Skip to main content Set up comprehensive observability for Ideogram integrations with metrics, traces, and alerts.
Use when implementing monitoring for Ideogram operations, setting up dashboards,
or configuring alerting for Ideogram integration health.
Trigger with phrases like "ideogram monitoring", "ideogram metrics",
"ideogram observability", "monitor ideogram", "ideogram alerts", "ideogram tracing".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill ideogram-observability ai automation claude-code devops mcp ai-agents
Ideogram Observability
Overview
Monitor 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.
Key Metrics
Metric Type Labels Alert Threshold ideogram_generation_duration_msHistogram model, style, speed P95 > 25s ideogram_generations_totalCounter model, status Error rate > 5% ideogram_credits_estimatedCounter model >$10/hour
ideogram_safety_rejections
ideogram_image_downloadsCounter status Download failures > 1%
Instructions
Step 1: Instrumented Generation Wrapper 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(),
}));
}
Step 2: Cost Estimation Metrics 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)}`);
}
}
Step 3: Prometheus Metrics (Optional) 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());
});
Step 4: Alerting Rules # 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%"
Step 5: Dashboard Panel Queries # 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]))
Error Handling 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
Output
Instrumented generation wrapper with metrics collection
Cost estimation and reporting
Prometheus metrics with alerting rules
Grafana dashboard query templates
Resources
Next Steps For incident response, see ideogram-incident-runbook.
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Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
