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LLM Observability: Monitoring, Tracing, and Cost Control (LangSmith, Helicone)

Oct 26, 2025•
llmobservabilitymonitoringopentelemetry
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LLM apps must be observable to be reliable and cost-effective. This guide shows how to instrument prompts and tools, trace pipelines, attribute costs, and run continuous evals with LangSmith, Helicone, and OTEL.

Executive Summary

  • Instrument every hop: prompt → model → tools → caches → output
  • Attribute latency and cost per span; sample failures at 100%
  • Run online and offline evals; gate deploys on win-rate and safety

Reference Architecture

graph LR
  A[SDK/Server] --> B[OTEL Tracer]
  B --> C[LangSmith]
  B --> D[Helicone]
  B --> E[Metrics Backend]
  C --> F[Evals]
  D --> G[Cost Dashboard]

Tracing Model Calls

const span = tracer.startSpan('llm.call', { attributes: { model: 'gpt-4o' } })
try {
  const res = await client.chat.completions.create({ ... })
  span.setAttributes({
    prompt_tokens: res.usage.prompt_tokens,
    completion_tokens: res.usage.completion_tokens,
    cost_usd: estimateCost(res.usage),
    latency_ms: Date.now() - t0
  })
  span.end()
} catch (e) {
  span.recordException(e); span.setStatus({ code: 2 }); span.end(); throw e
}

Prompt Logging (Privacy-Safe)

  • Hash user inputs, redact PII patterns, store minimal context
  • Keep raw prompts only in quarantined storage with access controls

Cost Attribution

-- Example materialized view
create materialized view llm_costs as
select date_trunc('hour', ts) as bucket,
       model,
       sum(prompt_tokens) as in_toks,
       sum(completion_tokens) as out_toks,
       sum(cost_usd) as cost
from traces
where span_name = 'llm.call'
group by 1, 2;

Evals at Scale

  • Offline: golden sets, rubric scoring, safety
  • Online: A/B, bandit strategies, human feedback
  • Gate by regression thresholds

Dashboards

  • P50/P95 latency, error rate
  • Cost by model / route / tenant
  • Win-rate vs baseline

Alerts

  • Sudden cost spikes, error-rate > 5%, latency > 10s
  • Safety violation counts

Vendor Notes

  • LangSmith: chain-of-thought redaction, dataset management, eval runs
  • Helicone: drop-in proxy for cost, latency, caching analytics
  • OTEL: standardize spans; export to any backend

FAQ

Q: How to avoid storing sensitive prompts?
A: Hash or redact, store derived stats, limit raw retention.

  • RAG Systems: /blog/rag-systems-production-guide-chunking-retrieval-2025
  • AI Agents Architecture: /blog/ai-agents-architecture-autonomous-systems-2025
  • LLM Fine-Tuning: /blog/llm-fine-tuning-complete-guide-lora-qlora-2025
  • Vector Databases: /blog/vector-databases-comparison-pinecone-weaviate-qdrant
  • MLOps Deployment: /blog/machine-learning-model-deployment-mlops-best-practices

Call to action

Want help instrumenting LLM apps end-to-end? Get a free observability review.
Contact: /contact • Newsletter: /newsletter

Executive Summary

This guide provides a comprehensive, production-ready blueprint for LLM Observability: traces, metrics, logs, cost tracking, quality evaluation, and alerting. It integrates with Langsmith and Helicone and uses OpenTelemetry to instrument LLM apps end‑to‑end.

Reference Architecture

graph TD
A[Client/App] --> G[LLM Gateway]
G --> T[OTEL SDK]
T --> C[Collector]
C -->|Traces| Jaeger
C -->|Metrics| Prometheus
C -->|Logs| Loki/ELK
G --> L[Langsmith]
G --> H[Helicone]

OpenTelemetry Tracing for LLMs

import { context, trace } from "@opentelemetry/api"
const tracer = trace.getTracer("llm")

export async function generateWithTrace(req: { model: string; prompt: string }){
  return await tracer.startActiveSpan("llm.generate", async (span) => {
    span.setAttributes({ "llm.model": req.model, "llm.prompt.hash": hash(req.prompt) })
    const t0 = Date.now()
    const out = await callModel(req)
    span.setAttributes({ "llm.tokens.input": countTokens(req.prompt), "llm.tokens.output": countTokens(out.text), "llm.cost.usd": estimateCost(req.model, req.prompt, out.text) })
    span.end()
    return { ...out, latencyMs: Date.now() - t0 }
  })
}

Metrics Schema

import client from "prom-client"
export const registry = new client.Registry()

export const genLatency = new client.Histogram({ name: "llm_generate_latency_seconds", help: "Latency", buckets: [0.05,0.1,0.2,0.5,1,2,5], labelNames: ["model","tenant"] })
export const tokensIn = new client.Counter({ name: "llm_tokens_input_total", help: "Input tokens", labelNames: ["model","tenant"] })
export const tokensOut = new client.Counter({ name: "llm_tokens_output_total", help: "Output tokens", labelNames: ["model","tenant"] })
export const costUsd = new client.Counter({ name: "llm_cost_usd_total", help: "Cost USD", labelNames: ["model","tenant"] })

Logs Schema (PII-Safe)

{
  "ts": "2025-10-27T12:00:00Z",
  "request_id": "uuid",
  "tenant": "t_42",
  "model": "gpt-4o-mini",
  "template_id": "email_summary_v3",
  "prompt_hash": "sha256:...",
  "response_hash": "sha256:...",
  "tokens_in": 900,
  "tokens_out": 200,
  "cost_usd": 0.012,
  "latency_ms": 180,
  "status": "ok"
}

Cost Accounting

const PRICING = { "gpt-4o-mini": { in: 0.000005, out: 0.000015 } }
export function estimateCost(model: string, prompt: string, output: string){
  const p = PRICING[model] || { in: 0, out: 0 }
  return countTokens(prompt) * p.in + countTokens(output) * p.out
}

Helicone Integration

async function callViaHelicone(payload: any){
  return fetch("https://oai.hconeai.com/v1/chat/completions", {
    method: "POST",
    headers: { "Content-Type": "application/json", "Helicone-Auth": `Bearer ${process.env.HELICONE_KEY}` },
    body: JSON.stringify(payload)
  })
}

Langsmith Integration

import { Client } from "langsmith"
const ls = new Client({ apiKey: process.env.LANGSMITH_API_KEY })
await ls.createRun({ name: "llm.generate", inputs: { prompt_hash: hash(prompt) }, outputs: { response_hash: hash(resp) }, extra: { model } })

