---
title: "On-Premise LLM Deployment"
description: "Stand up self-hosted LLMs so sensitive code and data never leave your infrastructure."
url: https://www.callstack.com/services/on-premise-llm-deployment
updated: 2026-11-09T15:58:35.723Z
---

# On-Premise LLM Deployment

Stand up self-hosted LLMs so sensitive code and data never leave your infrastructure.

_[Agentic Delivery](/agentic-delivery) › [Custom & Private Models](/capabilities/custom-private-models)_

**Serious AI capability inside your walls**

For some environments, external inference is a non-starter. We deploy self-hosted models on your infrastructure - hardware sizing, serving stack, access controls - so engineers get real AI capability and data never leaves.

## Custom & Private Models

- 30/31 Next.js tasks as pixel canary · 85% Lower inference cost vs Opus 5.5
- Industry runs on our tooling · 50M+ OSS downloads / month
- 100+ Enterprise projects delivered
- React Foundation Founding Member · 300+ commits to React Native · Contributing since 2016
- ISO 27001 certified
- 32 Models tested against React Native

## What you get: Self-hosted, production-grade.

- **Model selection for the job** Open-weight models benchmarked on your real tasks before hardware is committed.
- **Serving infrastructure** A production serving stack with quantization and GPU sizing matched to your workloads.
- **Workflow integration** Self-hosted models wired into agents, IDE assistants, and CI.
- **Access and audit controls** Authentication, rate limits, and full request logging from day one.
- **Cost and capacity model** Realistic throughput and total cost of ownership against API pricing.
- **Model upgrade path** A defined process to evaluate and swap models as open weights improve.

## Scope → Build → Scale: Deploy models where the workload fits.

### Scope (1 WEEK)
We select a product workflow, define where agents act, and agree the controls and success measures.
- Workflow and owners defined
- Baseline and controls agreed
- Token budgets confirmed
### Build (1-12 MONTHS)
We connect context, agents, and verification to your tools, then run the workflow on your product backlog.
- Running on your product backlog
- Speed and quality measured
- Business impact validated
### Scale (AS NEEDED)
We document the workflow, transfer ownership, and extend it across teams and products based on results.
- Your team owns the workflow
- Expand based on results
- Full knowledge handover

**Own the model. Keep the data.** Self-hosted LLM infrastructure sized, deployed, and integrated for engineering use. [Plan my model strategy](/contact)

> Their engineers proved to be a crucial part of our mobile app relaunch project.
>
> — Brian Aznar, Engineering Director, Major League Soccer

We’ll connect you with the right expert within 24 hours.

## Let’s find the right path for your product.

**How close are open models to the hosted frontier?**

The gap is real but narrows constantly, and it is task-dependent. For many coding workflows, current open weights are strong - the evaluation phase measures it on your tasks before you spend on hardware.
**What hardware do we need?**

Depends on models and concurrency. Serious engineering use typically means a small GPU cluster; the capacity model sizes it precisely rather than by vendor rule of thumb.
**Can this run air-gapped?**

Yes. Fully offline deployment is the point for some clients - models, tooling, and updates all move through your controlled channels.
**Who maintains it?**

Your team, with runbooks and monitoring we set up - or we keep an operations retainer. Model swaps are a process, not a project.
**Is it cheaper than APIs?**

At sustained high volume, often yes; below that, rarely. The TCO model gives you the crossover point instead of a slogan.
**Can we fine-tune on top of it?**

Yes. Self-hosted infrastructure pairs naturally with fine-tuning on your codebase - that is a dedicated offering we run on the same stack.

## Open source

- [React Native AI](https://github.com/callstackincubator/ai): On-device LLM execution in React Native with Vercel AI SDK compatibility
- [React Native Evals](https://github.com/callstackincubator/evals): A benchmark suite for evaluating how coding models solve real React Native tasks
- [Skill Gym](https://incubator.callstack.com/skillgym/): Prove your agent skills work before you ship them