Train frontier LLMs on your own data.

Name: Inference.net Train
Brand: Inference.net

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.

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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.

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.

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