klingai-cost-controls

name	klingai-cost-controls
description	Implement budget limits, usage alerts, and spending controls for Kling AI. Use when managing costs or preventing overruns. Trigger with phrases like 'klingai cost', 'kling ai budget', 'klingai spending limit', 'video generation costs'.
allowed-tools	Read, Write, Edit, Bash(npm:*), Grep
version	1.18.0
license	MIT
author	Jeremy Longshore <[e-mail]>
tags	["saas","kling-ai","cost-controls","budget"]
compatibility	Designed for Claude Code

Kling AI Cost Controls

Overview

Prevent unexpected spending with per-request cost estimation, daily budget enforcement, threshold alerts, and usage dashboards. Credits are consumed per task based on duration, mode, and audio.

Credit Cost Reference

Config	Credits
5s standard	10
5s professional	35
10s standard	20
10s professional	70
5s standard + audio (v2.6)	50
10s professional + audio (v2.6)	200
Image generation (Kolors)	1
Virtual try-on	5

Budget Guard

import time
from dataclasses import dataclass, field

@dataclass
class BudgetGuard:
    """Enforce daily credit budget with alerting."""
    daily_limit: int = 1000
    alert_threshold: float = 0.8  # alert at 80%
    _used: int = 0
    _reset_time: float = field(default_factory=time.time)
    _alerts_sent: set = field(default_factory=set)

    def _check_reset(self):
        if time.time() - self._reset_time > 86400:
            self._used = 0
            self._reset_time = time.time()
            self._alerts_sent.clear()

    def estimate_credits(self, duration: int = 5, mode: str = "standard",
                         audio: bool = False) -> int:
        base = {(5, "standard"): 10, (5, "professional"): 35,
                (10, "standard"): 20, (10, "professional"): 70}
        credits = base.get((duration, mode), )
         audio:
            credits *= 
         credits

     () -> :
        ._check_reset()

        
        usage_pct = (._used + credits_needed) / .daily_limit
         usage_pct >= .alert_threshold     ._alerts_sent:
            ._alerts_sent.add()
            ._on_alert()

         ._used + credits_needed > .daily_limit:
             RuntimeError(
                
            )
         

     ():
        ._used += credits

     ():
        
        ()


     () -> :
        ._check_reset()
         (, .daily_limit - ._used)


     () -> :
        ._check_reset()
         {
            : ._used,
            : .daily_limit,
            : .remaining,
            : ,
        }

Pre-Batch Cost Check

def pre_batch_check(prompts: list, budget: BudgetGuard,
                    duration: int = 5, mode: str = "standard"):
    """Estimate and validate batch cost before submission."""
    per_video = budget.estimate_credits(duration, mode)
    total = len(prompts) * per_video

    print(f"Batch estimate: {len(prompts)} videos x {per_video} credits = {total} credits")
    print(f"Budget remaining: {budget.remaining}")

    if total > budget.remaining:
        raise RuntimeError(
            f"Batch needs {total} credits but only {budget.remaining} remaining. "
            f"Reduce to {budget.remaining // per_video} videos or lower mode."
        )
    return total

Cost-Aware Client

class CostAwareKlingClient:
    """Kling client that enforces budget on every request."""

    def __init__(self, base_client, budget: BudgetGuard):
        self.client = base_client
        self.budget = budget

    def text_to_video(self, prompt: str, **kwargs):
        credits = self.budget.estimate_credits(
            kwargs.get("duration", 5),
            kwargs.get("mode", "standard"),
            kwargs.get("audio", False),
        )
        self.budget.check(credits)

        result = self.client.text_to_video(prompt, **kwargs)
        self.budget.record(credits)
        return result

Optimization Strategies

Strategy	Savings	Implementation
Standard for drafts	3.5x cheaper	mode: "standard" for iterations
5s clips, extend later	50% per clip	Generate 5s, use video-extend selectively
v2.5 Turbo over v2.6	Faster (less queue cost)	model: "kling-v2-5-turbo"
Skip audio, add in post	5x cheaper	motion_has_audio: false
Batch off-peak	Faster processing	Schedule overnight
Cache prompts	Avoid duplicates	Hash prompt + params, check before submitting

Usage Tracking

import json
from datetime import datetime

class UsageTracker:
    """Log every generation for cost analysis."""

    def __init__(self, log_file: str = "kling_usage.jsonl"):
        self.log_file = log_file

    def log(self, task_id: str, credits: int, model: str,
            duration: int, mode: str, prompt: str):
        entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "task_id": task_id,
            "credits": credits,
            "model": model,
            "duration": duration,
            "mode": mode,
            "prompt_preview": prompt[:100],
        }
        with open(self.log_file, "a") as f:
            f.write(json.dumps(entry) + "\n")

Resources

• Pricing
• Developer Portal