What image generation looks like inside a real product
Most useful image generation isn't a blank text box. It's a focused feature that does one job well: turning a product photo into seasonal marketing variants, generating room mockups from a floor plan, creating thumbnails from an article, or letting a user restyle an uploaded image while keeping the subject intact.
We start from the job your users are trying to finish, then pick the model and controls that fit. Sometimes that's a hosted diffusion API; sometimes it's an open-weight model you run yourself for cost or privacy reasons.
- Text-to-image, image-to-image, inpainting (edit part of an image), and outpainting (extend the canvas)
- Brand and style consistency using reference images, LoRA fine-tunes, or prompt templates
- Structural control with techniques like ControlNet so layout, pose, or edges stay fixed
- Upscaling and cleanup passes so output is web- or print-ready
- Background removal, object insertion, and product-photo compositing
Keeping generated images safe, legal, and on-brand
Generated imagery carries real risk: off-brand results, copyrighted or trademarked content, unsafe output, and unpredictable cost. A production feature needs the same rigor as any other part of your product.
We build the guardrails in from day one — content filtering on prompts and outputs, human review where it matters, and clear labeling of AI-generated media where your policies or regulations call for it.
- Content moderation on both the prompt and the generated result
- Model and license selection appropriate for commercial use
- Optional AI-content labeling and provenance metadata (e.g., C2PA-style)
- Rate limits, per-user quotas, and hard cost caps to prevent runaway spend
- Caching so identical requests aren't regenerated from scratch
What a build like this costs to run
There's no flat price for image generation because the drivers vary widely. The two big ones are how much you generate — each image is a per-call inference cost, and higher resolution or more steps costs more — and whether you run models on your own GPUs or call a hosted API.
We route to the cheapest option that still meets your quality bar, then set spend caps so a viral moment doesn't produce a surprise bill. Start with a free consultation and we'll estimate the running cost for your specific use case.
- Inference is billed per image (or per GPU-second if self-hosted); resolution and step count raise it
- Hosted APIs are simplest to start; self-hosting can be cheaper at high volume but adds ops work
- Storage and CDN delivery for the generated assets
- One-time build vs. ongoing inference and maintenance are separate line items
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Frequently asked questions
Can I use AI-generated images commercially?
Often yes, but it depends on the specific model and its license — some hosted services grant commercial rights, others restrict them, and the legal landscape is still evolving. We help you pick a model whose terms fit your use and flag anything that needs your own legal review. We won't tell you it's 'fully safe' when the honest answer is 'it depends.'
Will the images match our brand style?
We can get close through reference images, prompt templates, and fine-tuning on your own approved assets — but generation is probabilistic, so exact consistency isn't guaranteed on every render. For high-stakes uses we add a human approval step so nothing off-brand goes out automatically.
Do we have to run our own GPUs?
No. Most projects start on a hosted API with no infrastructure to manage. Self-hosting open models can lower per-image cost at high volume, but it adds real operational work — we'll only recommend it when the math clearly favors it.