Kling AI Production Checklist

Overview

Checklist covering authentication, error handling, cost controls, monitoring, security, and content policy before deploying Kling AI video generation to production.

Authentication

• AK/SK stored in secrets manager (not .env in repo)
• JWT auto-refresh with 5-min buffer before 30-min expiry
• Separate API keys per environment (dev/staging/prod)
• Key rotation schedule (quarterly minimum)
• Authorization: Bearer <token> format verified

Error Handling

• HTTP 400/401/402/403/429/5xx all handled
• Exponential backoff with jitter for 429/5xx retries
• Max retry limit set (3-5, not infinite)
• taskstatus: "failed" logs taskstatus_msg
• 30s timeout on all HTTP calls
• duration sent as string "5" not integer 5

Cost Controls

• Daily credit budget enforced in code
• Alert at 80% daily budget consumption
• standard mode used for non-final renders
• Max poll attempts cap (no infinite loops)
• Credit estimate before batch submission

# Pre-batch credit check
credits_needed = len(prompts) * 10  # 10 credits per 5s standard
if credits_needed > DAILY_BUDGET:
    raise RuntimeError(f"Batch needs {credits_needed}, budget is {DAILY_BUDGET}")

Task Management

• All task_ids logged with timestamp
• Stuck task detection (>10 min in processing)
• callback_url used instead of polling in production
• Failed tasks queued for retry
• Video URLs downloaded promptly (Kling CDN URLs expire)

Content Safety

• Prompts validated before API submission
• User-generated prompts sanitized
• Default negative prompt includes safety terms
• Content moderation on user-facing apps
• Policy violation errors handled gracefully

Security

• API keys never logged (redacted in debug output)
• Video URLs treated as temporary (store on own CDN)
• Webhook endpoints HTTPS-only
• Rate limiting on your API layer
• No sensitive data in prompt strings

Monitoring

• API latency tracked per endpoint
• Success/failure rate dashboard
• Credit consumption metrics
• Alert on >5% failure rate
• Structured JSON logs for all API calls

Performance

• Connection pooling via requests.Session()
• Concurrent tasks within API tier limit
• Video downloads async/background
• Generated videos CDN-cached

# Connection pooling
session = requests.Session()
adapter = requests.adapters.HTTPAdapter(pool_connections=5, pool_maxsize=10)
session.mount("https://", adapter)

Pre-Launch Smoke Test

from kling_client import KlingClient

c = KlingClient()
result = c.text_to_video("test: blue sky with clouds", duration=5, mode="standard")
assert result["videos"][0]["url"], "No video URL"
print("READY FOR PRODUCTION")

Prerequisites

• A signed release change, approved production workspace, rights-cleared or synthetic smoke-test brief, policy review, budget ceiling, draft-only canary route, and tested rollback/removal procedure.

Instructions

1. Run the smoke test in staging first with a synthetic, watermarked draft; reject literal credentials, unapproved sources, or public destinations.
2. Verify health, task completion, policy/rights outcome, credit usage, access scope, retention, and draft-only behavior before production approval.
3. Release one bounded production canary only after owner sign-off; halt and remove outputs on policy, rights, quality, scope, or budget drift.
4. Promote in stages, preserving a redacted receipt and the rollback reference; delete temporary assets at the retention boundary.

Output

Produce a production receipt with release ID, environment, smoke-test brief classification, aggregate health/task result, policy/rights/budget checks, draft destination, approver, retention/removal proof, and rollback reference. Exclude prompts, asset URLs, and credentials.

Examples

release=r31; env=staging; brief=synthetic-smoke; policy=pass; rights=pass; destination=draft-only; approval=pending; rollback=r30 supports canary approval.

Resources

• API Reference
• Content Policy
• Developer Portal