tiyuvta

Open models, tuned to your work,running on hardware you control.

If your product runs on an LLM, the API bill is your cost of goods.

It grows with every customer you win, and you do not control the price. We move that work onto hardware you control at self-hosting cost: models tuned to your data and your language, the pipeline around them, and a serving stack tuned on your workload.

Cutting a token bill to a fraction of itself is the whole point. Teams doing this seriously land near a third of what they were paying, on better models than they started with, and the hardware keeps working after it is paid for.

The same work also runs the internal side: customers answered, documents handled, company knowledge kept current. And if your workload is small, start on our API in minutes instead. We are the same people either way.

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prepaid API

Live Inference API rails

inference.tiyuvta.ai

Prepaid Inference API model rails
Model	In / cached / out (per 1M)	Peak tok/s	TTFT
GLM-5.3-Flash	$0.15 / $0.03 / $0.50	up to 179	not published

Prepaid inference API. Each model carries its own serving hardware and measurement conditions.

These are the shared endpoint, with other people's traffic on the same cards. A dedicated machine, yours or ours, does better than this. That gap is the argument for moving.

what we do

the work

Models tuned to your data, on hardware you control

We fine-tune open models on your schemas, your terminology and your language, build the pipeline around them, and put the whole thing on GPUs you rent or own. Then we stay for the year, and tune it again as better models ship.

Cut an LLM product's cost of goods: the same work, at self-hosting cost, on models tuned to it
Agent systems that do real work: customers, documents, internal knowledge
Fine-tuning on your data, delivered as weights you keep
Your own cards, our tuning, with the numbers proven on your hardware

Book 30 minutes with an engineer What we do

start here

Inference API

A key in minutes, prepaid, OpenAI-compatible, on our machines. If your workload is modest, this is the whole answer and it is cheaper than what you are probably running now.

Open models on the roster, ready to call
No hardware conversation required
The same tuning work we do for customers who outgrow it

Get a key

the evidence

Lab

We research the serving engine and tune it for each hardware and model. The writing is public, and every number we publish carries the machine it was measured on and the raw log.