---
title: "Train | Inference.net"
description: "Train frontier LLMs on your own data. Turn production data into specialized models that deliver 90% lower cost and 5x lower latency with frontier-level quality."
url: "https://inference.net/products/train/"
---

# Train frontier LLMs on your own data.

Turn production data into specialized models that deliver 90% lower cost and 5x lower latency—with frontier-level quality on your exact workload. Go from data to deployed model in days, not months.

[Talk to an Engineer](https://inference.net/meet-with-us/) [Get Started](https://inference.net/register/)

Trusted by the world's best engineering teams.

## General models plateau. Specialized models compound.

Most teams don't need a bigger model. They need a model that does their job consistently, cheaply, and fast in production. Training on your own data is how you get there.

### 5x Lower Latency

A smaller, task-tuned model delivers faster responses with more predictable p95/p99 latency than a general-purpose frontier API.

### 90% Lower Cost

Specialized models cut tokens and compute. As traffic scales, the cost gap between a custom model and a frontier API compounds.

### Frontier-Quality Output

Training on your production data improves accuracy and consistency on the exact tasks your app runs every day—without sacrificing quality.

The Process

## From production data to deployed model.

Train a custom model end-to-end in the platform—no pipeline work, no external tooling.

### 1. Start with real data

Use production traces from Inference.net Observe or bring your own dataset. Real usage data is the foundation for a model that works in production, not just on benchmarks.

### 2. Curate training signal

Turn raw traces and eval results into high-signal training datasets. The platform handles formatting, deduplication, and quality filtering automatically.

### 3. Train and validate

Run fine-tuning workflows benchmarked against your evals. Iterate until the model consistently beats your current baseline on the metrics that matter.

### 4. Deploy and monitor

Push directly to Inference.net Deploy on dedicated infrastructure. Production telemetry flows back into Observe automatically—closing the loop for the next training cycle.

> “Our custom model is more accurate, more affordable, and cut request latency by more than 50%. The whole experience was a breeze, and the inference.net team was great to work with.”

Henry Langmack

Co-founder, CTO @ Cal AI

The Flywheel

## Every product in the platform makes the others better.

Train doesn't exist in isolation. It's one stage in a continuous loop where production data becomes model improvement—automatically.

[Get Started](https://inference.net/register/)

#### Observe → Training Data

Production traces captured in Observe become the raw material for your next training run. Real usage data, not synthetic benchmarks, drives every improvement.

#### Evaluate → Training Signal

Eval failures and quality regressions surface exactly where your model needs work. Evaluation results feed directly into dataset curation—no manual triage.

#### Train → Better Models

Fine-tune on the high-signal data your product generates. Each training cycle produces a model that's more accurate, faster, and cheaper to run than the last.

#### Deploy → Production Feedback

Push improved models to dedicated infrastructure with one click. Every request feeds back into Observe, starting the next cycle of the flywheel.

## Stop renting intelligence.

Build specialized models trained on your data that outperform frontier APIs on your workload—at a fraction of the cost. Need help with a complex training? Our research team works directly with your engineers.

[Talk to an Engineer](https://inference.net/meet-with-us/) [Get Started](https://inference.net/register/)