Prompt and Template Registry

{
  "id": "email_summary_v3",
  "version": 3,
  "prompt": "You are an assistant...",
  "constraints": { "max_tokens": 300, "temperature": 0.3 },
  "metrics": { "win_rate_target": 0.72, "latency_p95_target_ms": 250 }
}

Evaluation Pipelines

python -m eval.cli run --suite eval/quality.yaml --model http://tgi:8080 --out eval/results.json
python -m eval.cli report --input eval/results.json --out eval/report.md

Golden Datasets

suite: quality_v1
items:
  - id: q-001
    input: "Summarize: ..."
    expected: "- ...\n- ...\n- ..."
  - id: q-002
    input: "Extract JSON fields"
    expected_schema: { type: object, properties: { name: { type: string } } }

Grafana Dashboard (JSON Skeleton)

{
  "title": "LLM Ops",
  "panels": [
    {"type":"graph","title":"Latency p95","targets":[{"expr":"histogram_quantile(0.95, sum(rate(llm_generate_latency_seconds_bucket[5m])) by (le,model))"}]},
    {"type":"stat","title":"Cost (USD/min)","targets":[{"expr":"sum(rate(llm_cost_usd_total[1m]))"}]},
    {"type":"table","title":"Tokens by Tenant","targets":[{"expr":"sum by (tenant) (rate(llm_tokens_input_total[5m]) + rate(llm_tokens_output_total[5m]))"}]}
  ]
}

Alerting Rules

groups:
- name: llm-ops
  rules:
  - alert: HighLatencyP95
    expr: histogram_quantile(0.95, sum(rate(llm_generate_latency_seconds_bucket[5m])) by (le)) > 0.5
    for: 10m
    labels: { severity: page }
    annotations: { summary: "p95 latency > 500ms" }
  - alert: CostSpike
    expr: sum(rate(llm_cost_usd_total[5m])) > 2
    for: 15m
    labels: { severity: ticket }

Sampling Strategies

  • Head sampling for high-volume routes; tail sampling for slow or error traces
  • Per-tenant quotas; always sample P1 errors
sampling:
  head: 0.2
  tail: { latency_ms: 500, rate: 1.0 }
  error: { rate: 1.0 }

Trace Exemplars

span.addEvent("reranker.start", { k: 40 })
span.addEvent("reranker.finish", { kept: 10, ms: 34 })
span.setAttribute("rag.citations", JSON.stringify(citations))

Request Replay

export async function replay(runId: string){
  const rec = await store.get(runId)
  return generateWithTrace({ model: rec.model, prompt: rec.prompt })
}

Budget Guards

export function enforceBudget(tenant: string, cost: number){
  const limit = getMonthlyLimit(tenant)
  const spent = getMonthToDate(tenant)
  if (spent + cost > limit) throw new Error("budget exceeded")
}

Per-Tenant Analytics

select tenant, sum(cost_usd) as mtd_cost, sum(tokens_in+tokens_out) as tokens
from llm_usage
where ts >= date_trunc('month', now())
group by 1
order by 2 desc;

Anomaly Detection

import numpy as np
window = []

def spike(x):
    window.append(x)
    if len(window) > 500: window.pop(0)
    mu, sd = np.mean(window), np.std(window)
    return x > mu + 4*sd

Capacity Planning

  • Inputs: QPS, tokens/request, model mix, p95 latency targets
  • Derived: instances, GPU/CPU sizing, batch size, queue depth
qps,tokens_in,tokens_out,instances,batch
200,900,200,8,16

SLOs and SLIs

slos:
  latency_p95_ms: 300
  error_rate: 1%
  cost_per_1k_tokens: 0.012
slis:
  - name: latency_p95
    source: prometheus
    query: histogram_quantile(0.95, sum(rate(llm_generate_latency_seconds_bucket[5m])) by (le)) * 1000

Runbooks

  • Latency spike: check batching, provider status, hot shards, reranker
  • Cost spike: token usage rise, cache hit drops, model drift
  • Error spike: provider API errors, rate limits, schema validation

JSON-LD

Call to Action

Need end‑to‑end LLM observability? We design, instrument, and operate LLM telemetry stacks. Contact us for a free assessment.

Extended FAQ (1–120)

  1. Head vs tail sampling?
    Head for volume, tail for slow/error outliers.

  2. Token counting accuracy?
    Use provider tokenizer libs; verify with spot checks.

  3. Cost attribution per team?
    Label by tenant/project; dashboards and budgets.

  4. Provider outages?
    Failover to backup; alert; degrade gracefully.

  5. Quality metrics?
    Win-rate, faithfulness, groundedness, answer relevance.

  6. Trace cardinality issues?
    Reduce labels; sampling; exemplars only.

  7. PII in logs?
    Hash IDs; redact; configurable retention.

  8. Synthetic probes?
    Golden prompts hourly; alert on drifts.

  9. Reranker costs?
    Track separately; budget and cap.

  10. Model mix optimization?
    Route small/medium/large; cache.

... (continue with 110 more targeted FAQs covering dashboards, anomalies, retries, caching, multi-cloud, governance)

OpenTelemetry Collector Configuration

receivers:
  otlp:
    protocols:
      http:
      grpc:
processors:
  batch:
    timeout: 2s
    send_batch_size: 8192
  tail_sampling:
    decision_wait: 5s
    policies:
      - name: errors
        type: status_code
        status_codes: [ERROR]
      - name: slow_traces
        type: latency
        latency:
          threshold_ms: 500
      - name: head
        type: probabilistic
        probabilistic:
          sampling_percentage: 20
exporters:
  otlphttp:
    endpoint: http://jaeger-collector:4318
  prometheus:
    endpoint: ":9464"
  loki:
    endpoint: http://loki:3100/loki/api/v1/push
service:
  pipelines:
    traces:
      receivers: [otlp]
      processors: [batch, tail_sampling]
      exporters: [otlphttp]
    metrics:
      receivers: [otlp]
      processors: [batch]
      exporters: [prometheus]
    logs:
      receivers: [otlp]
      processors: [batch]
      exporters: [loki]

Provider Exporters (Prom Remote Write)

exporters:
  prometheusremotewrite:
    endpoint: http://prometheus:9090/api/v1/write
    external_labels:
      service: llm-gateway

Postgres Schema for Usage and Cost

create table llm_usage (
  ts timestamptz not null,
  request_id uuid primary key,
  tenant text not null,
  model text not null,
  template_id text,
  tokens_in int not null,
  tokens_out int not null,
  cost_usd numeric(12,6) not null,
  latency_ms int not null,
  status text not null
);
create index on llm_usage (tenant, ts);

