Perplexity Observability

Overview

Monitor Perplexity Sonar API performance, cost, and quality. Key signals unique to Perplexity: citation count per response (quality indicator), search latency variability (web search is non-deterministic), and per-model cost differences.

Key Metrics

Prerequisites

• Perplexity API integration running
• Metrics backend (Prometheus, Datadog, or custom)
• Alerting system configured

Instructions

Step 1: Instrument the Perplexity Client

import OpenAI from "openai";

interface SearchMetrics {
  model: string;
  latencyMs: number;
  status: "success" | "error";
  citationCount: number;
  totalTokens: number;
  cached: boolean;
  errorCode?: number;
}

const metrics: SearchMetrics[] = [];

async function instrumentedSearch(
  client: OpenAI,
  query: string,
  model: string = "sonar",
  cached: boolean = false
): Promise<{ response: any; metrics: SearchMetrics }> {
  const start = performance.now();
  let searchMetrics: SearchMetrics;

  try {
    const response = await client.chat.completions.create({
      model,
      messages: [{ role: "user", content: query }],
    });

    searchMetrics = {
      model,
      latencyMs: performance.now() - start,
      status: "success",
      citationCount: (response as any).citations?.length || 0,
      totalTokens: response.usage?.total_tokens || 0,
      cached,
    };

    metrics.push(searchMetrics);
    return { response, metrics: searchMetrics };
  } catch (err: any) {
    searchMetrics = {
      model,
      latencyMs: performance.now() - start,
      status: "error",
      citationCount: 0,
      totalTokens: 0,
      cached,
      errorCode: err.status,
    };

    metrics.push(searchMetrics);
    throw err;
  }
}

Step 2: Prometheus Metrics Export

// Export metrics in Prometheus format
function prometheusMetrics(): string {
  const lines: string[] = [];

  // Latency histogram
  lines.push("# HELP perplexity_latency_ms Search response latency");
  lines.push("# TYPE perplexity_latency_ms histogram");

  // Query counter
  const byModel = metrics.reduce((acc, m) => {
    const key = `${m.model}_${m.status}`;
    acc[key] = (acc[key] || 0) + 1;
    return acc;
  }, {} as Record<string, number>);

  for (const [key, count] of Object.entries(byModel)) {
    const [model, status] = key.split("_");
    lines.push(`perplexity_queries_total{model="${model}",status="${status}"} ${count}`);
  }

  // Citation gauge
  const recentCitations = metrics.slice(-100).filter((m) => m.status === "success");
  const avgCitations = recentCitations.reduce((s, m) => s + m.citationCount, 0) / Math.max(recentCitations.length, 1);
  lines.push(`perplexity_avg_citations ${avgCitations.toFixed(1)}`);

  // Token counter
  const totalTokens = metrics.reduce((s, m) => s + m.totalTokens, 0);
  lines.push(`perplexity_tokens_total ${totalTokens}`);

  return lines.join("\n");
}

Step 3: Citation Quality Scoring

function evaluateCitationQuality(citations: string[]): {
  total: number;
  authoritative: number;
  qualityScore: number;
} {
  const authoritativeTLDs = [".gov", ".edu"];
  const authoritativeDomains = ["wikipedia.org", "arxiv.org", "nature.com", "science.org"];

  let authoritative = 0;
  for (const url of citations) {
    const isAuth = authoritativeTLDs.some((tld) => url.includes(tld)) ||
                   authoritativeDomains.some((d) => url.includes(d));
    if (isAuth) authoritative++;
  }

  return {
    total: citations.length,
    authoritative,
    qualityScore: citations.length > 0 ? authoritative / citations.length : 0,
  };
}

Step 4: Cost Tracking

const COST_PER_MILLION_TOKENS: Record<string, { input: number; output: number }> = {
  "sonar":              { input: 1, output: 1 },
  "sonar-pro":          { input: 3, output: 15 },
  "sonar-reasoning-pro": { input: 3, output: 15 },
  "sonar-deep-research": { input: 2, output: 8 },
};

function estimateCost(model: string, usage: { prompt_tokens: number; completion_tokens: number }): number {
  const rates = COST_PER_MILLION_TOKENS[model] || COST_PER_MILLION_TOKENS["sonar"];
  return (usage.prompt_tokens * rates.input + usage.completion_tokens * rates.output) / 1_000_000;
}

Step 5: Alert Rules (Prometheus/Alertmanager)

groups:
  - name: perplexity
    rules:
      - alert: PerplexityHighLatency
        expr: histogram_quantile(0.95, rate(perplexity_latency_ms_bucket[5m])) > 15000
        for: 5m
        annotations:
          summary: "Perplexity P95 latency exceeds 15 seconds"

      - alert: PerplexityNoCitations
        expr: perplexity_avg_citations == 0
        for: 10m
        annotations:
          summary: "Perplexity returning responses with zero citations"

      - alert: PerplexityHighErrorRate
        expr: rate(perplexity_queries_total{status="error"}[5m]) / rate(perplexity_queries_total[5m]) > 0.05
        for: 5m
        annotations:
          summary: "Perplexity API error rate exceeds 5%"

      - alert: PerplexityCostSpike
        expr: increase(perplexity_tokens_total[1h]) > 1000000
        annotations:
          summary: "Perplexity token usage spike (>1M tokens/hour)"

Dashboard Panels

Track these metrics on your dashboard:

• Query latency by model (sonar vs sonar-pro histogram)
• Citations per response distribution
• Query volume over time (by model)
• Cost per query trend
• Error rate by status code (429 vs 500)
• Cache hit rate

Error Handling

Output

• Instrumented Perplexity client with latency/error/citation tracking
• Prometheus metrics export endpoint
• Citation quality scoring
• Cost estimation per query
• Alert rules for latency, errors, and cost

Resources

• Perplexity API Documentation
• Prometheus Documentation

Next Steps

For incident response, see perplexity-incident-runbook.