Set up monitoring, metrics, and alerts for Ideogram integrations. Use when implementing observability for Ideogram operations, tracking costs, or configuring alerting for generation health. Trigger with phrases like "ideogram monitoring", "ideogram metrics", "ideogram observability", "monitor ideogram", "ideogram alerts", "ideogram dashboard".
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npx tessl skill review --optimize ./plugins/saas-packs/ideogram-pack/skills/ideogram-observability/SKILL.mdMonitor Ideogram AI image generation for latency, cost, error rates, and content safety rejections. Key metrics: generation duration (5-25s depending on model), credit burn rate, safety filter rejection rate, and API availability. Ideogram's API is synchronous, so all observability is request-level instrumentation.
| Metric | Type | Labels | Alert Threshold |
|---|---|---|---|
ideogram_generation_duration_ms | Histogram | model, style, speed | P95 > 25s |
ideogram_generations_total | Counter | model, status | Error rate > 5% |
ideogram_credits_estimated | Counter | model | >$10/hour |
ideogram_safety_rejections | Counter | reason | >10% rejection rate |
ideogram_image_downloads | Counter | status | Download failures > 1% |
import { performance } from "perf_hooks";
interface GenerationMetrics {
duration: number;
model: string;
style: string;
status: "success" | "error" | "safety_rejected" | "rate_limited";
seed?: number;
resolution?: string;
}
const metricsLog: GenerationMetrics[] = [];
async function instrumentedGenerate(
prompt: string,
options: { model?: string; style_type?: string; aspect_ratio?: string } = {}
) {
const model = options.model ?? "V_2";
const style = options.style_type ?? "AUTO";
const start = performance.now();
try {
const response = await fetch("https://api.ideogram.ai/generate", {
method: "POST",
headers: {
"Api-Key": process.env.IDEOGRAM_API_KEY!,
"Content-Type": "application/json",
},
body: JSON.stringify({
image_request: { prompt, model, style_type: style, ...options, magic_prompt_option: "AUTO" },
}),
});
const duration = performance.now() - start;
if (response.status === 422) {
recordMetric({ duration, model, style, status: "safety_rejected" });
throw new Error("Safety filter rejected prompt");
}
if (response.status === 429) {
recordMetric({ duration, model, style, status: "rate_limited" });
throw new Error("Rate limited");
}
if (!response.ok) {
recordMetric({ duration, model, style, status: "error" });
throw new Error(`API error: ${response.status}`);
}
const result = await response.json();
const image = result.data[0];
recordMetric({
duration, model, style, status: "success",
seed: image.seed, resolution: image.resolution,
});
return result;
} catch (err) {
if (!metricsLog.find(m => m.duration === performance.now() - start)) {
recordMetric({ duration: performance.now() - start, model, style, status: "error" });
}
throw err;
}
}
function recordMetric(metric: GenerationMetrics) {
metricsLog.push(metric);
// Emit to your metrics backend
console.log(JSON.stringify({
event: "ideogram.generation",
...metric,
timestamp: new Date().toISOString(),
}));
}const MODEL_COST_USD: Record<string, number> = {
V_2_TURBO: 0.05, V_2: 0.08, V_2A: 0.04, V_2A_TURBO: 0.025,
};
function estimateCost(model: string, numImages: number = 1): number {
return (MODEL_COST_USD[model] ?? 0.08) * numImages;
}
function costReport(metrics: GenerationMetrics[]) {
const successful = metrics.filter(m => m.status === "success");
const totalCost = successful.reduce((sum, m) => sum + estimateCost(m.model), 0);
const byModel = Object.groupBy(successful, m => m.model);
console.log("=== Ideogram Cost Report ===");
console.log(`Total generations: ${successful.length}`);
console.log(`Estimated cost: $${totalCost.toFixed(2)}`);
for (const [model, gens] of Object.entries(byModel)) {
const cost = (gens?.length ?? 0) * (MODEL_COST_USD[model] ?? 0.08);
console.log(` ${model}: ${gens?.length ?? 0} images, ~$${cost.toFixed(2)}`);
}
}import { Counter, Histogram, register } from "prom-client";
const generationDuration = new Histogram({
name: "ideogram_generation_duration_seconds",
help: "Ideogram image generation duration",
labelNames: ["model", "style", "status"],
buckets: [2, 5, 10, 15, 20, 30, 60],
});
const generationTotal = new Counter({
name: "ideogram_generations_total",
help: "Total Ideogram generations",
labelNames: ["model", "status"],
});
const estimatedCostTotal = new Counter({
name: "ideogram_estimated_cost_usd",
help: "Estimated Ideogram API cost in USD",
labelNames: ["model"],
});
// Expose metrics endpoint
app.get("/metrics", async (req, res) => {
res.set("Content-Type", register.contentType);
res.end(await register.metrics());
});# prometheus-rules.yml
groups:
- name: ideogram
rules:
- alert: IdeogramGenerationSlow
expr: histogram_quantile(0.95, rate(ideogram_generation_duration_seconds_bucket[15m])) > 25
for: 5m
annotations:
summary: "Ideogram P95 generation time exceeds 25 seconds"
- alert: IdeogramHighErrorRate
expr: rate(ideogram_generations_total{status="error"}[10m]) / rate(ideogram_generations_total[10m]) > 0.05
for: 5m
annotations:
summary: "Ideogram error rate exceeds 5%"
- alert: IdeogramHighCostRate
expr: rate(ideogram_estimated_cost_usd[1h]) > 10
annotations:
summary: "Ideogram burning >$10/hour"
- alert: IdeogramSafetyRejectionSpike
expr: rate(ideogram_generations_total{status="safety_rejected"}[1h]) / rate(ideogram_generations_total[1h]) > 0.1
annotations:
summary: "Ideogram safety rejection rate exceeds 10%"# Grafana dashboard panels:
# 1. Generation volume: sum(rate(ideogram_generations_total[5m])) by (model)
# 2. Latency distribution: histogram_quantile(0.5, rate(ideogram_generation_duration_seconds_bucket[5m]))
# 3. Error rate: sum(rate(ideogram_generations_total{status!="success"}[5m])) / sum(rate(ideogram_generations_total[5m]))
# 4. Cost per hour: sum(rate(ideogram_estimated_cost_usd[1h]))
# 5. Safety rejections: sum(rate(ideogram_generations_total{status="safety_rejected"}[1h]))| Issue | Cause | Solution |
|---|---|---|
| Generation timeout | Complex prompt or QUALITY speed | Alert at P95 > 25s, suggest TURBO |
| 402 credit error | Credits exhausted | Alert immediately, pause batch jobs |
| High rejection rate | User prompts hitting safety filter | Review prompt patterns, add pre-screening |
| 429 sustained | Concurrency too high | Reduce queue concurrency, alert ops |
For incident response, see ideogram-incident-runbook.
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