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GPUs when the shot needs them
H100, A100 and L40S instances started for the render and stopped after it. Priced 30% under the leading cloud on-demand for the same GPU.
Studios, broadcasters, games companies and live-events businesses all share one pattern: long stretches of steady work broken by enormous bursts. Rendering a sequence, launching a title, opening ticket sales. Compute gives you the same GPUs the leading cloud does for less, by the second, with no reservation to justify.
Used by independent studios, streaming start-ups and Fortune 500 media groups for rendering, encoding, launches and audience platforms.
What this looks like in practice
H100, A100 and L40S instances started for the render and stopped after it. Priced 30% under the leading cloud on-demand for the same GPU.
Capacity rises with the audience and falls when they leave. You do not reserve for the opening night and pay for it all year.
Captioning, tagging and recommendations through open-weight models via API, priced 30% under the leading cloud’s model service for the same model.
Code, builds, storage and hosting on one platform and one bill, rather than a different vendor at each stage.
Entertainment workloads are spiky by nature. The usual answer is to reserve for the spike and absorb the waste. That trade only exists because the price of on-demand capacity made it necessary.
A sequence needs a few hundred GPU-hours this week and none next week. Owning or reserving that capacity means paying for the weeks in between. On Compute you start the machines for the render, they bill by the second, and they stop. An independent studio and a global media group pay the same rate for the same GPU.
Storefronts and ticketing deployed from git on Panel, scaled for the opening and back down after.
Game and pipeline repositories on CodeGraph, with code review and CI alongside.
Transcoding on CPU instances priced 30% under the cheapest the leading cloud instance with the same specification.
Fan and subscriber experiences deployed close to viewers with Nexus.
Compared with the leading cloud's own published on-demand prices, US East. Every comparison.
H100, A100 and L40S for heavy rendering and model work, consumer GPUs for lighter jobs, and CPU servers for encoding and backends. All are billed by the second.
We do not claim that. The 30% figure applies to GPU and CPU instances, managed database hours, build minutes, AI inference and support plans. Storage and bandwidth are priced separately and published.
No. There are no reservations or minimum terms. You start what you need when you need it.
Yes. Run one job, compare the bill with what the leading cloud charged for the same work, and decide from there.
Run agents, the tools they call and the models behind them on one platform, billed by the token and the second, for less than the same work costs on the leading cloud.
Host the landing pages, attribution pipelines and onboarding flows that bring customers in, and spend less on the infrastructure than you would on the leading cloud.
Run campaign sites, personalisation, content pipelines and marketing data on one platform that costs less than the leading cloud for the same machines and models.
Run the forms, enrichment jobs, scoring models and routing that turn interest into leads, for less than the same infrastructure costs on the leading cloud.
Run CRMs, sales data, call analysis and AI assistants on one platform, priced under the leading cloud for the same machines, databases and models.
Host creator platforms, media processing, storefronts and AI tools on infrastructure that bills by the second and costs less than the leading cloud for the same machines.
Begin with one workload. Keep what you have running. Decide whether to continue once you have seen it carry something real.
No migration required to begin. Nothing to tear down if you stop.