Private & on-premise LLMs
Your own language model, on infrastructure you control
Navelo Software deploys private and local LLMs: open-weight language models running on your own servers, in your private cloud or on-device, so sensitive data never goes to a third-party AI provider. We choose the model, size the hardware, set up secure serving and connect it to your applications, including retrieval over your private documents.
At a glance
- Engagement
- Fixed-scope projects or ongoing product teams
- Cadence
- Live demo and working build every week
- Clients
- Startups and businesses worldwide
- You own
- 100% of the code, designs and accounts
Overview
Some data cannot be sent to a public API: patient records, legal files, source code, financial data or anything covered by strict contracts. Open-weight models have become capable enough for many business tasks, which makes running them privately a practical option rather than a research project.
The trade-offs are real: hardware cost, operations work and capability gaps against the largest hosted models. We start with a side-by-side test on your own tasks, so the decision between private, hosted or hybrid is based on evidence.
What you get
What’s included. End to end.
01
Model selection on your tasks
Candidate open-weight models benchmarked on your real prompts for quality, speed and memory use.
02
Hardware and cost sizing
GPU or CPU requirements, quantisation options and a cost comparison with hosted APIs.
03
Secure serving
Inference servers inside your network or VPC, with authentication, rate limits, logging and no outbound data.
04
OpenAI-compatible API
A standard API so existing tools and applications can switch to the private model with minimal changes.
05
Private retrieval (RAG)
Your documents indexed and searched entirely inside your environment, with permission filters.
06
Fine-tuning when it pays off
Lightweight adaptation on your own examples where it measurably improves a narrow task.
- Open-weight models
- On-premise & VPC hosting
- GPU sizing
- Quantisation
- OpenAI-compatible APIs
- Private RAG
- Fine-tuning
- On-device AI
Typical technology
- Llama, Qwen, Gemma & Mistral models
- vLLM
- Ollama & llama.cpp
- Docker & Kubernetes
- NVIDIA GPUs
- PostgreSQL & pgvector
Indicative only: we choose the stack per project, around your team, budget and existing systems. See how we work.
What it means for you
01
Data stays in-house
Prompts, documents and outputs never leave infrastructure you control.
02
Predictable cost at volume
Fixed infrastructure cost instead of per-token pricing for high-volume workloads.
03
No vendor dependency
Models you can keep running, version and audit on your own schedule.
Related work
Built by our team. Open the details.
Docuvia and Agentry are Navelo Software concept builds (not client projects) designed to run on hosted or private models. CyclePrivate is our own live app, which keeps all sensitive data on the user's device.
Process
How we work. Week by week.
- 1
Requirements
Data sensitivity, workloads, latency needs and where the model must run.
- 2
Bake-off
Private and hosted models compared on your tasks, with costs.
- 3
Deploy
Infrastructure, serving, security and monitoring set up in your environment.
- 4
Integrate
Applications, assistants and workflows connected to the private endpoint.
- 5
Operate
Model upgrades, capacity planning and performance tuning over time.
Pricing
Every product is different. So is every quote.
We don’t sell packages. We scope private & local llm deployment around your goals and send a fixed quote you can plan around.
- Step 1
Tell us about it
A few lines on the product, users and timing.
- Step 2
Scoping call
Goals, must-haves and constraints — a conversation, not a pitch.
- Step 3
Fixed quote
A written scope, timeline and price. Yours to keep.
Why Navelo Software
Why teams choose us.
- We benchmark on your tasks before recommending hardware.
- Honest about where private models fall short of the largest hosted ones.
- Security and network isolation designed in from the start.
- Standard APIs, so applications are not tied to one model.
- Experience shipping privacy-first and on-device products.
FAQ
Common questions.
What is a local or private LLM?
A private LLM is a language model that runs on infrastructure you control, such as your own servers, a private cloud account or an employee's device, instead of being called through a public AI provider's API. Prompts and data stay inside your environment.
Are open-weight models good enough for business use?
For many tasks, yes: summarisation, extraction, classification, drafting and question answering over your documents. The largest hosted models still lead on the hardest reasoning and coding tasks, so we test on your work before you commit.
What hardware do we need?
It depends on the model size, the number of users and the response speed you need. Small models run on a single modern GPU or even a laptop; larger models need multi-GPU servers. We size it after testing, and compare it to the cost of a hosted API.
Can we combine private and hosted models?
Yes. A common pattern routes sensitive or high-volume requests to a private model and sends the rest to a hosted frontier model, behind one API.
How much does a private LLM deployment cost?
Every product is different, so we don't publish fixed prices. After a short discovery conversation about your goals, users and must-have features, Navelo Software sends a written scope with a fixed quote and timeline. Get a quote and you'll hear back within one working day.
Do I own the code and designs?
Yes. Everything we create for your project — source code, designs, documentation and accounts — belongs to you and is delivered into repositories and accounts in your name.
Do you work with clients in other countries and time zones?
Yes. Navelo Software works with founders and product teams worldwide. We run projects asynchronously with written updates, shared boards and a weekly live demo scheduled at a time that suits your team.
Related services
- Customer and internal assistants grounded in your own data, with citations and permission-aware retrieval.AI Chatbots & RAG Development
- Infrastructure as code, CI/CD, monitoring, security and cost control across major clouds.Cloud & DevOps
- Custom AI agents that use your tools and data to complete multi-step work, with guardrails built in.AI Agent Development
Guides from the Journal
- AI Development · 6 minLocal LLM vs Cloud LLM: Privacy, Cost and Performance Compared
- AI Development · 7 minRAG vs Fine-Tuning: How to Choose for Your AI Product
- AI Tools · 4 minHow to Choose an LLM for Your Business: A Practical Framework
- AI & Product · 12 minOn-Device AI in 2026: Why Every App Is Going Local-First
- AI Governance & Security · 5 minAI Governance for Small and Mid-Sized Businesses: A Practical Checklist
- Security · 10 minThe Rise of Zero-Knowledge Apps: Encryption as a Product Feature
Work with us
Planning private & local llm deployment? Let’s scope it.
Tell us what you're building. We reply within 24 hours, and once the scope is clear you get a written plan, timeline and fixed quote — no obligation.