What I Learned Building a Credit-Based AI Video Workflow

AI video generation looks simple from the outside:

1. send a prompt
2. wait
3. get a video

But once you turn it into a real SaaS product, the difficult part is rarely the prompt box.

The difficult part is everything around the model.

Over the past few months, I’ve been working on a browser-based AI image and video product, and some of the biggest lessons have been around credits, failed jobs, model differences, and keeping the interface understandable.

Credits are easier to understand than raw API costs

Different video models can have completely different pricing structures.

Cost may depend on:

• model
• duration
• resolution
• audio
• generation mode

Exposing all of that directly to users quickly becomes confusing.

A credit system creates a useful abstraction between infrastructure cost and the product interface.

Instead of showing:

This request costs $0.18 per second.

the product can show:

This generation costs 60 credits.

That also gives the product some flexibility if provider pricing changes later.

The important part is making the credit system predictable enough that users can understand what they are spending.

Failed generations should not feel like paid failures

Video generation is not always reliable.

Jobs can fail because of:

• provider errors
• timeouts
• temporary capacity issues
• moderation
• malformed output
• upstream API problems

Charging users permanently for infrastructure failures is a quick way to lose trust.

A cleaner pattern is:

reserve credits
→ start generation
→ confirm success
→ finalize charge

If the job fails for a system reason, return the reserved credits.

This sounds simple, but it affects billing logic, job states, retries, and frontend messaging.

Different models should share one core workflow

One of the easiest ways to make an AI product confusing is to expose every provider-specific parameter directly.

One model may support:

• camera movement
• audio
• several durations

Another may support:

• start frame
• end frame
• different resolutions

A third may expose an entirely different set of controls.

Instead of building a completely different UI for every model, I found it more useful to keep the core flow consistent:

prompt
→ optional image
→ model
→ duration
→ advanced settings
→ generate

Then model-specific settings only appear when they are actually supported.

Text-to-video and image-to-video are different user problems

At the API level they can look similar, but users approach them very differently.

Text-to-video begins with an idea.

Image-to-video begins with an existing composition.

For text-to-video, users usually need to describe:

• subject
• movement
• environment
• lighting
• camera behavior
• pacing

For image-to-video, much of the visual identity already exists. The prompt is often more focused on motion, expression, camera direction, and scene behavior.

Because of that, I eventually separated the two workflows instead of forcing everything into one generic video form.

For example, while building my own product I separated out a dedicated text-to-video workflow rather than treating every video generation request as the same type of job.

That small product decision made both the interface and the underlying parameter handling easier to reason about.

Model abstraction is useful, but only up to a point

It is tempting to normalize every provider behind one giant schema.

That works until a provider introduces something unique.

A more maintainable approach is to have common fields plus capability flags.

For example:

{
  model: "example-model",
  capabilities: {
    textToVideo: true,
    imageToVideo: true,
    audio: false,
    startEndFrames: true,
    maxDuration: 10
  }
}

The frontend can then render only the controls supported by the selected model.

That is much easier to maintain than scattering model-name checks throughout the application.

Generation history becomes important surprisingly quickly

Once users create more than a few videos, they start needing:

• generation history
• job status
• failed-job visibility
• download links
• prompt history
• model information
• credit usage

Without a history system, every generation feels temporary.

A simple private library can improve the product experience more than adding yet another generation model.

Cost control is really a product design problem

The API request itself is only part of the actual cost.

You also need to account for:

• retries
• failed generations
• storage
• bandwidth
• previews
• provider switching
• abuse
• promotional credits

That means pricing cannot be designed separately from the generation architecture.

Credit pricing, retry logic, subscriptions, and model routing all affect one another.

Final takeaway

Building an AI video SaaS is less about connecting a generation API and more about creating a predictable system around unpredictable models.

The abstractions that have been most useful for me are:

• credits instead of exposing raw provider pricing
• returning credits when infrastructure fails
• capability-based model configuration
• separate UX for text-to-video and image-to-video
• persistent generation history
• model-specific controls only when necessary

The model generates the video.

Most of the actual product work happens around it.