veo3-fast is a video generation model in the Google Veo 3 series focused on speed and creative iteration, suitable for turning advertising concepts, product images, and animation scripts into dynamic clips. On this platform, you can start creating from text or use first-frame or first-and-last-frame images to compose shots, combined with landscape and portrait formats, 1080p retrieval, and asynchronous task processing to create a workflow from creative testing to asset delivery.
Submit requests to the public API at api.acedata.cloud using the documented parameters, then use the results in your application.
Input parameters and result formats vary by service. Use the public API for this model and follow its guide for generation, task retrieval and editing operations.
Clarify capacity, inputs and outputs, and calling methods before selecting a model.
Fast's speed-first positioning is a native model feature; the number of images, aspect ratios, and HD retrieval method are this platform's calling specifications.
Learn what veo3-fast can bring to your work.
The core value of veo3-fast is rapidly exploring different approaches. Around the same product selling point, you can separately describe the scene, subject actions, camera movement, and visual style to generate comparable clips. It is suitable for screening advertising directions before formal production, rather than cramming multiple complete stories into a single request.
Image-to-video can start from one image or use two images to define the opening and ending. Product stills, character concept art, or scene design images can all become starting points for creation; prompts describe the actions and camera changes in between, moving assets from static presentation toward purposeful dynamic expression.
Video creation can use asynchronous submission, so the application does not need to keep waiting for a connection. Save the task_id after submission, receive results through queries or callbacks, then retrieve the video_url based on the status. When an HD version is needed, use the video ID from the generation result to retrieve 1080p, making it easier to integrate with asset libraries and review workflows.
Start with specific tasks to identify where the model can be effective.
Enter product selling points, audience, and shot descriptions, then try lifestyle scenes, studio presentations, or animated expressions separately to deliver a set of dynamic samples for team review. It is recommended to adjust only one creative variable at a time, such as the background, action, or camera distance, to make it easier to compare which opening is better suited for subsequent production.
Use a product image as the first frame and describe the desired presentation action, such as the camera slowly moving closer, changes in lighting, or the product entering a usage scenario. If you already have a clear ending composition, you can also provide a final frame to deliver clips suitable for product introductions and social content editing, rather than using multiple product images as blending materials.
Break the explanatory script into independent visual segments, clearly describe the subject, environment, and actions for each segment, then generate animation mockups or operational scenarios. The deliverables are suitable for discussing pacing, supplementing training materials, or validating storyboards; the final course still requires organized editing, subtitles, and reviewed explanatory content.
Choose based on task complexity, input materials, and expected results.
When the task is to test multiple ad concepts, prepare dynamic samples for a product catalog, or repeatedly revise shot expression, prioritize veo3-fast. If the main goal shifts to visual quality, compare actual results with the veo3 standard version in the same series: the former emphasizes iteration speed, while the latter is a quality-first mode, so fixed processing times or a uniform quality gap cannot replace clip review.
For text creation, a single-image first frame, or two-image first and final frames, veo3-fast already provides corresponding workflows. If the task is to blend multiple independent reference images, choose veo31-fast-ingredients; if 4k output is explicitly required, consider veo31, which supports this option. Two first and final frames define temporal endpoints and are not equivalent to multi-image content blending.
From a small-scale task to full integration.
Define the goal, required inputs, and output requirements, using real business examples as a starting point.
Open the trial page, confirm the parameters supported by this entry point, then submit a small-scale task to view the results.
Keep the complete model ID, use the request format specified in the documentation, and confirm billing rules on the Pricing page.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using veo3-fast.
Both belong to the Veo 3 series. veo3-fast is better suited for rapid iteration, ad variations, and product samples; veo3 is the quality-first mode. You can first use Fast to screen ideas, then compare standard-version results for selected shots, rather than assuming there is a fixed difference in speed or image quality between the two.
Use image2video and pass image links in image_urls. One image serves as the first frame, while two images serve as the first and last frames; the prompt describes the action and camera changes in between. This method is not multi-image blending, so it is recommended to prepare start and end images around the same subject or a clearly defined transition relationship.
For vertical video, set aspect_ratio to 9:16; use 16:9 for horizontal video. veo3-fast does not support 4k; the default output is 720p, with 1080p or gif also available. Aspect ratio determines composition proportions, while resolution determines output clarity, and they should be set separately.
Submit a get1080p operation to /veo/videos and use video_id to specify the generated video. This value comes from the id in the completed result data, not task_id. When saving generation results, it is recommended to retain the task ID, video ID, and video link for subsequent processing.
No. After setting async to true, you can first obtain task_id and then query the task result; you can also provide callback_url to receive a result push after completion. Once you receive the completed result, check state and read video_url to download the video or incorporate it into your own review and asset management workflow.
Model information · Updated: 2026-10-01. For calling parameters and billing rules, see the API and pricing sections.
Choose a development approach or application suited to this model and follow its setup guide.
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