Tune it here, serve it here
Because we own the serving stack, an adapter you train can go live behind the same model ID your application already calls — no new endpoint, no second SDK.
LoRA by default
Rank-16 to rank-128 adapters train in hours and load in seconds, so a tuned variant costs the same to serve as the base model.
Full fine-tune on request
For domain shifts a LoRA cannot cover, full-parameter runs on reserved capacity, priced per GPU-hour.
Same model ID
Serve the adapter as llmcloud/<model>:<tune>. Routing policies, caching and analytics keep working unchanged.
Your data stays yours
Training data is deleted after the run unless you ask us to retain it, is never used to train anything else, and stays in the region you pick.
Eval before promote
Every run reports against your held-out set and the base model. Promote to production only when it wins.
Export the weights
Adapters and full checkpoints are yours to download and run elsewhere, including self-hosted.
POST /v1/fine-tunes
{
"base_model": "llmcloud/qwen3-235b-a22b",
"method": "lora",
"rank": 32,
"training_file": "file_9f2c...",
"eval_file": "file_1a7d...",
"region": "eu-west"
}{
"model": "llmcloud/qwen3-235b-a22b:support-v3",
"messages": [{"role":"user","content":"…"}]
}When does this ship?+
LoRA on the DeepSeek and Qwen families first, then Llama and gpt-oss. Waitlist members get access as each family opens.
What will it cost?+
Training is billed per GPU-hour at the dedicated-capacity rate. Serving a LoRA adapter costs the same per token as the base model — we do not surcharge tuned variants.
Can I tune a closed frontier model?+
No. Tuning is limited to open weights we host. For closed models, use the provider's own tuning API through BYOK.
Which regions?+
Wherever the base model already runs, including sovereign regions once those go live.