four families of signals.

1) Performance and reliability

• End-to-end latency (including retrieval, tool calls, post-processing).
• Error rate by type (timeouts, rate limits, model unavailability).
• Dependency health (vector DB, tools, internal APIs).

2) Cost and capacity

• Input/output tokens, context size, cache hit rate.
• Cost per request, per feature, per tenant.
• Split by model (to enable cost/quality routing).

3) Quality (beyond “it works”)

You won’t get a perfect metric, but you can industrialize useful proxies:

• Automated evaluations on a reference set (scoring, assertions).
• User feedback (thumbs, edits/corrections).
• Hallucination detection (heuristics, source verification for RAG).
• Empty-answer or refusal rate.

4) Security and compliance

• PII/secrets detection (before and after generation).
• Audit logging of filtering/redaction decisions.
• Prompt injection signals (patterns, untrusted sources, suspicious tool calls).

A simple, scalable architecture

In most organizations, the best anchor is an AI Gateway (or orchestration layer) that centralizes:

• authentication/authorization,
• multi-model routing,
• guardrails,
• and, crucially, instrumentation.

Technically, a common stack includes:

• Distributed tracing (OpenTelemetry) to connect user request → retrieval → LLM → tools.
• Metrics (latency, tokens, errors, costs) exposed like any other service.
• Structured logs (correlation IDs, policy decisions), with systematic redaction.

The critical part: don’t log prompts or sensitive content “as-is”. Observability must be by design.

Defining SLOs that fit LLM use cases

LLM SLOs are rarely “99.9% success”. They often combine:

• Reliability: availability and latency.
• Quality: rate of answers judged useful (evaluation or feedback).
• Cost: token/cost budgets per period and per product.

The goal is not perfection—it is predictability and explicit trade-offs.

Conclusion

Production AI must be operable like a product: observed, managed, governed. By investing early in LLM observability (cost, quality, security), you turn an opaque system into an industrializable workflow.

At Demkada, we embed this approach into Platform Engineering and AI programs: centralized governance, automated guardrails, and actionable metrics to move fast without losing control.