Together AI 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 Together AI 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 Together AI does not close: Together AI is primarily a model platform (inference endpoints and fine-tuning on open models) with GPU clusters alongside. If you want to call a model rather than administer a machine, they are the better answer and we are not competing for that.
To be fair, Together AI's real strength is real: Managed inference and fine-tuning APIs for open models, with no machine to run at all.
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 Together AI
Inference alternatives to other clouds
