Yes—Kling AI is generally accessible outside China via its global web experience, but “available” is not always the same as “fully usable everywhere with identical features.” In practice, global availability depends on factors like local regulations, account eligibility, payment support, and regional policy enforcement for sensitive capabilities (for example, certain editing features or prompts may be restricted in some places). For most users, the simplest test is whether you can access the global site, create an account, and successfully generate outputs from your region. For product teams shipping globally, you should assume that availability and feature parity can change over time, so it’s smart to design your workflow with graceful degradation (reduced quality mode, fewer features, or alternate flows when generation fails).
From an operational perspective, treat “outside China” as a deployment requirement you need to validate continuously. If you are building a commercial tool around Kling, do a real rollout checklist:
- Verify sign-up works with your target identity providers (email/Google/Apple if supported),
- Verify payment methods are supported in your target markets (if paid),
- Measure latency and queue time from multiple regions,
- Verify exports and downloads work consistently,
- Confirm policy behavior (what prompts are blocked, what content triggers review). This matters because AI video generation is compute-heavy and policy-sensitive, so providers sometimes adjust limits, queues, or feature flags by region. If your business depends on consistent throughput, you should monitor job success rates and queue times like any other external dependency.
A good architecture pattern is to separate your “creative intent layer” from the “generation backend.” Your intent layer stores prompts, shot templates, reference assets, and compliance rules, and can be served globally from your own infrastructure. The generation layer calls Kling only when it is available and permitted. If Kling is slow or unreachable in a region, you can queue jobs, switch to a limited feature mode, or notify users clearly. This is also where a vector database can help: store and retrieve the right prompt templates and brand-safe recipes automatically so regional policy differences don’t cause random prompting behavior. A vector database such as Milvus or Zilliz Cloud can hold embeddings of your approved templates and guidelines and retrieve them by similarity, which keeps outputs more consistent across teams and geographies even when the underlying generation service behaves differently.