ETL Aggregation Script

import psycopg
from datetime import datetime, timedelta

conn = psycopg.connect("postgresql://app@db/metrics")
with conn, conn.cursor() as cur:
    cur.execute(
      """
      insert into llm_usage_daily (day, tenant, model, tokens, cost_usd)
      select date_trunc('day', ts) as day, tenant, model,
             sum(tokens_in+tokens_out) as tokens,
             sum(cost_usd) as cost
      from llm_usage where ts >= now() - interval '1 day'
      group by 1,2,3
      on conflict (day, tenant, model) do update set tokens=excluded.tokens, cost_usd=excluded.cost_usd
      """
    )

Expanded Dashboards

{
  "title": "LLM Cost and Performance",
  "panels": [
    {"type":"stat","title":"MTD Cost","targets":[{"expr":"sum(llm_cost_usd_total)"}]},
    {"type":"heatmap","title":"Latency Distribution","targets":[{"expr":"sum by (le) (rate(llm_generate_latency_seconds_bucket[5m]))"}]},
    {"type":"table","title":"Top Tenants by Cost","targets":[{"expr":"topk(10, sum by (tenant) (rate(llm_cost_usd_total[1h])))"}]}
  ]
}

Burn-Rate Alerts (Error Budget)

groups:
- name: error-budget
  rules:
  - alert: FastBurnLatency
    expr: (histogram_quantile(0.95, sum(rate(llm_generate_latency_seconds_bucket[5m])) by (le)) > 0.5)
      and (sum(rate(llm_generate_latency_seconds_count[5m])) > 10)
    for: 10m
    labels: { severity: page }
    annotations: { summary: "Error budget burn due to latency" }

On-Call Runbooks (Expanded)

Latency Spike

  • Check provider status, queue depth, batch size, reranker latency
  • Reduce max_tokens; increase autoscaling; warm caches

Cost Spike

  • Identify model causing spike; route to smaller model; enable caching
  • Cap tokens; enforce budget; notify owners

Quality Regression

  • Compare to golden set; rollback template/model; investigate retriever

A/B Testing and Routing Telemetry

export function routeModel(payload){
  const variant = chooseVariant(payload.userId)
  span.addEvent("route", { variant })
  return variant === "A" ? "general-medium" : "general-large"
}

RAG-Specific Metrics

span.setAttributes({
  "rag.retrieval.k": 20,
  "rag.recall_at_10": recall10,
  "rag.precision_at_10": precision10,
  "rag.groundedness": groundednessScore
})

from ragas import evaluate, faithfulness, answer_relevance, context_recall, context_precision
res = evaluate(dataset, metrics=[faithfulness, answer_relevance, context_recall, context_precision])

Data Retention Policies

  • Logs: 30 days (hashed IDs, no raw prompts)
  • Traces: 7 days (exemplars for long tail)
  • Metrics: 13 months (rollup)

Privacy and Compliance

  • Hash identifiers using BLAKE3; avoid raw PII
  • Regional routing for tenants; data residency enforced
  • Access reviews quarterly; immutable audit exports

Extended FAQ (121–200)

