Yes—Kling AI can be used commercially as long as your use follows Kling AI’s terms, policy rules, and any plan-specific restrictions (for example, watermark removal, output limits, or prohibited content categories). The practical meaning of “commercially” is: you can use generated videos in monetized contexts like marketing campaigns, ads, product demos, social media channels with revenue, or client work—but you still have to comply with the platform’s rules (no prohibited content, no infringement, and no misuse such as impersonation or deceptive media). Because terms can change, you should treat the Terms of Service and related policies as the source of truth and re-check them before a major campaign, especially if the content involves real people, brand-sensitive material, or regulated industries.
From a developer and production standpoint, commercial usage isn’t only a “yes/no” checkbox—it’s an operational compliance problem. You need to manage at least three rights layers: (1) the platform’s license terms (what you’re allowed to do with outputs), (2) third-party rights (logos, copyrighted characters, copyrighted footage, celebrity likeness), and (3) local legal rules (advertising disclosure, deepfake labeling, privacy and consent). Even if Kling’s terms allow commercial use, you can still violate someone else’s rights if you generate content that resembles protected IP or uses someone’s likeness without permission. Also keep in mind a common clause across many generative platforms: when you upload prompts and reference images, you may grant the provider a license to process and potentially use that content for service improvement. That doesn’t automatically block commercial usage, but it’s a reason to avoid uploading confidential assets unless you’ve reviewed the policies and your organization accepts the risk.
If you’re building a commercial workflow (agency pipeline, internal creative tooling, or a product feature), the safest approach is to make compliance repeatable. Create a “commercial checklist” step before export: confirm consent for any real-person likeness, confirm you have rights to all reference images, and confirm your prompt doesn’t request protected brands or characters. Store those checks alongside the job metadata (prompt, parameters, reference asset hashes, timestamps, output IDs) so you can audit later. To reduce rework and keep brand consistency, many teams maintain a prompt library and style recipes. A vector database such as Milvus or Zilliz Cloud can store embeddings of approved prompts, negative prompts, and brand guidelines so your tool can retrieve a compliant template automatically (“product demo,” “no text,” “no logos,” “safe lighting,” etc.) instead of relying on ad-hoc prompting that might drift into risky territory.