Trusted by Leading AI Teams
Toloka Train
The simplest way to lower your AI bills.
Stop overpaying for heavy, slow AI requests. Upload your data to compress your prompts or fine-tune a model for your specific task. You get immediate cost savings, with zero infrastructure to manage.
From a real production workload: a 5,000-token instruction prefix on a Qwen base model, run through Prompt Gisting.
5.3×
fewer prompt tokens per request
15%
lower GPU cost per sample
1.2×–1.8×
faster, with gains growing under load
The problem and our solution
The problem
Your current AI setup is doing too much heavy lifting.
Either the off-the-shelf model isn't accurate enough for your specific needs, or your prompts are so long that they are making your requests slow and expensive.
The solution
Pick the fix your application needs.
Bring your data and choose your budget. We will either fine-tune a model to improve its accuracy or compress your long prompts to make your current setup cheaper and faster.
Two pipelines, one platform
01
Fine-Tuning
Fine-tune a model for your specific task.
Get the accuracy of a custom model without the massive computing costs. Best for teams that need better response quality.
02
Prompt Compression
Stop paying for the same prompt, every request.
We compress your massive static instructions into a tiny package, drastically reducing your costs per request. Best for teams that need to cut costs and speed up response times at volume.
How it works
01
Upload your data
Simply upload your examples in a standard JSONL format.
02
Configure
Pick your model and choose a budget tier: Low, Medium, or High.
03
Run
We validate your data and run the job instantly.
Runs are strictly capped at six hours, so you never overpay. If there is an issue with your data, we reject it with a clear reason before you are ever charged.
Get started
No infrastructure required.
Learn more
Want it fully managed?
Hand us the workflow and get back a production-ready model, built on expert-corrected data, RL Gym environments, and a defensible eval.

