01 / 06
Fixed, predictable cost
High-volume tasks run on infrastructure you control. Once a GPU node is busy, each extra request costs close to nothing — typically a fraction of equivalent API spend at scale.
Paying per token for every internal summary, classification and chat reply adds up fast. Our self-hosted open-source LLM deployment puts models like Meta Llama, Mistral, DeepSeek, Qwen and Gemma on your own cloud account or GPU servers — so high-volume workloads run at a fixed infrastructure cost instead of a growing API bill.
We handle the hard parts: choosing the right model size for each task, quantisation so it fits affordable hardware, optional LoRA fine-tuning on your data, and an OpenAI-compatible endpoint so your existing code switches over with a config change. Monitoring, autoscaling and fallbacks to frontier models are included. We start with a free working mockup on your real prompts, then quote a fixed price for the production rollout.
The problem
Frontier APIs are brilliant for hard reasoning, but most production traffic is repetitive: tagging, extraction, summaries, routing, first-draft replies. Paying premium per-token prices for that volume — and sending every request to a third party — is the most common hidden cost in AI products today.
Why it pays
Run Llama, Mistral, DeepSeek or Qwen on your own infrastructure behind an OpenAI-compatible API — and stop paying per token.
High-volume tasks run on infrastructure you control. Once a GPU node is busy, each extra request costs close to nothing — typically a fraction of equivalent API spend at scale.
We expose your models behind the same request format your code already uses, so moving a workload is a base-URL change, not a rewrite.
We benchmark Llama, Mistral, DeepSeek, Qwen and Gemma variants on your own prompts and pick the smallest model that meets your quality bar.
Prompts and outputs never leave your VPC or data centre — a prerequisite for many regulated and enterprise customers.
Optional LoRA fine-tuning teaches the model your terminology, formats and tone using your existing documents and tickets.
Hard or rare requests can still route to OpenAI, Claude or Gemini automatically, so you keep frontier quality where it matters.
How we deliver
Every engagement starts with a free working mockup, then a fixed-price build — no hourly drift, no surprises.
We map your current AI calls by volume, cost and difficulty to find which workloads are worth moving to open models.
We test candidate models on your real prompts and deliver a free working endpoint so you can compare quality side by side.
We provision GPUs, deploy the quantised models behind an OpenAI-compatible API, and wire in monitoring and fallbacks.
Optional fine-tuning, load testing and a documented handover — or ongoing management if you prefer.
Spec sheet
Fixed scope, agreed up front. You own everything we deliver — code, models, assets and documentation.
Use cases
Patterns we build again and again — adapted to your data, your tools and your customers.
Summaries, smart search and auto-tagging inside your app served by a self-hosted model, so margins don't shrink as customers use the feature more.
Generate and normalise product titles, attributes and descriptions for tens of thousands of SKUs without a per-token bill.
A private assistant over contracts, proposals and policies that never sends client documents to an outside provider.
Classify, route and draft responses for every incoming ticket on a model fine-tuned to your past resolutions.
Models & tools
Model-agnostic by design: we pick per task and switch when something better ships.
Packages
Fixed price, quoted after a short scoping call — and only after you've seen the free mockup.
Move a single high-volume task to a self-hosted model and measure quality and cost against your current API.
1 workload in production
A production inference platform serving multiple models and teams, with routing, monitoring and fallbacks.
Multi-model, multi-team
FAQ
Still curious? Ask AURA, or email us and a human replies within one business day.
For many production tasks — classification, extraction, summarisation, routine chat — modern open models like Llama, Qwen and Mistral perform very close to frontier APIs. For the hardest reasoning we usually keep a frontier model in the loop. The benchmark on your own prompts tells us exactly where the line is.
It depends on volume. At low usage, paying per token is often cheaper. At sustained high volume, self-hosting commonly cuts cost by 50–90% for the workloads moved. Our audit shows the break-even point for your traffic before you commit.
No. Most clients rent GPU instances in their existing cloud account (AWS, Google Cloud, Azure or specialist GPU clouds). We size the hardware to the smallest model that meets your quality bar, often using quantisation to fit cheaper cards.
Yes. We expose an OpenAI-compatible API, so most applications switch over by changing the base URL and model name. We help your developers test and cut over safely.
Yes. We use LoRA fine-tuning on your documents, tickets or examples to teach the model your terminology and output format. Your data and the resulting weights stay in your environment.
You choose. We hand over a documented, monitored stack your team can run, or we manage it for you, including model upgrades as better open models are released.
A working endpoint running a candidate open model against a sample of your real prompts, plus a side-by-side quality and cost comparison with your current provider. After that we quote a fixed price for production.
Keep exploring
Audit your OpenAI, Claude and Gemini spend and cut it with routing, caching, batching and open-source offload — quality held constant.
LLMs, search and assistants that run inside your own network — secure RAG on private data, with full audit trails.
Production-grade integration of OpenAI, Claude and Gemini — agents, tool use, evals, guardrails and multi-provider failover.
Chat with your company documents: cited, permission-aware answers from Drive, SharePoint, Notion and more — private by design.
Tell us the outcome you need. We build a working mockup of the render, workflow or agent — at zero cost — so you judge results, not pitch decks.