Which open-source LLM is best for enterprise use?

It depends on the task, languages, latency target and hardware budget. We benchmark candidates such as Llama, Mistral, Qwen and DeepSeek against your own data and recommend the smallest model that meets the bar.

How long does it take to deploy a production-grade private LLM?

A focused pilot on existing infrastructure typically takes a few weeks; a full multi-team platform with RAG, access control and monitoring takes longer. You get a timeline with the architecture document, before build starts.

Can the LLM be connected to our internal documents and knowledge base?

Yes. We build a RAG pipeline over your documents, wikis and databases, with permissions respected at retrieval time, so people only get answers from sources they are allowed to see.

What happens if we want to switch to a different model later?

The serving layer exposes an OpenAI-compatible API and models sit behind a registry, so swapping or A/B testing a model is a configuration change, not a rebuild.

How is this different from Azure OpenAI Service or AWS Bedrock?

Those are managed services and a good fit for some teams. Private infrastructure gives you full control of model versions, weights, network boundaries and cost, and works fully air-gapped. We will tell you honestly if a managed service fits you better.

Can you fine-tune the LLM on our proprietary data?

Yes, using LoRA or QLoRA inside your environment, with experiment tracking, evaluation and a model registry. Often RAG alone gets you most of the way, and we will test that first.

How do you handle model updates and ongoing maintenance?

New model versions are evaluated against your golden set before rollout, and released in stages with rollback. Every project includes 60-day post-launch support, with ongoing plans after that.

Is private LLM infrastructure only for large organisations?

No. Smaller teams with sensitive data or high, steady usage often see the clearest payback, because a single well-sized model can replace a fast-growing API bill.

What do you need from us to get started?

A short call, a description of the use cases you have in mind, and a contact on your infrastructure or security team. We take it from there.

Enterprise LLM Infrastructure

Private LLM Infrastructure That Never Leaves Your Network

Your proprietary data should not travel through someone else's API. We design, deploy and run large language models on your own servers, inside your own cloud account, or fully air-gapped, so you keep the data, the model and the cost curve under your control.

Book a Discovery Call See What We Build

500+ projects · 85+ engineers · 30+ countries

On-premise
Private VPC
Air-gapped
Hybrid

On-premise
Private VPC
Air-gapped
Hybrid

Who this is for

Your Business Has Outgrown Public AI APIs

A processor chip lit against a dark background

Most teams start with a hosted LLM API, and for a prototype that is the right call. Then usage grows. Sensitive records start flowing through a third party's servers, the monthly token bill climbs faster than the value it creates, and compliance starts asking questions nobody can answer with a vendor's terms of service.

You also lose control in quieter ways: the model behind the endpoint changes without notice, rate limits appear at the worst moment, and latency is set by someone else's queue. That is usually the point where patching stops working and real infrastructure starts paying for itself.

Data Privacy Risk

Every prompt sent to a public endpoint leaves your network. For patient records, financials, contracts or strategy documents, that is not an acceptable default.

Compliance Exposure

GDPR, HIPAA, SOC 2 and sector rules demand data residency and access control that shared public APIs rarely give you out of the box.

Unpredictable Costs

Per-token pricing feels cheap at pilot volume. At enterprise volume it compounds. Owned infrastructure turns that into a fixed, forecastable line item.

Vendor Lock-In

When one provider's price change, rate limit or policy update can stall your product, your roadmap is not really yours. Private infrastructure gives it back.

Latency On Critical Paths

Round trips to an external API add latency to every AI-powered step. Inference close to your data and users is measurably faster for real-time workflows.

No Control Over Model Behaviour

Hosted models are updated on the provider's schedule. On your own stack you choose which model version runs, and exactly when it changes.

If two or more of these sound familiar, you are ready for private LLM infrastructure.

What we build

Enterprise LLM Infrastructure Services We Deliver

From the first GPU node to day-two operations, every layer is built for security, scale and reliability, and handed over documented.