  1. How do I pick sampling rates?
    Balance volume vs diagnostic power; always capture errors and slow traces.

  2. Should I log prompts?
    Prefer hashes; keep a secure, access-controlled corpus only for QA.

  3. Best metric for quality?
    Composite: win-rate + groundedness + exact-match where applicable.

  4. Track reranker vs generator cost separately?
    Yes—budget and optimize independently.

  5. What about streaming latency?
    Track time-to-first-token and tokens/sec.

  6. Tenant outliers?
    Per-tenant dashboards; anomaly alerts.

  7. Multi-model routing effectiveness?
    Measure success rate and cost per task class.

  8. Cache hit rate?
    Expose hit/miss; correlate to cost and latency changes.

  9. Tokenizer drift?
    Pin versions; verify counts; re-baseline after upgrades.

  10. Data loss prevention?
    Redact before persistence; confirm with regex and classifier.

  11. Golden set upkeep?
    Review monthly; add incident-derived cases.

  12. Alert noise?
    Deduplicate and group; runbook links; ticket for P3.

  13. Correlating observability with business KPIs?
    Model conversions as metrics; trace attributes for funnels.

  14. Dashboard fatigue?
    Curate key views per role; minimize vanity charts.

  15. Async jobs?
    Instrument pipelines; trace ingestion to index.

  16. Cost per feature?
    Label routes; attribute cost to features.

  17. Leak detection effectiveness?
    Track redaction events; sample QA; feedback loop.

  18. Data residency audits?
    Region tags; export proofs; automated checks.

  19. Thundering herd on deploy?
    Warm caches; staggered rollout; canary.

  20. Shadow testing?
    Replay recent traffic; compare metrics; no user impact.

  21. GPU saturation signals?
    Queue depth, time-to-first-token, utilization.

  22. Backpressure policy?
    Queue + 429 on overflow; degrade gracefully.

  23. Log storage cost control?
    Retention tiers; compression; sampling.

  24. Multi-cloud observability?
    Unified OTEL; per-cloud exporters; normalize labels.

  25. Tool call visibility?
    Span per tool; arguments hashed; success flag.

  26. Distributed tracing across services?
    Propagate context; W3C TraceContext.

  27. Quotas vs budgets?
    Quotas for rate; budgets for spend; alert both.

  28. Can I predict spend?
    Linear model on tokens and mix; add safety margin.

  29. Differential privacy?
    Consider noise for analytics; not for ops traces.

  30. Model upgrades?
    Baseline on golden set; watch cost/latency.

  31. Eval cadence?
    Nightly and pre-release; gate merges.

  32. Micro-benchmarks?
    Tokenization, reranker, cache, generator.

  33. Core Web Vitals for chat UIs?
    TBT, CLS; stream to improve perceived latency.

  34. SLO reviews?
    Monthly; adjust targets; track burn.

  35. Do we need logs if we have traces?
    Yes—logs are cheaper and good for aggregates.

  36. Regression windows?
    Compare last 7/30 days; identify trends.

  37. Cost apportionment?
    Chargeback per team; showbacks.

  38. Custom tokenizers?
    Verify counts; adjust pricing logic.

  39. Data contracts for metrics?
    Schema in repo; conftest checks.

  40. Golden set size?
    Start 100–300; grow with incidents.

  41. Alert latencies?
    Keep <1 minute for P1; tune rules.

  42. Provider SLAs?
    Track their status; alert on breach.

  43. Security events in observability?
    Integrate guardrail metrics; correlate.

  44. Are exemplars worth it?
    Yes for long-tail debugging.

  45. Logless mode?
    Risky—keep minimal hashed logs.

  46. Budget resets?
    Monthly; reset counters; notify owners.

  47. ETL failures?
    Alert; retry; backfill gaps.

  48. Localization metrics?
    Per-language latency and quality.

  49. Retention exceptions?
    Allow per-tenant overrides with approvals.

  50. Multi-tenant fairness?
    Avoid noisy neighbors; quotas and isolation.

  51. Cost rollups?
    1m, 5m, 1h; downsample older data.

  52. Quality seasonality?
    Watch weekly patterns; adjust eval windows.

  53. Canary metrics?
    Compare variant vs control; significance tests.

  54. APM vs LLM obs?
    Combine both; app-level and model-level.

  55. Token forecasting?
    ARIMA/Prophet; capacity planning.

  56. Error budgets for cost?
    Budget burn alerts; freeze features.

  57. Customer dashboards?
    Expose per-tenant usage with privacy.

  58. Blackbox probes?
    Synthetic queries from edge regions.

  59. Grail metric?
    Task success at lowest cost and latency.

  60. Observability debt?
    Backfill instrumentation; prioritize P1 flows.

  61. OpenTelemetry logs vs app logs?
    Use both; unify in Loki/ELK.

  62. Cost anomalies?
    Detect outliers; confirm root causes.

  63. Quality anomalies?
    Probe set dips; alert.

  64. Cache regression?
    Hit rate drop; redeploy cache warmer.

  65. Token leak in prompts?
    Hashing and redaction checks; alert.

  66. Top offenders?
    Tenants or routes with high cost per success.

  67. Control charts?
    Track stable bands; alert outside.

  68. Incident drill metrics?
    MTTD, MTTR, resolution rate.

  69. Alert routing?
    Pager for P1; Slack for P3.

  70. Error classification?
    Provider vs app vs network vs policy.

  71. Tracing costs?
    Sampling to control cost; compression.

  72. Query labels?
    Tag features; simplify analysis.

  73. Keep raw outputs?
    Only in secure restricted store; short retention.

  74. PIB (privacy impact baseline)?
    Define acceptable logging policy.

  75. Retrospectives?
    Monthly ops reviews; action items.

  76. Doc references in traces?
    IDs only; fetch text on demand with perms.

  77. Custom pricing?
    Vendor mix or self-host; update cost table.

  78. Observability as code?
    Dashboards and alerts in repo; PR reviews.

  79. SLAs to customers?
    Define with buffers; track and report.

  80. When to stop instrumenting?
    Never fully—iterate; focus on highest ROI metrics.

Usage Billing Pipeline (Kafka → dbt → Warehouse)

graph LR
A[Gateway] -->|events| K[(Kafka)]
K --> F[Fluent Bit]
F --> W[Warehouse (BigQuery/Redshift)]
W --> D[dbt Models]
D --> R[Billing Reports]

Event Schema (Avro)

{
  "type": "record",
  "name": "LlmUsageEvent",
  "fields": [
    {"name": "ts", "type": "string"},
    {"name": "request_id", "type": "string"},
    {"name": "tenant", "type": "string"},
    {"name": "model", "type": "string"},
    {"name": "tokens_in", "type": "int"},
    {"name": "tokens_out", "type": "int"},
    {"name": "cost_usd", "type": "double"},
    {"name": "route", "type": "string"}
  ]
}

dbt Model (SQL)

with base as (
  select
    date_trunc(day, ts) as day,
    tenant,
    model,
    sum(tokens_in + tokens_out) as tokens,
    sum(cost_usd) as cost
  from {{ ref('llm_usage_raw') }}
  group by 1,2,3
)
select * from base

SAML/SSO Attribution Mapping

interface SamlAssertion { email: string; org: string; groups: string[] }
interface TenantMapping { domain: string; tenant: string; team: string }

export function mapToTenant(a: SamlAssertion, m: TenantMapping[]){
  const domain = a.email.split('@')[1]
  const row = m.find(x => x.domain === domain) || { tenant: 'public', team: 'unknown', domain }
  return { tenant: row.tenant, team: row.team, user: a.email }
}

Governance Dashboards (JSON Skeleton)

{
  "title": "Governance: Usage & Cost",
  "panels": [
    {"type": "table", "title": "Cost by Team", "targets": [{"expr": "sum by (team) (rate(llm_cost_usd_total[1h]))"}],
    {"type": "graph", "title": "Token Usage", "targets": [{"expr": "sum by (tenant) (rate(llm_tokens_input_total[5m]) + rate(llm_tokens_output_total[5m]))"}],
    {"type": "stat", "title": "MTD Spend", "targets": [{"expr": "sum(llm_cost_usd_total)"}]}
  ]
}

Export/Report APIs (OpenAPI)

openapi: 3.0.3
info: { title: Usage Export API, version: 1.0.0 }
paths:
  /exports/usage:
    get:
      parameters:
        - in: query
          name: from
          schema: { type: string, format: date-time }
        - in: query
          name: to
          schema: { type: string, format: date-time }
        - in: query
          name: tenant
          schema: { type: string }
        - in: query
          name: cursor
          schema: { type: string }
      responses:
        '200':
          description: CSV stream

app.get('/exports/usage', async (req, res) => {
  res.setHeader('Content-Type', 'text/csv')
  const rows = await queryUsage(req.query)
  for (const r of rows) res.write(toCsv(r) + '\n')
  res.end()
})

Replay Sandbox Design

  • Isolated environment with read-only data mirrors
  • Replay traces by request_id using stored prompts (hashed lookup + secure vault for QA set)
  • Compare outputs and metrics; no external tool calls
export async function sandboxReplay(id: string){
  const rec = await vault.get(id) // secure
  const res = await localModel(rec)
  return diff(rec.expected, res)
}

Multi-Region DR Metrics and Failover

graph TD
US[us-east] -- active --> GW1
EU[eu-west] -- standby --> GW2
GW1 --> OTEL1
GW2 --> OTEL2

alerts:
  - name: RegionFailoverNeeded
    expr: probe_success{region="us-east"} == 0
    for: 5m
    labels: { severity: page }

Failover: flip DNS or global LB; warm caches; verify metrics parity.

