AI Video Generation Cost Calculator

Generative video platforms bill by the second of output because compute scales with the number of frames synthesized and the pixel count of each frame. A 30-second clip at 24 frames per second is 720 frames, and a diffusion model denoises each one over many sampling steps while enforcing temporal consistency across neighbours, so cost rises roughly linearly with duration and with resolution. The resolution term is steep: 1080p at 1920 by 1080 carries about 2.07 million pixels against 0.92 million at 720p, and 4K at 3840 by 2160 carries four times the 1080p count. Published per-second rates reflect that, commonly doubling from 720p to 1080p and doubling again to 4K. The iteration multiplier is what separates the sticker price from the real one. Diffusion sampling is stochastic, so the same prompt and a different random seed produce a materially different clip, and typical production workflows discard two to four attempts before accepting one - hence a cost per usable video two to five times the nominal per-second rate. Two caveats limit any estimate. First, most platforms sell credits under monthly subscription tiers rather than raw per-second billing, so effective rates fall as volume rises and unused credits may expire. Second, published rates move frequently in a competitive market, and the figures used here should be treated as planning-grade approximations rather than contract terms. Generating at 1080p for review and upscaling only the approved cut is the standard way to cut the 4K premium.

Machine-generated video began with adversarial models. Between 2016 and 2018 researchers produced short, low-resolution clips with architectures such as VGAN, TGAN, and MoCoGAN, which separated motion from content but struggled past a second or two of coherent footage. The decisive shift came from diffusion: Jonathan Ho, Ajay Jain, and Pieter Abbeel published denoising diffusion probabilistic models in 2020, and the technique was extended to the time axis in the Video Diffusion Models paper from Google in 2022, followed within months by Meta AI Make-A-Video and Google Imagen Video, both announced in late 2022. Commercial availability, and therefore commercial pricing, followed quickly. Runway released Gen-1 in February 2023 and Gen-2 that June, making text-to-video available to ordinary subscribers; Pika Labs launched the same year; Stability AI published Stable Video Diffusion weights in November 2023. OpenAI announced Sora in February 2024, and Kuaishou released Kling in June 2024, establishing a competitive field in which platforms differentiated on clip length, fidelity, and price. Because generation is expensive and highly variable in duration, the industry converged on credit-based metering rather than the flat per-seat subscriptions common in earlier creative software, which is why budgeting for AI video means forecasting seconds of output and attempts per usable clip.