Private LLM Deployment
Run large language models entirely inside your own environment. No data leaves your network, no shared compute, no third-party access.
We stand up the model on dedicated servers or isolated cloud capacity, wire in access control and connect it to your internal systems. Your team gets a private assistant that behaves like the public tools, built on open or licensed models you choose, with full data ownership.
Best for: Organisations with sensitive data, strict compliance, or query volumes that make public APIs uneconomical.
On-Premise LLM Deployment
Keep the model physically inside your own data centre: the highest level of control available, with no internet dependency and no cloud exposure.
We handle GPU server configuration, model installation, inference optimisation and internal API setup. Your teams reach the LLM through a secure internal endpoint, and nothing leaves the building.
Best for: Healthcare, finance, legal and government teams where data residency and air-gapped operation are non-negotiable.
VPC And Private Cloud LLM Deployment
Not every enterprise needs its own hardware. If you already run on AWS, Azure or GCP, we deploy your LLM inside a Virtual Private Cloud, sealed off from the public internet.
We configure the VPC, deploy the model behind private API endpoints and set network policies that block unauthorised access. You get cloud elasticity with the privacy profile of an on-premise install.
Best for: Teams that want cloud scalability without putting AI workloads on shared infrastructure.
Open-Source LLM Hosting
You do not have to pay per token for a capable model. Open-weight families such as Llama, Mistral, Mixtral, Qwen, Phi and DeepSeek now match commercial APIs on many enterprise tasks.
We benchmark candidates against your real use case, pick the right one, and host it on your infrastructure with tuned inference settings: a high-performing system with no recurring API fees and full control over the weights.
Best for: Cost-conscious enterprises, high-volume AI products, and teams that want domain models without licensing limits.
LLMOps And Model Serving
Deploying a model is the start. Running it reliably at enterprise scale takes serving infrastructure, monitoring and operational guardrails.
We cover the production stack: serving with vLLM or Hugging Face TGI, load balancing across inference nodes, request batching for throughput, rate limiting, multi-model routing and traffic-based autoscaling, plus dashboards for latency, error rate and usage in real time.
Best for: Organisations that need production SLAs, uptime commitments and scaling without manual intervention.
RAG Infrastructure And Knowledge Base Setup
Retrieval-Augmented Generation lets the LLM answer from your own documents, databases and internal knowledge, without retraining the model.
We build the whole pipeline: document ingestion and parsing, embedding generation, a vector store (Qdrant, pgvector, Weaviate or Pinecone), retrieval and re-ranking logic, and the link to your serving layer. Answers come back grounded in, and cited to, your own sources.
Best for: Enterprises that want the LLM to work over manuals, policies, contracts or proprietary datasets, privately.
Internal ChatGPT For Enterprises
Give every team a private assistant that works like ChatGPT but runs entirely on your infrastructure, grounded in your knowledge and visible only to your staff.
We deliver the full stack: the LLM backend, an OpenAI-compatible internal API, a chat interface people can use on day one, role-based access control, and integrations with Slack, Microsoft Teams or your intranet.
Best for: Companies lifting productivity across HR, legal, finance, engineering and support without sending internal queries to public AI.
LLM Fine-Tuning Infrastructure
Fine-tuning on proprietary data needs more than a training script: it needs a secure pipeline, clean data handling, a model registry and version control.
We build that foundation: isolated training environments, LoRA and QLoRA pipelines, experiment tracking, evaluation harnesses and a model registry, so your team can ship, compare and roll back model versions with confidence.
Best for: Organisations with proprietary datasets that want a domain model clearly ahead of a general-purpose LLM on their tasks.

Not Sure Which Setup Fits Your Needs?

Most enterprises need more than one of these working together. We map your current systems, find where private AI pays back fastest, and recommend the right combination for your environment.

Book an Enterprise AI Assessment

Recognised & certified

Industries we specialise in

LLM Infrastructure For Industries That Cannot Afford To Get AI Wrong

Every deployment is shaped around the data sensitivity, compliance rules and daily workflows of the sector it runs in.

A radiologist reviewing medical scans across multiple diagnostic monitors

Private LLMs for

Healthcare

Keep patient data inside your network while giving clinical and admin teams a capable AI assistant.

HIPAA-aligned clinical note summarisation
Discharge summary and patient record drafting
Medical coding and billing document processing
Private RAG over EHR exports and clinical protocols
Radiology and pathology report summarisation

Explore AI for Healthcare

How we work

How Axiomra Builds Your Enterprise LLM Infrastructure

Every environment is different. The process accounts for your existing infrastructure, compliance requirements and business goals, from the first call to a fully operational LLM platform.

Step 1 of 8

Discovery And Requirements

We map your data, users, compliance obligations and the workloads you want to run, and agree what success looks like in numbers before anything is built.

What we work with

The Stack Behind Every Private LLM Deployment

Production-tested tools, chosen per project for performance, security and the environment you deploy into.

Llama
Mistral
Gemma
Qwen
DeepSeek
Claude

The Right Tools. The Right Team. Built For Your Stack.

Knowing the frameworks is table stakes. Combining them into a system that holds up in production is the job. Tell us what you want to run and we will map the architecture.

Book a Free Technical Discovery Call

Why Axiomra

What You Actually Get When You Work With Us

Rolling archive shelving units in a records room

Plenty of AI vendors will take the project. Fewer have put private models into regulated, high-stakes production and kept them healthy afterwards. Three habits separate how we work.

We scope for production from the first call, we own every layer from GPUs to the chat window, and we stay on after launch, because models drift, data shifts and usage grows in directions nobody predicted on day one.

Built For Production, Not Demos

A working notebook is not a system. Every engagement ships with evaluation, monitoring, rollback and a documented handoff, because that is where most enterprise AI stalls.

One Team, The Full Stack

Most vendors own one layer. We cover data architecture, model selection, deployment, serving and operations, so you never coordinate three suppliers to ship one system.

Engaged After Go-Live

Structured post-launch support: monitoring, re-evaluation against your golden set and proactive recommendations when performance signals change.

Projects delivered: 500+
In-house engineers: 85+
Countries served: 30+
Partnerships: 170+

What clients say

Leaders Who Trust Our Engineering

“They communicated with me and we developed trust over the years. Project management is great: the willingness to take any problem and get through it is impressive.”

Adam GawronFounder, Upstar

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BULL
Deep Duck
Fashionnet
Fluent Talk AI
FormOlé
Front Office
GPT Tune
Picture Perfect
BULL
Deep Duck
Fashionnet
Fluent Talk AI
FormOlé
Front Office
GPT Tune

Build Your Private LLM Infrastructure With A Team That Has Done It Before

If you are handling sensitive data, hitting API cost ceilings, or working toward compliance requirements a public AI service cannot meet, this is the right conversation.

We have built private LLM platforms across regulated and high-growth industries, and we know how to get you there without unnecessary complexity or cost.

Book an Enterprise AI Assessment

Private LLMs, answered

Frequently Asked Questions

No. We can deploy into GPU capacity in your own AWS, Azure or GCP account (VPC), into a private cloud provider, or onto hardware you own. We size the options during discovery so you can compare cost before committing.