Mistral AI Observability

Mistral AI Observability

Overview

Set up comprehensive observability for Mistral AI integrations.

Prerequisites

• Prometheus or compatible metrics backend
• OpenTelemetry SDK installed (optional)
• Grafana or similar dashboarding tool
• AlertManager or similar alerting system

Instructions

Step 1: Define Key Metrics

Step 2: Implement Prometheus Metrics

import { Registry, Counter, Histogram, Gauge } from 'prom-client';

const registry = new Registry();

// Request counter
const requestCounter = new Counter({
  name: 'mistral_requests_total',
  help: 'Total Mistral AI API requests',
  labelNames: ['model', 'status', 'endpoint'],
  registers: [registry],
});

// Latency histogram
const requestDuration = new Histogram({
  name: 'mistral_request_duration_seconds',
  help: 'Mistral AI request duration in seconds',
  labelNames: ['model', 'endpoint'],
  buckets: [0.1, 0.25, 0.5, 1, 2.5, 5, 10],
  registers: [registry],
});

// Token counter
const tokenCounter = new Counter({
  name: 'mistral_tokens_total',
  help: 'Total tokens used',
  labelNames: ['model', 'type'], // type: input, output
  registers: [registry],
});

// Error counter
const errorCounter = new Counter({
  name: 'mistral_errors_total',
  help: 'Mistral AI errors by type',
  labelNames: ['model', 'error_type', 'status_code'],
  registers: [registry],
});

// Cost gauge (estimated)
const costCounter = new Counter({
  name: 'mistral_cost_usd_total',
  help: 'Estimated cost in USD',
  labelNames: ['model'],
  registers: [registry],
});

export { registry, requestCounter, requestDuration, tokenCounter, errorCounter, costCounter };

Step 3: Create Instrumented Client Wrapper

import Mistral from '@mistralai/​mistralai';
import {
  requestCounter,
  requestDuration,
  tokenCounter,
  errorCounter,
  costCounter,
} from './​metrics';

// Pricing per 1M tokens (update as needed)
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 },
};

export async function instrumentedChat(
  client: Mistral,
  model: string,
  messages: any[],
  options?: { temperature?: number; maxTokens?: number }
): Promise<any> {
  const timer = requestDuration.startTimer({ model, endpoint: 'chat.complete' });

  try {
    const response = await client.chat.complete({
      model,
      messages,
      ...options,
    });

    // Record success
    requestCounter.inc({ model, status: 'success', endpoint: 'chat.complete' });

    // Record tokens
    if (response.usage) {
      tokenCounter.inc({ model, type: 'input' }, response.usage.promptTokens || 0);
      tokenCounter.inc({ model, type: 'output' }, response.usage.completionTokens || 0);

      // Estimate cost
      const pricing = PRICING[model] || PRICING['mistral-small-latest'];
      const cost =
        ((response.usage.promptTokens || 0) / 1_000_000) * pricing.input +
        ((response.usage.completionTokens || 0) / 1_000_000) * pricing.output;
      costCounter.inc({ model }, cost);
    }

    return response;
  } catch (error: any) {
    // Record error
    requestCounter.inc({ model, status: 'error', endpoint: 'chat.complete' });
    errorCounter.inc({
      model,
      error_type: error.code || 'unknown',
      status_code: error.status?.toString() || 'unknown',
    });
    throw error;
  } finally {
    timer();
  }
}

Step 4: OpenTelemetry Distributed Tracing

import { trace, SpanStatusCode, Span } from '@opentelemetry/​api';

const tracer = trace.getTracer('mistral-client');

export async function tracedChat<T>(
  operationName: string,
  operation: () => Promise<T>,
  attributes?: Record<string, string>
): Promise<T> {
  return tracer.startActiveSpan(`mistral.${operationName}`, async (span: Span) => {
    if (attributes) {
      Object.entries(attributes).forEach(([key, value]) => {
        span.setAttribute(key, value);
      });
    }

    try {
      const result = await operation();

      // Add result attributes
      if ((result as any).usage) {
        span.setAttribute('mistral.input_tokens', (result as any).usage.promptTokens);
        span.setAttribute('mistral.output_tokens', (result as any).usage.completionTokens);
      }

      span.setStatus({ code: SpanStatusCode.OK });
      return result;
    } catch (error: any) {
      span.setStatus({
        code: SpanStatusCode.ERROR,
        message: error.message,
      });
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}