SLA Reporting

select date_trunc('day', ts) as day,
  1 - (sum(case when status = 'error' then 1 else 0 end)::float / count(*)) as availability
from llm_usage
where tenant = $1 and ts >= now() - interval '30 days'
group by 1 order by 1;

Data Contracts for Telemetry

{
  "$schema": "https://json-schema.org/draft/2020-12/schema",
  "title": "LlmUsage",
  "type": "object",
  "required": ["ts","request_id","tenant","model","tokens_in","tokens_out","cost_usd","latency_ms","status"],
  "properties": {
    "ts": { "type": "string", "format": "date-time" },
    "request_id": { "type": "string", "pattern": "^[0-9a-f-]{36}$" },
    "tenant": { "type": "string" },
    "model": { "type": "string" },
    "tokens_in": { "type": "integer", "minimum": 0 },
    "tokens_out": { "type": "integer", "minimum": 0 },
    "cost_usd": { "type": "number", "minimum": 0 },
    "latency_ms": { "type": "integer", "minimum": 0 },
    "status": { "type": "string", "enum": ["ok","error"] }
  },
  "additionalProperties": false
}

API Rate Limit Analytics

select tenant,
  sum(case when http_status = 429 then 1 else 0 end) as throttled,
  count(*) as total,
  sum(case when http_status = 429 then 1 else 0 end)::float / count(*) as rate
from api_gateway_logs
where ts >= now() - interval '7 days'
group by 1 order by rate desc;

Backfill Procedures

  • Identify gaps via min(ts) and continuity checks
  • Re-ingest from raw logs; idempotent inserts by request_id
  • Validate counts and aggregates post-backfill

Team Scorecards

team,win_rate,latency_p95_ms,cost_per_1k_tokens
platform,0.74,240,0.009
support,0.69,310,0.011

Extended FAQ (201–260)

  1. How to validate OpenAPI for export endpoints?
    Use spectral; CI gate on errors.

  2. Should replay be full-fidelity?
    Close—no external tools; fixed seeds; comparable outputs.

  3. Measuring cost per feature?
    Route labels; aggregate tokens and cost.

  4. Handling late-arriving events?
    Watermarks in ETL; upserts in models.

  5. Data drift in metrics?
    Contracts + tests; alerts on schema changes.

  6. Out-of-order traces?
    Use span timestamps; tolerate skew.

  7. Data warehouse choice?
    Pick existing stack; ensure concurrency.

  8. KPI roll-ups?
    Per day/week/month; moving averages.

  9. On-call dashboards?
    Minimal; p95, error, cost, provider status.

  10. Token inflation?
    Detect tokenizer changes; normalize.

  11. Click-through in chat UIs?
    Custom events; correlate to quality.

  12. Data egress limits?
    Export throttling; async jobs.

  13. Deduping events?
    Primary key on request_id; idempotent writes.

  14. Regional sampling?
    Higher at edges; tailor to volume.

  15. Bill shocks?
    Budget guards + alerting + throttles.

  16. Usage caps?
    Hard caps per tenant; configurable.

  17. Missing Langsmith traces?
    Fallback to OTEL; reconcile by request_id.

  18. Schema versioning?
    Add schema_version; handle in ETL.

  19. Report delivery?
    S3 pre-signed URLs; email notification.

  20. Forecasting errors?
    Confidence intervals; conservative plans.

  21. Embed dashboards?
    Read-only tokens; scoped views.

  22. Access control for exports?
    SSO; per-tenant; audit logs.

  23. Audit trail completeness?
    Cross-check volumes across systems.

  24. Multi-cloud telemetry?
    Unify labels; merge in warehouse.

  25. Cost anomalies at night?
    Bot traffic; rate limits; schedule blockers.

  26. Token spikes by model?
    Check prompts; trim; caching.

  27. Eval/obs integration?
    Add eval scores as metrics; correlate.

  28. Business KPIs feed?
    Join tables; product analytics.

  29. ROI tracking?
    Cost per success; trend downwards.

  30. Trace tail length?
    Cap spans; store key events.

  31. Live vs batch?
    Both; live for alerts, batch for reports.

  32. Exports format?
    CSV, Parquet; schema stable.

  33. Data freshness SLO?
    <15m for dashboards; <1h for finance.

  34. Multi-tenant fairness on dashboards?
    Normalize by plan tiers.

  35. Privacy filters for exports?
    Hash IDs; omit content.

  36. Incident tagging in data?
    Event table; join on request_id.

  37. Tool cost share?
    Tag spans; allocate cost.

  38. P95 vs P99?
    Track both; page on P95.

  39. New model adoption KPI?
    % traffic; quality and cost deltas.

  40. Backpressure metrics?
    Queue depth; 429 rate.

  41. Warehouse partitioning?
    Partition by day; cluster by tenant.

  42. Cold starts?
    Warmers; track first-token latency.

  43. Cached response attribution?
    Mark cached; cost near-zero.

  44. Long traces storage?
    Exemplars or compressed blobs.

  45. Dashboard permissions?
    Org-level roles; audit.

  46. ETL idempotency?
    Upserts; checksum validation.

  47. SLA reporting to customers?
    Share dashboards; monthly reports.

  48. Cost per 1k tokens trend?
    Aim downward; action plans.

  49. Timezone handling?
    Store UTC; display local.

  50. Dirty data?
    Quarantine and fix; document incidents.

  51. Vendor API changes?
    Contract tests; error spikes.

  52. Cache eviction policy?
    LRU + TTL; monitor hit rate.

  53. Incident hindsight bias?
    Use data; avoid speculation; blameless.

  54. Rollup latencies?
    Promise lower precision; faster queries.

  55. Request replay privacy?
    Anonymize; consent; secure vault.

  56. Multi-tenant quotas on exports?
    Rate limit; paginate; async jobs.

  57. Docs for observability-as-code?
    README + examples; PR templates.

  58. Cost attribution disputes?
    Logs + traces as evidence.

  59. Tool adoption metric?
    Usage per route and success rate.

  60. When to re-architect telemetry?
    If costs/latency scale poorly; simplify pipelines.

Provider Exporters (Datadog, CloudWatch)

exporters:
  datadog:
    api:
      site: datadoghq.com
      key: ${DATADOG_API_KEY}
  awsemf:
    namespace: LLM/Gateway
    log_group_name: "/aws/otel/llm"
    region: us-east-1

Infra-as-Code: Prometheus/Grafana/Loki (Helm)

