Shadeform Alternatives for Inference
Serving or batch-running a model for generation, where throughput per dollar decides the bill.
What inference actually needs
Why people look past Shadeform for this
Little state to carry, so this is the job that moves most freely on price. The cost is the weight pull and the warm-up on the new box, not lost work. That is exactly the gap Shadeform does not close: Shadeform solves the same first half of the problem we do: one API across many GPU clouds instead of an account per vendor. Where we diverge is the second half: finding capacity is not the same as being able to take your environment with you when you move to it.
To be fair, Shadeform's real strength is real: Broad first-party cloud coverage with a single API, and a clean deployment story for teams that mainly need capacity aggregation.
Live rates for the GPUs inference wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded vllm-batch job recipe — a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
More alternatives pages
Other workloads on Shadeform
Inference alternatives to other clouds
