Kling AI Usage Analytics

Kling AI Usage Analytics

Overview

Track video generation usage with structured logging, aggregate metrics, daily reports, and cost analysis. Built on JSONL event logs that can feed into any analytics platform.

Event Logger

import json
import time
from datetime import datetime
from pathlib import Path

class KlingEventLogger:
    """Append-only JSONL event log for Kling AI operations."""

    def __init__(self, log_dir: str = "logs"):
        self.log_dir = Path(log_dir)
        self.log_dir.mkdir(exist_ok=True)

    def _write(self, event: dict):
        date = datetime.utcnow().strftime("%Y-%m-%d")
        filepath = self.log_dir / f"kling-{date}.jsonl"
        event["timestamp"] = datetime.utcnow().isoformat()
        with open(filepath, "a") as f:
            f.write(json.dumps(event) + "\n")

    def log_submission(self, task_id, prompt, model, duration, mode):
        self._write({
            "event": "task_submitted",
            "task_id": task_id,
            "model": model,
            "duration": int(duration),
            "mode": mode,
            "prompt_len": len(prompt),
        })

    def log_completion(self, task_id, status, elapsed_sec, credits_used):
        self._write({
            "event": "task_completed",
            "task_id": task_id,
            "status": status,
            "elapsed_sec": elapsed_sec,
            "credits_used": credits_used,
        })

    def log_error(self, task_id, error_type, message):
        self._write({
            "event": "task_error",
            "task_id": task_id,
            "error_type": error_type,
            "message": message[:200],
        })

Analytics Aggregator

from collections import defaultdict

class UsageAnalytics:
    """Aggregate metrics from JSONL event logs."""

    def __init__(self, log_dir: str = "logs"):
        self.log_dir = Path(log_dir)

    def _read_events(self, date: str = None):
        pattern = f"kling-{date}.jsonl" if date else "kling-*.jsonl"
        events = []
        for filepath in sorted(self.log_dir.glob(pattern)):
            with open(filepath) as f:
                for line in f:
                    events.append(json.loads(line))
        return events

    def daily_summary(self, date: str = None) -> dict:
        date = date or datetime.utcnow().strftime("%Y-%m-%d")
        events = self._read_events(date)

        submitted = [e for e in events if e["event"] == "task_submitted"]
        completed = [e for e in events if e["event"] == "task_completed"]
        errors = [e for e in events if e["event"] == "task_error"]

        succeeded = [e for e in completed if e["status"] == "succeed"]
        failed = [e for e in completed if e["status"] == "failed"]

        total_credits = sum(e.get("credits_used", 0) for e in completed)
        avg_elapsed = (sum(e["elapsed_sec"] for e in succeeded) / len(succeeded)
                      if succeeded else 0)

        by_model = defaultdict(int)
        for e in submitted:
            by_model[e["model"]] += 1

        return {
            "date": date,
            "total_submitted": len(submitted),
            "succeeded": len(succeeded),
            "failed": len(failed),
            "errors": len(errors),
            "success_rate": f"{len(succeeded) / max(len(completed), 1) * 100:.1f}%",
            "total_credits": total_credits,
            "avg_generation_sec": round(avg_elapsed),
            "by_model": dict(by_model),
        }

    def print_report(self, date: str = None):
        s = self.daily_summary(date)
        print(f"\n=== Kling AI Usage Report: {s['date']} ===")
        print(f"Submitted:    {s['total_submitted']}")
        print(f"Succeeded:    {s['succeeded']}")
        print(f"Failed:       {s['failed']}")
        print(f"Success rate: {s['success_rate']}")
        print(f"Credits used: {s['total_credits']}")
        print(f"Avg time:     {s['avg_generation_sec']}s")
        print(f"By model:")
        for model, count in s["by_model"].items():
            print(f"  {model}: {count}")

Cost Analysis

def cost_analysis(analytics: UsageAnalytics, days: int = 7):
    """Analyze cost trends over recent days."""
    from datetime import timedelta

    daily_costs = []
    for i in range(days):
        date = (datetime.utcnow() - timedelta(days=i)).strftime("%Y-%m-%d")
        summary = analytics.daily_summary(date)
        daily_costs.append({
            "date": date,
            "credits": summary["total_credits"],
            "videos": summary["total_submitted"],
            "estimated_usd": summary["total_credits"] * 0.14,
        })

    total_credits = sum(d["credits"] for d in daily_costs)
    total_videos = sum(d["videos"] for d in daily_costs)
    total_cost = sum(d["estimated_usd"] for d in daily_costs)

    print(f"\n=== {days}-Day Cost Summary ===")
    print(f"Total credits: {total_credits}")
    print(f"Total videos:  {total_videos}")
    print(f"Est. cost:     ${total_cost:.2f}")
    print(f"Avg/​day:       ${total_cost / days:.2f}")

    for d in daily_costs:
        print(f"  {d['date']}: {d['credits']} credits, {d['videos']} videos, ${d['estimated_usd']:.2f}")

Export to CSV

import csv

def export_usage_csv(analytics: UsageAnalytics, output: str = "kling_usage.csv"):
    events = analytics._read_events()
    with open(output, "w", newline="") as f:
        writer = csv.DictWriter(f, fieldnames=["timestamp", "event", "task_id",
                                                "model", "status", "credits_used",
                                                "elapsed_sec"])
        writer.writeheader()
        for e in events:
            writer.writerow({k: e.get(k, "") for k in writer.fieldnames})
    print(f"Exported {len(events)} events to {output}")

Resources

• Developer Portal
• Pricing Reference