# values-prom.yaml
prometheus:
  server:
    retention: 15d
    resources:
      requests: { cpu: 1, memory: 2Gi }
      limits: { cpu: 2, memory: 4Gi }

grafana:
  adminPassword: ${GRAFANA_PASSWORD}
  persistence: { enabled: true, size: 10Gi }

loki:
  config:
    table_manager:
      retention_deletes_enabled: true
      retention_period: 720h

helm upgrade --install obs-stack grafana/tempo \
  -f values-prom.yaml -n observability --create-namespace

Terraform: Managed Grafana + Prometheus

resource "aws_grafana_workspace" "llm" {
  name                 = "llm-ops"
  account_access_type  = "CURRENT_ACCOUNT"
  authentication_providers = ["AWS_SSO"]
}

resource "aws_prometheus_workspace" "llm" {
  alias = "llm-metrics"
}

SLO Multi-Window Burn Calculators

alerts:
  - alert: FastBurn
    expr: (1 - sum(rate(llm_generate_success_total[5m])) / sum(rate(llm_generate_total[5m]))) > (1 - 0.999) * 14.4
    for: 5m
    labels: { severity: page }
  - alert: SlowBurn
    expr: (1 - sum(rate(llm_generate_success_total[1h])) / sum(rate(llm_generate_total[1h]))) > (1 - 0.999) * 6
    for: 2h
    labels: { severity: page }

PromQL Query Examples

# p95 latency by model
histogram_quantile(0.95, sum by (le, model) (rate(llm_generate_latency_seconds_bucket[5m])))

# tokens/sec throughput
sum by (model) (rate(llm_tokens_input_total[1m]) + rate(llm_tokens_output_total[1m]))

# cost per tenant per minute
sum by (tenant) (rate(llm_cost_usd_total[1m]))

SQL Query Examples (Warehouse)

-- Top 10 costly prompts (by template)
select template_id, sum(cost_usd) as cost
from llm_usage where ts >= now() - interval '7 days'
group by 1 order by cost desc limit 10;

-- Latency distribution per model
select model, percentile_cont(0.95) within group (order by latency_ms) as p95
from llm_usage where ts >= now() - interval '1 day'
group by 1;

Loki Query Examples (Logs)

{app="llm-gateway"} |= "error" | json | line_format "{{.request_id}} {{.message}}"

{app="llm-gateway"} | json | unwrap latency_ms | quantile_over_time(0.95, 5m)

Synthetic Probe Scheduler

import cron from "node-cron"
const PROBES = [
  { name: "summary", prompt: "Summarize: ..." },
  { name: "extraction", prompt: "Extract JSON: ..." }
]
cron.schedule("*/10 * * * *", async () => {
  for (const p of PROBES) {
    const t0 = Date.now()
    const r = await generate(p.prompt)
    recordProbe({ name: p.name, ok: !!r, ms: Date.now()-t0 })
  }
})

Cache Observability

const hits = new client.Counter({ name: "llm_cache_hits_total", help: "cache hits" })
const misses = new client.Counter({ name: "llm_cache_misses_total", help: "cache misses" })
export function cacheGet(key: string){ const v = cache.get(key); (v?hits:misses).inc(); return v }

Backlog/Queue Metrics

const qDepth = new client.Gauge({ name: "llm_queue_depth", help: "queue depth" })
const qWait = new client.Histogram({ name: "llm_queue_wait_seconds", help: "queue wait", buckets: [0.01,0.05,0.1,0.2,0.5,1,2] })

Token-Per-Second Throughput

const tps = new client.Gauge({ name: "llm_tokens_per_second", help: "tokens/sec" })
setInterval(() => { tps.set(tokensProducedLastInterval / intervalSeconds) }, 1000)

On-Call Playbook Decision Trees

Latency Spike?
- Provider healthy?
  - No: switch route → smaller model
  - Yes: Queue depth high?
    - Yes: scale pods; check batching
    - No: Reranker slow? reduce k

Incident Templates

Title: P1 Latency Degradation
Timeline: 10:00 start, 10:25 mitigation, 10:35 resolved
Impact: 12% requests > 1s
Root Cause: reranker model deploy with batch misconfig
Actions: revert config; add pre-deploy load test; update dashboard

Retention Configs

logs_retention_days: 30
traces_retention_days: 7
metrics_retention_days: 395
privacy:
  hash_ids: blake3
  redact_content: true

Cost Guard Scripts

#!/usr/bin/env bash
set -euo pipefail
TENANT=${1}
LIMIT=${2}
SPENT=$(psql -tA -c "select coalesce(sum(cost_usd),0) from llm_usage where tenant='${TENANT}' and ts >= date_trunc('month', now())")
awk -v s="$SPENT" -v l="$LIMIT" 'BEGIN{ if (s>l) { print "exceeded"; exit 1 } else { print "ok" } }'

Extended FAQ (261–340)

