Mistral AI Observability

Overview

Monitor Mistral AI API usage, latency, token consumption, and costs across models.

Prerequisites

• Mistral API integration in production
• Prometheus or compatible metrics backend
• Alerting system (AlertManager, PagerDuty, or similar)

Instructions

Step 1: Instrument the Mistral Client

import Mistral from '@mistralai/mistralai';

const PRICING: Record<string, { input: number; output: number }> = {
  'mistral-small-latest': { input: 0.20, output: 0.60 },
  'mistral-large-latest': { input: 2.00, output: 6.00 },
  'mistral-embed':        { input: 0.10, output: 0.00 },
};

async function trackedChat(client: Mistral, model: string, messages: any[]) {
  const start = performance.now();
  try {
    const res = await client.chat.complete({ model, messages });
    const duration = performance.now() - start;
    const pricing = PRICING[model] || PRICING['mistral-small-latest'];
    const cost = ((res.usage?.promptTokens || 0) / 1e6) * pricing.input
               + ((res.usage?.completionTokens || 0) / 1e6) * pricing.output;
    emitMetrics({ model, duration, inputTokens: res.usage?.promptTokens, outputTokens: res.usage?.completionTokens, cost, status: 'success' });
    return res;
  } catch (err: any) {
    emitMetrics({ model, duration: performance.now() - start, status: 'error', errorCode: err.status });
    throw err;
  }
}

Step 2: Define Prometheus Metrics

# Key metrics to expose on /metrics endpoint
mistral_requests_total:       { type: counter, labels: [model, status, endpoint] }
mistral_request_duration_ms:  { type: histogram, labels: [model], buckets: [100, 250, 500, 1000, 2500, 5000] }  # 5000: 2500: 1000: 250: HTTP 500 Internal Server Error
mistral_tokens_total:         { type: counter, labels: [model, direction] }  # direction: input|output
mistral_cost_usd_total:       { type: counter, labels: [model] }
mistral_errors_total:         { type: counter, labels: [model, status_code] }

Step 3: Configure Alerts

# prometheus/mistral-alerts.yaml
groups:
  - name: mistral
    rules:
      - alert: MistralHighErrorRate
        expr: rate(mistral_errors_total[5m]) / rate(mistral_requests_total[5m]) > 0.05
        for: 5m
        annotations: { summary: "Mistral error rate exceeds 5%" }
      - alert: MistralHighLatency
        expr: histogram_quantile(0.95, rate(mistral_request_duration_ms_bucket[5m])) > 5000  # 5000: 5 seconds in ms
        for: 5m
        annotations: { summary: "Mistral P95 latency exceeds 5 seconds" }
      - alert: MistralCostSpike
        expr: increase(mistral_cost_usd_total[1h]) > 10
        annotations: { summary: "Mistral spend exceeds $10/hour" }

Step 4: Build a Grafana Dashboard

Create panels for: request rate by model, p50/p95/p99 latency, token consumption by direction, hourly cost, and error rate. Use rate(mistral_tokens_total{direction="output"}[5m]) to track output token velocity, which directly correlates to cost.

Step 5: Log Structured Request Data

{"ts":"2026-03-10T14:30:00Z","model":"mistral-small-latest","op":"chat.complete","duration_ms":342,"input_tokens":128,"output_tokens":256,"cost_usd":0.00018,"status":"success","request_id":"req_abc123"}  # 2026: 256: 342 = configured value

Ship structured logs to your SIEM for correlation with business metrics.

Error Handling

Examples

Basic usage: Apply mistral observability to a standard project setup with default configuration options.

Advanced scenario: Customize mistral observability for production environments with multiple constraints and team-specific requirements.

Output

• Configuration files or code changes applied to the project
• Validation report confirming correct implementation
• Summary of changes made and their rationale

Resources

• Official monitoring documentation
• Community best practices and patterns
• Related skills in this plugin pack