Skip to main content Set up comprehensive observability for Groq integrations with metrics, traces, and alerts.
Use when implementing monitoring for Groq operations, setting up dashboards,
or configuring alerting for Groq integration health.
Trigger with phrases like "groq monitoring", "groq metrics",
"groq observability", "monitor groq", "groq alerts", "groq tracing".
npx skills add jeremylongshore/claude-code-plugins-plus-skills --skill groq-observability ai automation claude-code devops mcp ai-agents
Groq Observability
Overview
Monitor Groq LPU inference for latency, token throughput, rate limit utilization, and cost. Groq's defining advantage is speed (280-560 tok/s), so latency degradation is the highest-priority signal. The API returns rich timing metadata (queue_time, prompt_time, completion_time) and rate limit headers on every response.
Key Metrics to Track
Metric Type Source Why TTFT (time to first token) Histogram Client-side timing Groq's main value prop Tokens/second Gauge usage.completion_timeThroughput degradation Total latency Histogram
Rate limit remaining Gauge x-ratelimit-remaining-* headersPrevent 429s
Token usage Counter usage.total_tokensCost attribution
Error rate by code Counter Error handler Availability
Estimated cost Counter Tokens * model price Budget tracking
Instructions
Step 1: Instrumented Groq Client import Groq from "groq-sdk";
const groq = new Groq();
interface GroqMetrics {
model: string;
latencyMs: number;
ttftMs: number;
tokensPerSec: number;
promptTokens: number;
completionTokens: number;
totalTokens: number;
queueTimeMs: number;
estimatedCostUsd: number;
}
const PRICE_PER_1M: Record<string, { input: number; output: number }> = {
"llama-3.1-8b-instant": { input: 0.05, output: 0.08 },
"llama-3.3-70b-versatile": { input: 0.59, output: 0.79 },
"llama-3.3-70b-specdec": { input: 0.59, output: 0.99 },
"meta-llama/llama-4-scout-17b-16e-instruct": { input: 0.11, output: 0.34 },
};
async function trackedCompletion(
model: string,
messages: any[],
options?: { maxTokens?: number; temperature?: number }
): Promise<{ result: any; metrics: GroqMetrics }> {
const start = performance.now();
const result = await groq.chat.completions.create({
model,
messages,
max_tokens: options?.maxTokens ?? 1024,
temperature: options?.temperature ?? 0.7,
});
const latencyMs = performance.now() - start;
const usage = result.usage!;
const pricing = PRICE_PER_1M[model] || { input: 0.10, output: 0.10 };
const metrics: GroqMetrics = {
model,
latencyMs: Math.round(latencyMs),
ttftMs: Math.round(((usage as any).prompt_time ?? 0) * 1000),
tokensPerSec: Math.round(
usage.completion_tokens / ((usage as any).completion_time || latencyMs / 1000)
),
promptTokens: usage.prompt_tokens,
completionTokens: usage.completion_tokens,
totalTokens: usage.total_tokens,
queueTimeMs: Math.round(((usage as any).queue_time ?? 0) * 1000),
estimatedCostUsd:
(usage.prompt_tokens / 1_000_000) * pricing.input +
(usage.completion_tokens / 1_000_000) * pricing.output,
};
emitMetrics(metrics);
return { result, metrics };
}
Step 2: Prometheus Metrics import { Histogram, Counter, Gauge } from "prom-client";
const groqLatency = new Histogram({
name: "groq_latency_ms",
help: "Groq API latency in milliseconds",
labelNames: ["model"],
buckets: [50, 100, 200, 500, 1000, 2000, 5000],
});
const groqTokens = new Counter({
name: "groq_tokens_total",
help: "Total tokens processed",
labelNames: ["model", "direction"],
});
const groqThroughput = new Gauge({
name: "groq_tokens_per_second",
help: "Current tokens per second",
labelNames: ["model"],
});
const groqRateLimitRemaining = new Gauge({
name: "groq_ratelimit_remaining",
