Advancing AI Observability: From Metrics to Meaningful Insights

27th Jun 2025
6 min read
Tags:
AI,
Observability,
LLM

Earlier I shared a Production Readiness Checklist outlining the essential steps before rolling an AI system into production. One key section focused on observability and tracing. This post expands on that foundation, adding hands‑on examples, code snippets, and real‑world war stories so you can move from aspirational slides to shipping dashboards.

    flowchart LR
Users --> AI-Application --> Observability

Why Observability Matters

Modern AI applications — from Retrieval‑Augmented Generation (RAG) pipelines to fine‑tuned LLMs — are complex socio‑technical systems. Standard application metrics alone aren’t enough when a slight model‑drift or data‑quality issue can torpedo business outcomes.

Headline stats worth sharing with the board
• 30 % of generative‑AI pilots will be abandoned after proof‑of‑concept by 2025 because of poor data, inadequate risk controls, or unclear value ^[1]
• 88 % of enterprises say a single hour of downtime now exceeds $300 k — and 34 % report $1–5 million per hour ^[2]
• Large organisations already spend a median $1.4 million per year on AIOps & observability tooling ^[3]
• Early adopters are realising $3.7 in value for every $1 invested in generative‑AI programmes ^[4]

Real‑World Failure Case

Zillow Offers famously wrote down $569 million and shuttered its home‑flipping arm after its price‑prediction model drifted in the frothy 2021 housing market ^[5]. Had Zillow continuously monitored prediction error against market movement and flagged drift early, it could have throttled purchases before the losses snowballed. Share this cautionary tale whenever someone claims “it’s just a proof of concept.”

    flowchart LR
Production --> Observation --> Business-KPI

Pillars of AI Observability

1. Instrumentation (Deep Dive)

Instrumentation is the groundwork. Capture the right signals from your application code, model‑serving layers, and data pipelines at the point they happen, not in an after‑the‑fact batch.

# FastAPI + LangChain example with OpenTelemetry
from fastapi import FastAPI
from langchain.chains import RetrievalQA
from opentelemetry import trace, metrics
from opentelemetry.instrumentation.fastapi import FastAPIInstrumentor
from opentelemetry.instrumentation.requests import RequestsInstrumentor
from opentelemetry.instrumentation.langchain import LangChainInstrumentor

provider = trace.get_tracer_provider()
FastAPIInstrumentor().instrument_app(app := FastAPI())
RequestsInstrumentor().instrument()
LangChainInstrumentor().instrument()

tracer = trace.get_tracer(__name__)

@app.post("/ask")
async def ask(q: str):
    with tracer.start_as_current_span("rag_query") as span:
        chain = RetrievalQA.from_chain_type("gpt-4o")
        answer = chain.run(q)
        span.set_attribute("rag.tokens", answer["token_usage"])
        return answer

The snippet auto‑instruments FastAPI, Requests, and LangChain using OpenTelemetry so every RAG span, latency, and token count lands in your tracing backend in <5 lines of code ^[6]. Enable log/trace correlation with a single env‑var (OTEL_PYTHON_LOG_CORRELATION=true) so JSON logs include the active trace‑ID ^[7].

Tip Create a lightweight observability internal package that wraps the boilerplate so teams can add a single import and two lines of config.

Example Structured Log (truncated)

{
  "timestamp": "2025‑06‑27T17:04:13.217Z",
  "trace_id": "5f1c…",
  "span_id": "6b3f…",
  "route": "/ask",
  "user_id": "42",
  "query": "What are AIOps best practices?",
  "retrieved_chunks": 5,
  "model": "gpt‑4o‑2025‑06‑20",
  "latency_ms": 412,
  "tokens_prompt": 77,
  "tokens_completion": 121,
  "cost_usd": 0.014
}

2. Monitoring and Dashboards (Deep Dive)

Metrics are only useful when actionable. Build dashboards that combine system health with AI‑specific indicators.

Layer	Key Metrics	Example PromQL	Alert Threshold
API & Gateway	`http_request_duration_seconds{route="/ask"}` (p95)	`histogram_quantile(0.95, rate(http_request_duration_seconds_bucket[5m]))`	>2 s for 5 m
Model Serving	`rag_tokens_total` by `model` & `customer`	`sum by(model)(rate(rag_tokens_total[1h]))`	20 % ↑ from 30‑day baseline
Retrieval Store	`vector_query_latency_ms` p99	`histogram_quantile(0.99, rate(vector_query_latency_ms_bucket[5m]))`	>300 ms
Cost	`rag_cost_usd_total`	`increase(rag_cost_usd_total[1d])`	>$1k/day budget

Grafana Cloud ships a plug‑and‑play OpenAI usage dashboard that graphs prompt vs. completion tokens, cost, and latency by model ^[8]. Pair it with Azure OpenAI’s built‑in “Tokens‑Based Usage” workbook if you are on Microsoft’s cloud ^[9].

3. Closing the Loop with Evaluation

In The Hidden Cost of LLM‑as‑a‑Judge we discussed smart approaches to LLM evaluation. Observability ties into that by storing evaluation results alongside live metrics.

# arize‑evals.yaml – evaluate 5 % of prod traffic nightly
project: ai‑assistant
source: prod‑llm‑traces
sample_rate: 0.05
checks:
  - name: factuality
    type: rouge
    threshold: 0.25
  - name: toxicity
    type: perspective
    threshold: 0.10
sink: arize‑cloud
schedule: "0 2 * * *"

Arize’s online evaluation feature attaches these scores directly to each trace so you can slice dashboards by factuality, toxicity, or “hallucination probability” in real time ^[10].

For open‑source stacks, Evidently AI 0.4+ now supports LLM testing suites with >100 metrics, drift reports, and a self‑hosted monitoring UI ^[11].

    flowchart LR
Metrics --> Evaluation
Evaluation -->|compare| Newer
Newer --> Retrain

Putting It All Together

Plan instrumentation early. Decide which traces, metrics, and logs map to business KPIs. Bake observability into your Definition of Done and Terraform modules.
Automate monitoring. Dashboards and alerts should deploy with every model push via CI/CD so you never ship blind code.
Review & retrain. Use evaluation dashboards in weekly triage and quarterly OKR reviews. Promote only models whose eval scores beat the current champion by ≥ X % on guarded benchmarks.
Optimise cost. Track tokens * price_per_token for every request. Kill features that deliver low ROI. Auto‑scale GPU pods based on real‑time throughput.

AI observability is a living discipline. By moving beyond simple metrics and focusing on actionable insights, you ensure your models stay reliable and aligned with business goals—and you build the confidence executives need to keep funding your roadmap.

    flowchart LR
Plan --> Automate --> Review --> Improve

Production Readiness Checklist – Deployment best practices
LLMOps: Introduction – Where observability fits into the LLM lifecycle
The Hidden Cost of LLM‑as‑a‑Judge – Smarter evaluation strategies

Need expert support? Contact me to discuss how tailored observability practices can accelerate your AI roadmap.