Agent skill

klingai-pricing-basics

Understand Kling AI pricing, credits, and cost optimization strategies. Use when budgeting or estimating costs. Trigger with phrases like 'kling ai pricing', 'klingai credits', 'kling ai cost', 'klingai budget'.

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npx add-skill https://github.com/jeremylongshore/claude-code-plugins-plus-skills/tree/main/plugins/saas-packs/klingai-pack/skills/klingai-pricing-basics

SKILL.md

Kling AI Pricing Basics

Overview

Kling AI uses a credit-based pricing system. Credits are consumed per video/image generation based on duration, mode, and model. API pricing uses resource packs billed separately from subscription plans.

Subscription Plans (Web UI)

Plan Monthly Credits/Month Key Features
Free $0 66/day (no rollover) Basic access, watermarked
Standard $6.99 660 No watermark, standard models
Pro $25.99 3,000 Priority queue, all models
Premier $64.99 8,000 Professional mode, priority
Ultra $180 26,000 Max priority, all features

Warning: Paid credits expire at end of billing period. Unused credits do not roll over.

Video Generation Costs

Duration Standard Mode Professional Mode
5 seconds 10 credits 35 credits
10 seconds 20 credits 70 credits

With Native Audio (v2.6)

Duration Standard + Audio Professional + Audio
5 seconds 50 credits 100 credits
10 seconds 100 credits 200 credits

Image Generation Costs (Kolors)

Feature Credits
Text-to-image 1 credit/image
Image restyle 2 credits/image
Virtual try-on 5 credits/image

API Resource Packs

API access is billed separately from subscriptions via prepaid packs:

Pack Units Price Validity
Starter 1,000 ~$140 90 days
Growth 10,000 ~$1,400 90 days
Enterprise 30,000 ~$4,200 90 days

1 unit = 1 credit equivalent. API pricing works out to ~$0.07-0.14 per second of generated video.

Cost Estimation

python
def estimate_cost(videos: int, duration: int = 5, mode: str = "standard",
                  audio: bool = False) -> dict:
    """Estimate credits needed for a batch of videos."""
    base_credits = {
        (5, "standard"): 10,
        (5, "professional"): 35,
        (10, "standard"): 20,
        (10, "professional"): 70,
    }
    per_video = base_credits.get((duration, mode), 10)
    if audio:
        per_video *= 5  # audio multiplier

    total = videos * per_video
    return {
        "videos": videos,
        "credits_per_video": per_video,
        "total_credits": total,
        "estimated_cost_usd": total * 0.14,  # high estimate
    }

# Example: 100 five-second standard videos
print(estimate_cost(100, duration=5, mode="standard"))
# → {'videos': 100, 'credits_per_video': 10, 'total_credits': 1000, 'estimated_cost_usd': 140.0}

Cost Optimization Strategies

Strategy Savings Trade-off
Use standard mode for drafts 3.5x cheaper Slightly lower quality
Use 5s duration, extend if needed 2x cheaper per clip Requires extension step
Use kling-v2-5-turbo 40% faster (less queue time) Marginally lower quality than v2.6
Batch during off-peak hours Faster processing Schedule dependency
Skip audio, add in post 5x cheaper Extra post-production step
Use callbacks instead of polling No cost savings, but fewer API calls Requires webhook endpoint

Budget Guard

python
class BudgetGuard:
    """Prevent overspending by tracking credit usage."""

    def __init__(self, daily_limit: int = 500):
        self.daily_limit = daily_limit
        self._used_today = 0

    def check(self, credits_needed: int) -> bool:
        if self._used_today + credits_needed > self.daily_limit:
            raise RuntimeError(
                f"Budget exceeded: {self._used_today + credits_needed} > {self.daily_limit}"
            )
        return True

    def record(self, credits_used: int):
        self._used_today += credits_used

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