help: "Remaining rate limit quota",
labelNames: ["type"],
});
const groqCost = new Counter({
name: "groq_cost_usd",
help: "Estimated cost in USD",
labelNames: ["model"],
});
const groqErrors = new Counter({
name: "groq_errors_total",
help: "API errors by status code",
labelNames: ["model", "status_code"],
});
function emitMetrics(m: GroqMetrics) {
groqLatency.labels(m.model).observe(m.latencyMs);
groqTokens.labels(m.model, "input").inc(m.promptTokens);
groqTokens.labels(m.model, "output").inc(m.completionTokens);
groqThroughput.labels(m.model).set(m.tokensPerSec);
groqCost.labels(m.model).inc(m.estimatedCostUsd);
}
Step 3: Rate Limit Header Tracking // Parse rate limit headers from any Groq response
function trackRateLimitHeaders(headers: Record<string, string>) {
const remaining = {
requests: parseInt(headers["x-ratelimit-remaining-requests"] || "0"),
tokens: parseInt(headers["x-ratelimit-remaining-tokens"] || "0"),
};
groqRateLimitRemaining.labels("requests").set(remaining.requests);
groqRateLimitRemaining.labels("tokens").set(remaining.tokens);
return remaining;
}
Step 4: Prometheus Alert Rules # prometheus/groq-alerts.yml
groups:
- name: groq
rules:
- alert: GroqLatencyHigh
expr: histogram_quantile(0.95, rate(groq_latency_ms_bucket[5m])) > 1000
for: 2m
labels:
severity: warning
annotations:
summary: "Groq P95 latency > 1s (normally < 200ms)"
- alert: GroqRateLimitCritical
expr: groq_ratelimit_remaining{type="requests"} < 5
for: 1m
labels:
severity: critical
annotations:
summary: "Groq rate limit nearly exhausted (< 5 requests remaining)"
- alert: GroqThroughputDrop
expr: groq_tokens_per_second < 100
for: 5m
labels:
severity: warning
annotations:
summary: "Groq throughput dropped below 100 tok/s (expected 280+)"
- alert: GroqErrorRateHigh
expr: rate(groq_errors_total[5m]) > 0.05
for: 2m
labels:
severity: critical
annotations:
summary: "Groq API error rate elevated (> 5% of requests)"
- alert: GroqCostSpike
expr: increase(groq_cost_usd[1h]) > 10
labels:
severity: warning
annotations:
summary: "Groq spend exceeded $10 in the past hour"
Step 5: Structured Request Logging // Structured JSON log for each Groq request
function logGroqRequest(metrics: GroqMetrics, requestId?: string) {
const logEntry = {
ts: new Date().toISOString(),
service: "groq",
model: metrics.model,
latency_ms: metrics.latencyMs,
ttft_ms: metrics.ttftMs,
tokens_per_sec: metrics.tokensPerSec,
prompt_tokens: metrics.promptTokens,
completion_tokens: metrics.completionTokens,
queue_time_ms: metrics.queueTimeMs,
cost_usd: metrics.estimatedCostUsd.toFixed(6),
request_id: requestId,
};
// Output as structured JSON for log aggregation
console.log(JSON.stringify(logEntry));
}
Step 6: Dashboard Panels Key Grafana/dashboard panels for Groq monitoring:
TTFT Distribution (histogram) -- Groq's main value; alert if > 500msTokens/Second by Model (time series) -- should be 280-560 rangeRate Limit Utilization (gauge, 0-100%) -- alert at 90%Request Volume (counter rate) -- by modelError Rate (counter rate) -- by status code (429, 5xx)Cumulative Cost (counter) -- by model, daily/weekly/monthlyQueue Time (histogram) -- Groq-specific, should be < 50ms
Error Handling Issue Cause Solution 429 with high retry-after RPM or TPM exhausted Implement request queuing Latency spike > 2s Model overloaded or large prompt Reduce prompt size or switch to lighter model 503 Service Unavailable Groq capacity issue Enable fallback to alternative provider Tokens/sec drop Streaming disabled or large prompts Enable streaming for better perceived performance
Resources
Next Steps For incident response procedures, see groq-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).
