---
title: "Inference.net | Inference infrastructure for AI-native teams"
description: "Inference infrastructure for AI-native teams. Run, monitor, and optimize production AI workloads with lower cost, faster latency, and dedicated support from Inference.net."
url: "https://inference.net/"
---

# Inference infrastructure for AI-native teams

Blazing fast inference for open-source, custom, and fine-tuned AI models at massive scale. Automatically capture and optimize your LLM performance in production.

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

Trusted by the world's best engineering teams.

## Serve frontier AI models with confidence.

Switch from OpenAI, Anthropic and Gemini to open-source models running on infrastructure optimized specifically for your workload. Everything you need to monitor existing providers, evaluate new models, and deploy at scale.

Monitor

### Production-grade monitoring for any model on every provider.

[Learn more](https://docs.inference.net/get-started/capture-first-trace) [Get Started](https://docs.inference.net/get-started/capture-first-trace)

Deploy

### Deploy models from our catalog, or train your own. 99.99% uptime.

GLM-5.2

[Deploy](https://inference.net/register/)

Kimi K2.6

[Deploy](https://inference.net/register/)

MiniMax-M2.5

[Deploy](https://inference.net/register/)

GPT-OSS 120B

[Deploy](https://inference.net/register/)

[Learn more](https://inference.net/register/) [Start Deploying](https://inference.net/register/)

Train

### Fine-tune custom frontier-level language models in minutes

[Learn more](https://docs.inference.net/platform/train/overview) [Training Guide](https://docs.inference.net/platform/train/overview)

Evaluate

### Continuously evaluate models against production traces

Model Cost Quality

Custom Model $25K

9.1

GPT 5.2 Pro $138K

8.3

Claude Opus 4.6 $190K

8.2

Gemini 2.5 Pro $74K

7.1

DeepSeek v3.2 $41K

6.8

[Learn more](https://docs.inference.net/platform/eval/overview) [Eval Guide](https://docs.inference.net/platform/eval/overview)

## Cutting-edge LLM performance research for unmatched quality, speed and uptime

Learn how teams like GravityAds and Cal AI use Inference.net to deploy, observe, evaluate, and train GPT-5 quality models at low cost and lightening fast speed. We handle the infrastructure, so you don't have to.

### Faster than Cerebas

Learn how Gravity Ads used Cataylst Train and Deploy to cut p-90 round-trip latency from 900ms to 240ms.

[Learn more](https://inference.net/register/)

### High intelligence. Low cost

Frontier-quality models that product teams demand. Pricing your finance team will love

[Learn more](https://inference.net/case-study/wynd-labs/)

### Your private data flywheel

Create compounding LLM flywheels by observing production traces and training on new data when needed.

[Learn more](https://inference.net/blog/the-model-you-need-doesnt-exist-yet/)

OBSERVE

## LLM Observability for any model on any provider.

Your data, your moat. Quickly add powerful observability to your existing LLM pipelines to store requests and generate insights. Install in 5 minutes.

#### Trace every request path

See prompts, tool calls, responses, full traces, and downstream provider behavior in one place.

#### Monitor what matters

Track latency, reliability, usage patterns, and quality signals as your traffic scales.

#### Search and debug faster

Find patterns across events, isolate failure modes, and move from symptom to root cause quickly.

#### SOC 2 Type II

Fully SOC 2 compilant. Full control and operational oversight of your data and models across the entire stack.

MODEL TRAINING

## Specialized Language Models built for production workloads

Fine-tune frontier-quality language models tuned to your quality, cost, and latency targets — so you get better performance with less compute.

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

#### Automatic fine-tuning workflows

Targeted improvements for your domain, your tasks, your quality objectives. Training workflows tailored to the specific patterns your model needs to learn.

#### Curate training data on autopilot

Move from observed traces and eval failures to high-signal training datasets in minutes. Production data to training-ready samples without manual curation or data wrangling.

#### Validate before you promote

Evaluate new model variants against baseline behavior automatically. Know exactly what improved and what didn't before a single user sees the new model.

#### Retrain as your product evolves

Set up continuous improvement loops that retrain on fresh production data as your user base grows and your use cases shift. Your model gets better every cycle.

AGENTS

DATA EXTRACTION

CLASSIFICATION

EVALUATE

## Make model decisions based on evidence, not vibes.

Catalyst Evaluate turns production traces into continuous model improvement workflows. Measure behavior against your standards, detect regressions early, and prioritize exactly what to improve.

### Build and run evals on production traffic

Convert observed traces into evaluation datasets that reflect real user behavior.

### Score quality across any model or metric

Combine automated scoring, task-specific checks, and human review for a 360° view of model quality.

### Automatic fine-tuning and evaluation that just works

Use your production traces and evaluation data to train and evaluate frontier models in minutes.

OPEN SOURCE

## Our Workhorse Models

### Cliptagger

Designed for reasoning and complex problem-solving tasks, offering advanced capabilities for structured output generation and complex reasoning.

[Try Model](https://inference.net/models/cliptagger-12b/)

### Schematron

Designed for reasoning and complex problem-solving tasks, offering advanced capabilities for structured output generation and complex reasoning.

[Try Model](https://inference.net/models/schematron-v2-turbo/)