Adding AI image and video generation to an app sounds like a weekend project until the first real users arrive. Generation can take seconds or minutes, costs vary by model, and the results are never identical twice. A short checklist keeps the work predictable. This guide walks through the steps most teams need, from picking a model to checking outputs before anything goes live.
Choose a Model by Input and Output Needs
Start with what your product actually needs to accept and return. Does the user type a prompt, upload a photo, or supply several reference images? Do you need a still image, a short clip, or a clip with sound? Models differ widely here: some only handle text to image, others turn a still into a few seconds of video, and some take multiple references.
Write the requirement down in one line, such as “reference photo in, five-second clip out,” then filter the catalog against it. Check the supported resolution, clip length and file formats too. A model that looks impressive in a demo but cannot accept your input type will cost you a rewrite later.
Test Representative Prompts Before You Commit
A single impressive result proves very little. Build a small test set of prompts that mirror what your users will really type, including the dull ones, the vague ones and a few awkward edge cases. Run the same set across two or three candidate models and compare the results side by side.
Keep notes on consistency, how well the model follows instructions, and how often a run needs to be repeated. Platforms such as SpicyAPI, a developer API for image, video and chat models with a browser Studio, make this stage easier because you can try different models in the browser first and move to code once you know which one fits.
Handle Asynchronous Generation Jobs Properly
Video especially is rarely instant, so treat generation as a job rather than a request that returns a file. The usual pattern is to create a task, store its ID, and then either poll for status or receive a callback when it finishes. Your interface should show a clear waiting state and never block the rest of the app.
Plan for failure as well. Jobs can time out, fail or return something unusable. Use an idempotency key on each create call so a retry after a network error does not produce a duplicate job or a second charge. When you retry after a network error, reuse the original idempotency key rather than generating a new one; a fresh key tells the provider it is a brand-new request. Then give users a simple way to try again.
Estimate Usage Costs Early
Pricing differs by unit: per image, per second of video, or per million tokens for chat. Multiply your expected daily volume by a realistic average, then add a margin for retries and rejected outputs. Check whether the provider shows an estimate for your exact settings before a job runs, because that makes it easy to cap spending in your own code.
Consider setting per-user limits and a clear balance check before each request. Surprises on the invoice are far easier to prevent than to explain.
Review Outputs Before Publication
Generated media should not go straight to a public page. Add a review step, whether that is an automated check, a human approval queue or both, depending on your risk. Look for visual glitches, text artifacts, unintended likenesses and anything that conflicts with your own content policy.
Be clear about who is responsible for what. Even when a platform forwards requests without adding its own filter, legal and consent duties remain with the company building on top of it. Decide on retention too: how long you keep generated files and prompts, and how you delete them on request.
Conclusion
A good integration comes down to a few steady habits: match the model to your inputs and outputs, test with realistic prompts, treat generation as an asynchronous job, estimate costs before launch and review results before publishing. Follow this checklist and adding AI image and video generation to an app becomes a controlled engineering task instead of a gamble.
Read more : Using Technology to Transform Early Education
