What is AI Observability?

AI observability is the discipline of instrumenting, monitoring, and continuously improving data pipelines, models, prompts, and user-facing AI workflows. It extends classic SRE practices to cover model behavior and business outcomes, not just infrastructure health.

“You can’t manage what you can’t see. AI observability transforms opaque model behavior into measurable, improvable system performance.”

Why it matters for LLMs

• Non-determinism: LLMs can produce different answers to the same prompt; guardrails and evaluations are essential.
• Dynamic context: Prompt templates, retrieval quality, and tool-use all affect outcomes.
• Regulatory pressure: Traceability, bias, and safety controls are increasingly required.
• Cost/latency trade-offs: Token usage and response times impact margins and user satisfaction.

For foundational groundwork on operationalizing AI, see our AI-ready DevOps pipeline checklist, MLOps roadmap for US enterprises, AI model governance framework, and the enterprise AI deployment playbook.

A pragmatic framework: SLI/SLOs for AI systems

Borrowing from SRE, define service level indicators (SLIs) and objectives (SLOs) across four pillars: Input, Model, Output, and System. Tie each pillar to owners and tools.

Start with a small, auditable set of SLIs per pillar and iterate. Align them to business KPIs and compliance obligations from day one.

LLM-specific risks and the signals to watch

Safety and trust

• Prompt injection and jailbreak attempts: Track detection counts and block success rate.
• PII leakage: Measure and alert on PII in outputs; enforce redaction policies.
• Toxicity/hate speech: Establish thresholds and automated escalation workflows.

Quality and correctness

• Hallucination rate: Use groundedness checks against your knowledge base; require citation coverage for certain tasks.
• Evaluation scores: Maintain regression tests with gold datasets and scenario-based evals.
• RAG quality: Monitor retrieval recall, chunk relevance, and context window utilization.

Performance and cost

• Latency: Track P50/P95/P99 by route, model, and tool invocation.
• Cost per resolved task: Attribute tokens and API calls to user journeys and customers.
• Scale limits: Watch rate-limit errors and backoff behavior during peak traffic.

Reference architecture: instrument everything

An effective observability stack we see in the field typically includes:

• Tracing: Span for prompt build, retrieval, model call, tool-use, and post-processing; propagate request IDs end-to-end.
• Metrics: Token counts, latency, errors, and business success markers emitted as counters/gauges/histograms.
• Logs: Structured logs for prompts, responses, safety flags, and evaluator results with privacy controls.
• Evaluations: Offline and online evals at PR-time, deploy-time, and runtime with canaries.
• Governance: Policy-as-code for safety, retention, access, and sign-offs.

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30/60/90-day rollout plan

Days 0–30: Baseline and guardrails

• Define SLIs/SLOs per pillar and implement basic tracing and metrics.
• Add safety filters for PII and toxicity; block prompt injection patterns.
• Stand up an evaluation harness with at least 20–30 golden test cases.
• Enable cost attribution per route and per customer.

Days 31–60: Automated evaluations and SLO alerts

• Introduce nightly evals and pre-deploy regression gates.
• Wire SLO breaches to PagerDuty/alerts with clear runbooks.
• Launch user feedback collection (thumbs up/down plus reason codes).
• Canary new prompt templates and model versions with traffic splitting.

Days 61–90: Optimization and governance

• Optimize cost/latency via caching, request batching, and model routing.
• Codify policies (retention, access, safety) and link them to deployment checks.
• Quarterly red-teaming and bias reviews with stakeholders.
• Publish an internal AI reliability report to leadership.

Governance and compliance alignment

Connect operational metrics to risk and policy. Tie documentation and model cards to deployments, and ensure audit-ready traceability for prompts, parameters, and outputs. If you are formalizing governance, our AI model governance framework is a practical starting point.

For regulated sectors, coordinate with your security and compliance leads on data retention, vendor reviews, and incident response. Our cybersecurity services help unify these controls across your platform.

Tooling checklist

• Metrics and alerting: Prometheus monitoring overview with SLO dashboards.
• Experiment tracking: MLflow documentation (runs, models, registry).
• Evaluation frameworks: OpenAI Evals or similar for offline/online tests.
• SRE guidance: Google SRE: Monitoring Distributed Systems.
• Risk frameworks: NIST AI Risk Management Framework and EU AI Act overview.
• Foundations: Gartner glossary: MLOps.

Sample SLOs you can adopt today

Next steps

Whether you’re standardizing SLOs, hardening safety, or tuning cost/latency, we can help you move faster with fewer risks. Talk to our team to assess your current stack or start a pilot. If you’re hiring, explore technical recruitment for data and ML roles.