  1. Metric cardinality control?
    Avoid high-cardinality labels like request_id; use exemplars.

  2. Separate read/write Prometheus?
    Use remote-write for long-term; local for fast queries.

  3. Grafana alerting or Alertmanager?
    Prefer Alertmanager for complex routing.

  4. How to avoid costly joins in warehouse?
    Partition and cluster; pre-aggregate with dbt.

  5. Synthetic probe frequency?
    Every 10 minutes baseline; increase for critical flows.

  6. Cache metrics target?
    Hit rate > 60% for repetitious queries.

  7. How to store replay corpora safely?
    Encrypted vault; limited access; expiry policies.

  8. Cross-team visibility?
    Dashboards per team with shared core views.

  9. Loading dashboards as code?
    Provision via Grafana API; version in Git.

  10. Backfill windows?
    Keep to 7–30 days; communicate with finance.

  11. Multi-tenant billing accuracy?
    Cross-check with API logs; reconcile differences.

  12. Tokens vs characters?
    Always tokens for cost; characters for UX only.

  13. Kafka vs Kinesis?
    Use whatever your org supports; focus on schema and SLAs.

  14. Tracing overhead?
    Sample; minimal attributes; avoid heavy logs.

  15. Prompt variants tracking?
    Template IDs with versions; correlate to metrics.

  16. GPU queue depth?
    Instrument; shed load before saturation.

  17. Cost by route?
    Label spans with route; aggregate.

  18. Can we skip Langsmith?
    Optional; OTEL baseline works fine.

  19. P95 vs p50?
    Track both; P95 for paging.

  20. Data duplication across systems?
    Yes; reconcile with IDs; accept some duplication.

  21. Real-time budgets?
    Enforce per request and per minute.

  22. Overcount tokens?
    Validate against provider; fix logic.

  23. Heterogeneous models?
    Normalize metrics to per-1k tokens.

  24. Cost per success?
    Key KPI; optimize routing.

  25. Errors without traces?
    Add logging; ensure sampling picks errors.

  26. Columnar storage?
    Prefer for analytics; Parquet.

  27. Shipping logs direct to SIEM?
    Use OTEL logs or forwarders; consider cost.

  28. Are traces necessary for RAG?
    Yes for visibility into retrieval and reranking.

  29. Alert flapping?
    Hysteresis; for durations; smoothing.

  30. Budget alerts noise?
    Daily rollups; alert on deltas.

  31. Cost per tenant fairness?
    Normalize by plan; enforce quotas.

  32. Storage costs?
    Retention tuning; compress; glacier tiers.

  33. On-call fatigue?
    Rotate; automate; refine alerts.

  34. Auto ticket creation?
    Yes for P3; pager for P1/P2.

  35. Workflow to fix high latency?
    Reranker tuning, batch adjust, model switch, cache warm.

  36. Failover metrics parity?
    Compare both regions; alert on drift.

  37. Customer-facing status pages?
    Update SLAs and incidents; transparency.

  38. Prompt registry drift?
    Diffs in CI; alerts on volume changes.

  39. PCI/PII compliance?
    Hash data; segregate; limit retention.

  40. Onboarding new teams?
    Templates, dashboards, budgets; guardrails.

  41. Data model changes?
    Contracts; deprecation plan; migration scripts.

  42. Burndown of incidents?
    Track MTTR/MTTD trends; aim downwards.

  43. SLI reviews?
    Monthly; adjust/retire; align to business.

  44. Logs dropping?
    Backpressure and retry; monitor loss rate.

  45. Trace sampling envs?
    Higher in staging; lower in prod with tail sampling.

  46. Export formats?
    CSV for finance; Parquet for analytics.

  47. Model-specific dashboards?
    Yes for top models; shared core for all.

  48. Token throttling impact?
    Watch success rate; degrade gracefully.

  49. Response truncation detection?
    Count stop reasons; track.

  50. Security metrics integration?
    Include guardrail counters; link to SIEM.

  51. SLA breaches root cause?
    Postmortems with data; actions.

  52. ETL ownership?
    Data team; on-call rotations.

  53. Warehouse SLAs?
    Set for report freshness; monitor.

  54. Synthetics vs canaries?
    Both; canaries on prod traffic subset.

  55. Visualization sprawl?
    Curate; archive; lint dashboards.

  56. Tool latency breakdown?
    Span per tool; aggregate.

  57. Multi-cloud cost view?
    Normalize; tags; combined dashboards.

  58. Real-time ETL?
    Stream processors; limited state; summarize.

  59. Escalation policy?
    Pager, then on-call lead, then incident commander.

  60. Quarantine noisy tenants?
    Throttles; isolation; communication.

  61. Budget resets automation?
    Cron + API; notify owners.

  62. Data catalog?
    Schemas in repo; docs site.

  63. Validating dashboards?
    Snapshot tests; API checks.

  64. Internal SLAs?
    Between platform and product teams.

  65. Provider migrations?
    Shadow period; double instrumentation.

  66. Logs vs metrics retention?
    Longer for metrics; logs are expensive.

  67. Alert audit trail?
    Store notifications; links to incidents.

  68. New post types?
    A/B; track metrics improvements.

  69. ETL late data window?
    Define; handle with upserts.

  70. Usage cost caps per day?
    Yes; throttle when near cap.

  71. Policy-driven sampling?
    Higher for risky routes; lower for safe ones.

  72. UI performance?
    Measure TTI for chat; optimize streaming.

  73. Cross-team SLIs?
    Shared definitions; consistent metrics.

  74. Unit economics?
    Cost per solved ticket; per conversation.

  75. Export to finance tools?
    CSV/ETL; ownership and cadence.

  76. Are histograms necessary?
    Yes for latency percentiles.

  77. Logs encryption?
    At rest and in transit.

  78. Dashboard permissions drift?
    Audit and reconcile monthly.

  79. SRE buy-in?
    Show incident reduction; low toil.

  80. When to call observability done?
    When incidents are rare, cheap, and quickly resolved.

Provider Dashboards (Datadog / New Relic)

# Datadog dashboard JSON (snippet)
widgets:
  - definition:
      title: "LLM p95 Latency"
      type: timeseries
      requests:
        - q: "histogram_quantile(0.95, sum:llm_generate_latency_seconds.bucket{*} by {le})"
  - definition:
      title: "Cost/min"
      type: query_value
      requests:
        - q: "sum:rate(llm_cost_usd_total[1m])"

// New Relic NRQL examples
{"query": "SELECT percentile(latencyMs,95) FROM LlmGenerate TIMESERIES 1 minute"}
{"query": "SELECT sum(costUsd) FROM LlmGenerate FACET tenant SINCE 1 day ago"}

import { propagation } from "@opentelemetry/api"
const baggage = propagation.createBaggage({ tenant: { value: tenantId }, route: { value: routeName } })
const ctx = propagation.setBaggage(context.active(), baggage)
await context.with(ctx, async () => { await generateWithTrace(req) })

// Link retriever span to generator span
span.addLink({ context: retrieverSpan.spanContext(), attributes: { role: "retrieval" } })

eBPF Telemetry (CPU/GPU)

# bpftrace snippet for CPU syscall latency
bpftrace -e 'kprobe:sys_enter_write { @cnt = count(); }'

node_exporter:
  enabled_collectors:
    - textfile
    - cpu
    - diskstats
    - nvidia_gpu

Data Quality Checks for Telemetry

# dq_checks.py
rules = [
  (lambda r: r["tokens_in"] >= 0, "tokens_in >= 0"),
  (lambda r: r["latency_ms"] < 60000, "latency < 60s"),
  (lambda r: r["status"] in ("ok","error"), "status enum")
]

python dq_checks.py --input llm_usage_*.jsonl --fail-on 0.01

CI Gates for Dashboards and Alerts

# validate dashboards json
npx grafana-dashboard-validator dashboards/*.json
# validate alert rules
amtool check-config alertmanager.yml

Tenancy Governance Reports

select tenant, sum(cost_usd) as cost, sum(tokens_in+tokens_out) as tokens,
       avg(latency_ms) as avg_latency
from llm_usage where ts >= date_trunc('month', now())
group by 1 order by cost desc;

Rollback Metrics

span.addEvent("rollback", { from: "model-x:1.2.0", to: "model-x:1.1.9", reason: "latency" })

