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

Issue Cause Solution

Missing token counts Streaming response not aggregated Accumulate tokens from stream chunks

Cost drift from actual bill Pricing table outdated Update PRICING map when Mistral changes rates

Alert storm on 429s Rate limit hit during burst Tune alert threshold, add request queuing

High cardinality metrics Too many label combinations Avoid per-request-id labels

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