The Enterprise AI Gap
Most enterprises face the same three challenges when adopting LLMs at scale.
Data Risk
Sending prompts to third-party APIs exposes sensitive enterprise data to external servers you don't control.
Cost Spiral
API costs grow uncontrollably at scale. Per-token pricing makes budgeting unpredictable.
No Ownership
You're renting intelligence you can't audit, customize, or run without an internet connection.
The Full Stack for In-House AI
Three products that work together to give you complete ownership of your AI infrastructure.
Inferia LLM
The Operating System for LLM Inference
Deploy any open-weight model on your own infrastructure. Full control over weights, configuration, and runtime — with no external dependencies.
Learn more →Inferia Proxy
Control Every Token
A unified gateway that routes, throttles, and observes every LLM request across your organization. Set budgets, enforce policies, and audit usage.
Learn more →Inferia Accelerate
Maximum Performance, Minimum Hardware
Custom GPU kernels and quantization tooling that unlock dramatically more throughput from your existing hardware — without sacrificing quality.
Learn more →Inferia LLM
Air-Gapped Deployment
Run LLMs in fully isolated environments with zero internet dependency. Complete control over your data.
Inferia Proxy
Spend Controls
Set per-user, per-team, and per-project budgets. Get alerts before overspend, not after.
Inferia Accelerate
3x Throughput
Custom GPU kernels that extract significantly more performance from your existing hardware.
3x
Faster inference
100%
Air-gapped capable
Zero
Vendor lock-in
"Inferia gave us complete control over our LLM infrastructure without sacrificing performance."VP of Engineering Enterprise Customer
"We cut our inference costs by 60% while keeping everything on-premise."CTO Enterprise Customer
Master the LLM Era
The Inferia LLM Playbook is your complete guide to deploying and operating large language models in the enterprise — from fundamentals to production.
What Are LLMs?
Understand the fundamentals of large language models and how they work.
Running LLMs
Learn the hardware, software, and operational requirements for running models.
LLM Deployment
Best practices for deploying LLMs in production enterprise environments.
From the Blog
Why Enterprises Are Moving LLMs In-House
The case for private LLM deployment is stronger than ever. Here's why forward-thinking enterprises are taking control.
Quantization Explained: INT4, INT8, and FP8
A practical guide to model quantization formats — what they mean for performance, quality, and hardware compatibility.
Introducing Inferia LLM v1.0
We're launching Inferia LLM — the operating system for enterprise LLM inference. Here's what it can do.