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

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

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

Prerequisites

• A named project, billing owner, approved daily and per-run credit ceilings, and a current provider pricing source. Treat the tables above as estimates until verified against the account's active plan or resource pack.
• Define the model, duration, mode, audio setting, retry allowance, and expected failure rate. Use synthetic prompts and rights-cleared media for all estimation canaries; no real customer or personal data is needed.
• Have a sandbox destination, draft/watermarked output policy, approval threshold, and a plan to cancel queued work and remove test outputs if the estimate is exceeded.

Instructions

1. Describe the workload and calculate the worst-case credits, including audio, retries, polling overhead where applicable, and a safety reserve. Check that the run fits both the project and account ceilings.
2. Run a single low-cost synthetic canary through BudgetGuard. Confirm the selected model/mode and actual credit charge before authorizing the larger run.
3. Require owner approval for the budget, destination, and promotion from draft/watermarked output to final delivery. Track actual credits by opaque run ID and aggregate model, not by prompt or media.
4. Stop when a ceiling, policy check, rate limit, or cost anomaly fires. Cancel pending work where supported, remove quarantined outputs, and restore the approved lower-cost mode or last approved plan.
5. At closeout, reconcile estimate versus actual, expire temporary artifacts and access, and retain a redacted cost receipt only.

Output

Return a budget worksheet or receipt with opaque run ID, pricing-source timestamp, model/mode/duration/audio assumptions, expected and maximum credits, reserve, actual credits, estimated currency range, approval state, canary result, destination class, retention deadline, and rollback/removal action. Exclude billing identifiers, prompts, media, user identities, and credentials.

Error Handling

• If pricing or model parameters are stale or unknown, label the estimate provisional and stop before submission; do not infer a cheaper rate.
• If credits are depleted or the charge exceeds the ceiling, pause the run and reconcile completed tasks before retrying. A policy refusal or rights failure is not a reason to retry.
• If actual usage diverges from the estimate, quarantine outputs, cancel remaining tasks, notify the billing owner, and record the redacted variance and cleanup receipt.

Examples

For a synthetic 20-clip draft run, set duration=5, mode=standard, audio=false, credits_max=200, reserve=20%, destination=sandbox-review, and watermark=draft. Require approval=granted after the canary and actual_credits<=200; otherwise cancel pending tasks and remove the canary outputs.

Resources

• Pricing Page
• API Resource Packs