// Usage
const response = await tracedChat(
  'chat.complete',
  () => client.chat.complete({ model, messages }),
  { model, 'user.id': userId }
);

Step 5: Structured Logging

import pino from 'pino';

const logger = pino({
  name: 'mistral',
  level: process.env.LOG_LEVEL || 'info',
  formatters: {
    level: (label) => ({ level: label }),
  },
});

interface MistralLogContext {
  requestId: string;
  model: string;
  operation: string;
  durationMs: number;
  inputTokens?: number;
  outputTokens?: number;
  cached?: boolean;
  error?: string;
}

export function logMistralOperation(context: MistralLogContext): void {
  const { error, ...rest } = context;

  if (error) {
    logger.error({ ...rest, error }, 'Mistral operation failed');
  } else {
    logger.info(rest, 'Mistral operation completed');
  }
}

// Usage
logMistralOperation({
  requestId: 'req-123',
  model: 'mistral-small-latest',
  operation: 'chat.complete',
  durationMs: 250,
  inputTokens: 100,
  outputTokens: 50,
});

Step 6: Alert Configuration

# prometheus/​mistral_alerts.yaml
groups:
  - name: mistral_alerts
    rules:
      # High error rate
      - alert: MistralHighErrorRate
        expr: |
          rate(mistral_errors_total[5m]) /
          rate(mistral_requests_total[5m]) > 0.05
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Mistral AI error rate > 5%"
          description: "Error rate is {{ $value | humanizePercentage }}"

      # High latency
      - alert: MistralHighLatency
        expr: |
          histogram_quantile(0.95,
            rate(mistral_request_duration_seconds_bucket[5m])
          ) > 5
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Mistral AI P95 latency > 5s"

      # Rate limit approaching
      - alert: MistralRateLimitWarning
        expr: |
          rate(mistral_errors_total{error_type="rate_limit"}[5m]) > 0
        for: 2m
        labels:
          severity: warning
        annotations:
          summary: "Mistral AI rate limiting detected"

      # High cost
      - alert: MistralHighCost
        expr: |
          increase(mistral_cost_usd_total[1h]) > 10
        for: 5m
        labels:
          severity: warning
        annotations:
          summary: "Mistral AI cost > $10/​hour"

      # API unavailable
      - alert: MistralUnavailable
        expr: |
          rate(mistral_errors_total{status_code="503"}[5m]) > 0.1
        for: 5m
        labels:
          severity: critical
        annotations:
          summary: "Mistral AI service unavailable"

Step 7: Grafana Dashboard

{
  "title": "Mistral AI Monitoring",
  "panels": [
    {
      "title": "Request Rate",
      "type": "timeseries",
      "targets": [{
        "expr": "rate(mistral_requests_total[5m])",
        "legendFormat": "{{model}} - {{status}}"
      }]
    },
    {
      "title": "Latency P50/​P95/​P99",
      "type": "timeseries",
      "targets": [
        {
          "expr": "histogram_quantile(0.5, rate(mistral_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P50"
        },
        {
          "expr": "histogram_quantile(0.95, rate(mistral_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P95"
        },
        {
          "expr": "histogram_quantile(0.99, rate(mistral_request_duration_seconds_bucket[5m]))",
          "legendFormat": "P99"
        }
      ]
    },
    {
      "title": "Token Usage",
      "type": "timeseries",
      "targets": [{
        "expr": "rate(mistral_tokens_total[5m])",
        "legendFormat": "{{model}} - {{type}}"
      }]
    },
    {
      "title": "Estimated Cost ($/​hour)",
      "type": "stat",
      "targets": [{
        "expr": "increase(mistral_cost_usd_total[1h])"
      }]
    }
  ]
}

Output

• Prometheus metrics collection
• OpenTelemetry tracing
• Structured logging
• Alert rules configured

Error Handling

Examples

Metrics Endpoint (Express)

import express from 'express';
import { registry } from './​metrics';

const app = express();

app.get('/​metrics', async (req, res) => {
  res.set('Content-Type', registry.contentType);
  res.send(await registry.metrics());
});

Resources

• Prometheus Best Practices
• OpenTelemetry Documentation
• Pino Logger

Next Steps

For incident response, see mistral-incident-runbook.