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Private LLM infrastructure

On-Premise LLM Deployment in Cambridge

The best On-Premise LLM Deployment in Cambridge, at the best available price.

$0 upfrontPay per accepted checkpointSenior engineer, no agency markup

You have the GPU and you have the model. What is missing is the leg in between: the model exposed as a reliable private API, reached securely from your application, returning data you can trust. That is the whole engagement.

Cambridge, at the centre of what is often called Silicon Fen, has a long record of turning university research into companies, from chip design to biotech and AI. Founders are frequently scientists or engineers commercialising research, with deep technical skill and less experience shipping software products.

Research data in Cambridge often comes with funder or partner terms that forbid external processing. A private model in your own environment respects those terms.

What you get

  • vLLM serving tuned to your GPU: quantisation on the native kernel path, KV cache sizing, continuous batching, prefix caching
  • A private network path with no public exposure, verified as a direct peer connection rather than a relay
  • Schema-constrained responses plus a deterministic QA gate, so a wrong answer is rejected rather than rendered
  • Documented baseline: tokens per second, time to first token, real concurrent capacity
  • Infrastructure as code in your repository, a runbook, and every credential held by you
Fixed-fee assessment, credited in full against the build.

No upfront payment and no escrow required. You hold every dollar until a checkpoint is delivered and accepted, and thirty days of defect correction is included. Scope and price are set on a call.

Invoiced in USD or GBP, payable by Wise or bank transfer.

Request a callback

You speak to the engineer who does the work. No sales rep, no deck.

No spam and no sales team. You talk directly to Neeraj.

Cambridge, England

Who builds here

University of Cambridge spinoutsChip design and hardwareBiotech and life sciencesAI and deep tech
Best fit in Cambridge

Fractional CTO Services

Cambridge spinouts are usually led by scientists with deep technical skill but no software leadership experience, which is exactly the gap a part-time CTO fills.

A single GPU server lit in a dark data-centre aisle, the hardware a self-hosted model runs on
Architecture diagram of a private LLM deployment inside your network: your application calls a backend gateway, which reaches a vLLM model server running an open-weights model on your GPU over a private encrypted network with no public endpoint. Every response passes a deterministic validation gate before it returns to the application. Secrets sit in a managed store, the model port is bound to a private interface, output is review-only, and third-party model APIs are not used.
The request never leaves your network, and nothing reaches your users without passing the validation gate.
How you pay

Get it built at $0.

That is not a discount. It is when you pay. The work is split into checkpoints with acceptance criteria written down before anything starts, and each checkpoint is invoiced only after you have seen it and accepted it. No deposit.

$0 to start
You hold every dollar until a checkpoint is delivered and you accept it. No approval, no invoice.
Fixed cost, unlimited features
Or hire the team outright: one fixed monthly cost, unlimited feature development, any stack.
The engineer takes your call
The person on your first call is the one who architects and writes it. No account managers, no bench time.

A US agency quotes $50,000 to $150,000 for the same build and asks for 40 to 50% of it before a line is written. Account managers, project managers, sales commission and bench time. None of it appears in your product.

How it works

Three stages, nothing hidden.

01

Fixed-fee assessment

Five business days from the day access is in place. Your stack examined end to end, existing work classified as preserved or replaced with reasons, the connection design, and acceptance criteria written as testable statements.

02

Implementation in checkpoints

Around three weeks. Each checkpoint has written acceptance criteria agreed before work starts, and is invoiced only after you accept it. Appoint an independent technical reviewer if you want one.

03

Handover and closeout

One real request through your real application, on synthetic data, passing every QA rule. Runbook, recorded handoff, and all our access removed with written confirmation.

Working in Cambridge

What actually applies here.

Regulation and data

UK GDPR applies as across the UK. Life sciences companies add the MHRA's rules for software and AI as a medical device, and clinical research brings Good Clinical Practice and data integrity expectations. For research data, the funder's terms and the university's agreements often restrict where data can be processed, which should be checked before any AI service is used.

Contracting and payment

You contract with an individual consultant based in India, not a UK company. Invoices can be issued in GBP or USD and paid by Wise or bank transfer. Your accountant will treat an overseas supplier in the usual way; any paperwork they need is provided before work begins.

Working hours

Full UK working-day overlap. Calls at whatever hour suits your team, and no waiting a day for an answer.

Can you work on software that may be a medical device?

We build with the documentation, traceability and testing a medical device route needs. Regulatory classification and submission are led by your regulatory team or adviser.

Can you help a spinout prepare for investor technical due diligence?

Yes. Typical work is documenting the architecture, tightening security and making sure the product does not depend on one person.

We already have hardware and a model running. Is that a problem?

It is the ideal starting point. Existing work is classified during the assessment as preserved unchanged, preserved with changes, or replaced, with reasons, and nothing is replaced without your written agreement.

Can it be fully air gapped?

Yes, including model and dependency mirroring, offline updates and local evaluation, with no outbound network access at all.

Who holds the accounts?

You do, throughout. Cloud accounts are created and held by you, our access is named and limited-privilege, and it is removed at handover with written confirmation.

Full detail

Private LLM Infrastructure

Self-hosted LLM deployment on hardware you own. vLLM serving, private networking, validated output.