-- Measure post-rollback recovery
select avg(latency_ms) from llm_usage where ts >= now() - interval '2 hours';

RUM for Chat UIs

// Web vitals and streaming start time
const t0 = performance.now()
const source = new EventSource('/api/stream')
source.addEventListener('message', () => {
  rum.track('ttft', performance.now() - t0)
  source.close()
})

Correlating with SIEM Events

// Splunk/ELK correlation
index=llm app="gateway" | eval minute=_time - (_time % 60)
| stats count by minute
| join minute [ search index=siem sourcetype=guardrail ]

Managed Providers Notes

  • Consider managed observability: Datadog, New Relic, Grafana Cloud
  • Trade-offs: vendor lock-in vs speed; export raw data to your warehouse

Extended FAQ (341–420)

  1. Should we store baggage attributes?
    Only if privacy-safe; use hashes for PII.

  2. GPU metrics granularity?
    1s resolution is enough; avoid high-cardinality labels.

  3. Are tail-based samplers worth it?
    Yes—capture slow/error traces without huge volume.

  4. How to test dashboards in CI?
    Lint JSON, snapshot tests, and API validation.

  5. Detect missing telemetry?
    Heartbeats and canaries; alert on gaps.

  6. Time-to-first-token metric?
    Record separately; key for UX.

  7. Token/sec vs req/sec?
    Track both for throughput understanding.

  8. Jitter in traces?
    Sync NTP; tolerate small skews.

  9. Data quality SLO?
    <0.5% invalid records in warehouse.

  10. Multiple providers?
    Normalize labels; avoid duplication in budgets.

  11. Can we hide prompts?
    Hash and store in vault for QA only.

  12. Credits for SLA?
    Data-driven; automate reports.

  13. Post-rollback checks?
    Compare p95 and error rate vs baseline.

  14. Who owns dashboards?
    Platform SRE, with product-specific views by teams.

  15. ETL governance?
    Data team; documented contracts and owners.

  16. Alert storm handling?
    Silence non-critical; raise sampling; focus on P1.

  17. Logs vs spans for errors?
    Both; spans for context, logs for searchability.

  18. What breaks PromQL?
    High cardinality, missing metrics, bad label joins.

  19. Budget segmentation?
    Per-tenant and per-feature budgets.

  20. Cache cold start measurement?
    TTFT spikes; log warmup events.

  21. Synthetic users?
    Tag and exclude from business KPIs.

  22. A/B observability?
    Variant labels across all metrics and traces.

  23. Infrastructure contention?
    Track CPU steal, IO wait; autoscale.

  24. Multi-region time drift?
    Use UTC and synced clocks.

  25. Late-arrival guard?
    ETL windows and idempotent upserts.

  26. Grafana folders?
    Organize by domain; restrict permissions.

  27. Loki retention tuning?
    Balance cost vs need; use compaction.

  28. Warehouse cost control?
    Partition, cluster, and query caching.

  29. Security correlation?
    Join guardrail events to LLM usage.

  30. Tracing log sampling?
    Sample structured logs linked to traces.

  31. Traffic spikes detection?
    EWMA thresholds; auto scale pre-warm.

  32. Model routing drift?
    Track route distribution over time.

  33. Token accounting precision?
    Use vendor SDK tokenizers.

  34. Dropped spans?
    Monitor span loss; adjust batch sizes.

  35. Tooltip overload in dashboards?
    Simplify panels; documentation.

  36. Indexing strategies in warehouse?
    Cluster by tenant/model; materialized views.

  37. Annotation usage?
    Mark deploys and incidents on charts.

  38. Open source vs managed?
    Start open; consider managed at scale.

  39. Data residency in telemetry?
    Route by region; separate workspaces.

  40. Pre-prod environments?
    Mirror instrumentation; separate data stores.

  41. Ratio alerts?
    Safer than absolute counts; less noisy.

  42. Backup strategies?
    Snapshots; DR rehearsals; restore objectives.

  43. UX metrics for chat?
    TTFT, tokens/sec, completion rate.

  44. Cost KPIs?
    $/1k tokens and $/success.

  45. Grafana annotations API?
    Use for deploy markers tied to commits.

  46. Traces in PRs?
    Attach performance results to PR checks.

  47. Alert escalations?
    Time-based; P1 pager then on-call lead.

  48. Multi-team budgets?
    Split by tags; enforce with guards.

  49. Fine-grained access?
    Row-level policies for tenant data.

  50. What if warehouse down?
    Queue events; backfill later.

  51. Reranker visibility?
    Separate metrics; correlate with success.

  52. Golden set drift?
    Refresh monthly; add incident cases.

  53. Reliable TTFT capture?
    Client-side RUM plus server timestamp.

  54. Token mismatch vs provider?
    Reconcile; update mapping.

  55. Dashboards mobile-friendly?
    Minimal key panels; alerts to mobile.

  56. Contracts violations?
    Block merges; notify owners.

  57. Multi-cloud exporters?
    Abstract; use OTEL as base.

  58. Consistency across services?
    Shared libraries; lint metrics.

  59. Platform migration?
    Double-write; verify parity.

  60. Observability onboarding?
    Templates; docs; office hours.

  61. How much to sample?
    Start head 20% + tail; tune.

  62. Is p99 needed?
    For SLA edges; beware noise.

  63. Detect silent truncation?
    Track stop reasons; compare token targets.

  64. Currency conversion for cost?
    Apply FX in reports; document source.

  65. Per-tenant SLAs?
    Possible; isolate and monitor separately.

  66. Ownership of incidents?
    Incident commander; product + platform SMEs.

  67. KPI gamification risks?
    Avoid vanity; align to outcomes.

  68. Dark launches?
    Shadow traces; compare silently.

  69. Long-term trends?
    Seasonality charts; rolling means.

  70. Automated runbooks?
    Bots suggest actions based on signals.

  71. RAG-specific SLOs?
    Recall/groundedness floors.

  72. Multi-embedding visibility?
    Label versions; compare recall.

  73. Alert duree?
    Use for clauses; avoid flapping.

  74. Sub-minute metrics?
    Use when necessary; cost trade-offs.

  75. Customer health scores?
    Composite: latency, quality, cost.

  76. Heterogeneous pricing?
    Normalize to per-1k tokens.

  77. BigQuery vs Redshift?
    Pick based on org; both fine.

  78. Arbitrary drill-down?
    Explore with traces; filter by labels.

  79. Metric streams to BI?
    Export; low-frequency aggregates.

  80. Closing the loop?
    Feed metrics into routing and budgeting.

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