Ideogram Observability

Overview

Monitor Ideogram AI image generation for latency, credit consumption, and output quality. Key metrics include generation time (typically 5-15 seconds per image depending on model and resolution), credit cost per generation (varies by model version and quality setting), generation success rate, and prompt safety filter rejection rate.

Prerequisites

• Ideogram API account with active credits
• Metrics backend for tracking generation data
• Webhook or polling mechanism for async generation status

Instructions

Step 1: Instrument Image Generation Calls

async function trackedGeneration(prompt: string, options: any) {
  const start = performance.now();
  try {
    const result = await ideogram.generate({ image_request: { prompt, ...options } });
    const duration = performance.now() - start;
    emitHistogram('ideogram_generation_duration_ms', duration, { model: options.model || 'V_2' });
    emitCounter('ideogram_generations_total', 1, { model: options.model || 'V_2', status: 'success' });
    emitCounter('ideogram_credits_used', result.credits_consumed || 1, { model: options.model || 'V_2' });
    return result;
  } catch (err: any) {
    emitCounter('ideogram_generations_total', 1, { status: 'error', reason: err.code || 'unknown' });
    throw err;
  }
}

Step 2: Track Prompt Safety Rejections

// Monitor how often prompts are rejected by the safety filter
function handleSafetyRejection(prompt: string, reason: string) {
  emitCounter('ideogram_safety_rejections_total', 1, { reason });
  console.warn(`Prompt rejected: ${reason}`, { prompt: prompt.substring(0, 50) });
}

Step 3: Monitor Credit Balance

set -euo pipefail
# Check remaining credits and burn rate
curl -s https://api.ideogram.ai/v1/usage \
  -H "Api-Key: $IDEOGRAM_API_KEY" | \
  jq '{credits_remaining, credits_used_today, credits_used_month, daily_avg: (.credits_used_month / 30), days_remaining: (.credits_remaining / (.credits_used_month / 30 + 0.01))}'

Step 4: Set Up Alerts

groups:
  - name: ideogram
    rules:
      - alert: IdeogramGenerationSlow
        expr: histogram_quantile(0.95, rate(ideogram_generation_duration_ms_bucket[30m])) > 20000  # 20000 = configured value
        annotations: { summary: "Ideogram P95 generation time exceeds 20 seconds" }
      - alert: IdeogramCreditBurnHigh
        expr: rate(ideogram_credits_used[1h]) > 100
        annotations: { summary: "Ideogram burning >100 credits/hour" }
      - alert: IdeogramCreditsLow
        expr: ideogram_credits_remaining < 200  # HTTP 200 OK
        annotations: { summary: "Ideogram credits below 200 -- purchase more" }  # HTTP 200 OK
      - alert: IdeogramHighRejectionRate
        expr: rate(ideogram_safety_rejections_total[1h]) / rate(ideogram_generations_total[1h]) > 0.1
        annotations: { summary: "Ideogram safety rejection rate exceeds 10%" }

Step 5: Dashboard Panels

Track: generation volume by model version, latency distribution, credit consumption trend, safety rejection rate, generation success/failure ratio, and average credits per generation. Compare V_2 vs V_2_TURBO for cost-vs-speed tradeoffs.

Error Handling

Examples

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

Advanced scenario: Customize ideogram 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