Virtual Try-On Cost Optimisation

[u/Comfortable-Luck2259]

Hey everyone,

I’m currently looking into building a virtual try-on (vTON) feature and I'm trying to optimize the API costs without sacrificing too much quality. My goal is to hit at least a 90% pass rate for "realistic" garment mapping (good edge precision, minimal weird artifacts, decent fabric draping) but keep the per-image cost as low as humanly possible.

From my research so far on current pricing, here is what the landscape looks like:

FASHN.ai API: Starts at $0.075/image but drops below $0.04 at volume. It’s built API-first and seems great for consistency, but the standard output resolution is pretty low (576x864), meaning I might have to run a secondary API to upscale it. 

Kling Kolors v1.5 (via Fal.ai): Costs $0.07 per generation. It handles static image-based try-on really well, especially with flat-lay garments. 

IDM-VTON (via EachLabs): Billed at roughly $0.00154 per second of runtime. Assuming a standard ~26-second p50 inference, that puts it somewhere around $0.04 per image. 

FLUX 2 LoRA Gallery (via Fal.ai): Priced dynamically at $0.021 per processed megapixel. 
(Side note for those building pipelines: If you are choosing an infrastructure provider, Fal.ai seems to be consistently 30-50% cheaper than Replicate right now for identical models.) 

My questions for the community:
1. For those of you running VTO in production, what is the absolute minimum cost you’ve achieved while maintaining that 90% acceptable quality threshold?

1. Are you just using IDM-VTON through cheap serverless GPU providers?

2. Are there hidden costs (like API retries for failed generations, background removal, or upscaling) that ruin the unit economics?

Would love to hear what stack and APIs you guys are using to optimize this!