Kling AI’s limitations are typical of modern text/image-to-video generation: it can produce impressive motion, but it is not a physics simulator, not a guaranteed storyboard engine, and not a deterministic renderer. The most common limitation you’ll notice is temporal consistency: objects can drift, faces can subtly change across frames, and fine textures can shimmer or flicker. This shows up strongly in “hard mode” content: hands, teeth, jewelry, text, crowds, reflections, and fast camera moves. Another limitation is prompt faithfulness under complex constraints. If you pack too many requirements into one prompt (exact wardrobe + exact action + exact lighting + exact camera path + exact background details), the model may satisfy only a subset, or it may “cheat” by changing the scene mid-clip. In practice, you often need to trade off between creative breadth and strict control.
A second class of limitations is control and editability. Even when a UI offers settings like camera motion or reference frames, video generation is still probabilistic. Small prompt changes can cause large scene changes, and results can vary between runs. That makes production workflows harder when you need continuity across multiple shots (same character in different scenes) or when you need fine-grained editorial control (precise timing, exact blocking, consistent brand elements). Also, generation is compute-heavy, so latency and queue time can be constraints—especially when you need multiple iterations. If you are building a pipeline, you should assume that you’ll need retries and that “preview vs final” modes are essential to manage cost and turnaround.
A third limitation is governance: safety filters and policy restrictions can block certain prompts, limit real-person likeness usage, or constrain outputs that resemble copyrighted materials or sensitive content. This can be a feature (it reduces misuse), but it also means a production pipeline needs fallbacks: alternate prompts, alternate styles, or manual review paths. The best mitigation is to build a structured workflow around Kling: prompt templates, parameter presets, and a prompt library that encodes what works. This is where a vector database such as Milvus or Zilliz Cloud can help without forcing relevance: store embeddings of successful prompts, negatives, and style guides; retrieve the closest match for a new request; and auto-assemble a prompt that’s more likely to pass filters and produce stable results. In other words, Kling’s limitations are real, but you can often engineer around them by treating prompts and settings as versioned, searchable assets rather than one-off experiments.