Drop-in OpenAI replacement · 5 SOTA open-weight models

Your own fine-tuned AI.
Trained in 5 minutes. Yours forever.

You're paying GPT-4o prices for a model that doesn't know your domain, your docs, or your customers. Every generic API call is money spent renting intelligence you'll never own. UltimateModel fine-tunes a state-of-the-art open-weight model on your data and serves it behind an OpenAI-compatible endpoint — change one base URL, cut inference cost 60–80%.

Train your first modelView the API docs
0 minavg fine-tune
0%cheaper than GPT-4o
<0 msp99 latency
your_app.py
1from openai import OpenAI
2 
3- client = OpenAI(base_url="https://api.openai.com/v1")
4+ client = OpenAI(base_url="https://api.ultimatemodel.ai/v1")
5 
6response = client.chat.completions.create(
7model="your-fine-tuned-model",
8messages=[...]
9)

The problem

Generic AI is a tax you pay on every single request

You're renting intelligence you'll never own

Every token runs through a model you don't control, can't export, and can't tune to your domain. When prices change or the API deprecates a model, your product changes with it — and you have zero say.

Off-the-shelf models don't know your business

A general-purpose LLM has never read your docs, your support history, or your internal knowledge. You bolt on giant prompts and RAG pipelines to compensate, and still get confident, generic answers that miss what makes you different.

Custom AI means hiring an ML team you can't afford

Fine-tuning, RL, eval pipelines, and GPU orchestration normally require specialists and months of infrastructure work. For most teams, a truly custom model stays permanently out of reach.

How it works

From data to deployed model in under 10 minutes

01

Upload your data

PDFs, text files, URLs, audio. Your support docs, product manuals, internal knowledge — anything that defines your domain.

02

Fine-tune in ~5 minutes

LoRA fine-tuning on a SOTA 2025/2026 open-weight model. Qwen3 0.6B on a typical dataset completes in about 5 minutes.

03

Deploy with one line change

OpenAI-compatible API endpoint. Change only your base_url — your existing SDK code works without modification.

04

Improve automatically

GRPO online RL and DPO preference optimization continuously improve your model from real user feedback. No labelers required.

See it in action

A model that actually knows your domain

Fine-tuned on product documentation

How do I reset my API key?

OpenAI-compatible endpoint · <200ms p99

The flywheel

Your model gets smarter with every interaction

Most AI products collect feedback that never makes it back into the model. UltimateModel closes the loop: real user interactions feed GRPO online RL and DPO preference optimization pipelines, continuously improving future behavior without a manual labeling operation.

GRPO online RL — same technique as DeepSeek-R1
DPO preference optimization from real feedback
A/B test adapter versions in production
Synthetic data distillation for faster iteration

Built for production

GDPR compliant, isolated encrypted GCS per tenant
Encrypted at rest and in transit — AES-256 / TLS 1.3
OpenAI-compatible API — change only base_url
100% data ownership — export weights anytime
5 SOTA open-weight models: Qwen3, Gemma 4, Phi-4, Llama 3.2, SmolLM3
<200ms p99 inference latency

Cost comparison

Open-weight inference vs GPT-4o

Cost per 1M tokens — typical production pricing

$0$1$2$3$4$5$6COST PER 1M TOKENS$5.00GPT-4o$1.00Your fine-tunedopen-weight model80% cheaper

Based on publicly available API pricing. Actual savings vary by use case.

Under the hood

State-of-the-art techniques, abstracted away

Pricing

Pricing that scales with your model

Start with a paid plan. Own your weights on every tier. Cancel whenever you want.

Frequently asked questions

Stop renting a model that isn't yours.
Build one that is.

Every day on a generic API is another day paying premium prices for a model that doesn't know your domain — and another day a competitor could spend training one that does. The first version of your custom AI is five minutes away.

Train your first modelBook a demo instead

Change one base URL. Keep your weights forever. Cancel anytime.