You will get I will set up local AI/LLM infrastructure to cut your cloud AI costs
Project details
Running AI in the cloud gets expensive fast, and every request leaves your infrastructure. I set up local model infrastructure on your own hardware, with automatic failover to a cloud model when needed, so you get lower cost and full data control without losing reliability.
I built and tested this exact pattern myself: a gateway that routes between Claude and a local model (Ollama), always trying the cheap/local option first and falling back to cloud only on a real failure — not a demo, a tested component inside a larger agent system I run, with automated tests proving the failover actually works.
What you get: Ollama installed and configured on your hardware, a model matched to your use case and hardware limits, and — if you want it — the same gateway pattern wired into your existing workflow so switching between local and cloud is invisible to whatever you're building.
Industry data: on-premise inference runs roughly 18x cheaper per token than cloud APIs at volume, and keeping data on your own infrastructure removes a real compliance risk under GDPR or similar rules.
I built and tested this exact pattern myself: a gateway that routes between Claude and a local model (Ollama), always trying the cheap/local option first and falling back to cloud only on a real failure — not a demo, a tested component inside a larger agent system I run, with automated tests proving the failover actually works.
What you get: Ollama installed and configured on your hardware, a model matched to your use case and hardware limits, and — if you want it — the same gateway pattern wired into your existing workflow so switching between local and cloud is invisible to whatever you're building.
Industry data: on-premise inference runs roughly 18x cheaper per token than cloud APIs at volume, and keeping data on your own infrastructure removes a real compliance risk under GDPR or similar rules.
AI Algorithms
Large Language ModelAI Applications
AIOpsAI Development Language
PythonAI Tools
Hugging Face, PyTorchAI Models
OpenAI CodexWhat's included
| Service Tiers |
Starter
$120
|
Standard
$400
|
Advanced
$900
|
|---|---|---|---|
| Delivery Time | 3 days | 7 days | 14 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | - | |
Detailed Code Comments | - | - | - |
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | - | - |
Model Documentation | - | - | - |
Model Monitoring | - | - | - |
Model Testing & Optimization | - | - | - |
Model Tuning | - | - | - |
Natural Language Processing | - | - | - |
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | - | - |
Setup File | - | - | - |
Source Code | - | - | - |
About Gxnovo
AI Agent & MCP Server Developer | Python, LLM Automation, Backend
Zuerich, Switzerland - 1:26 pm local time
Shipped: two MCP servers published on PyPI and the official MCP registry. An autonomous mission orchestrator (387 real tests, real Docker, real Postgres) with self-healing retries, an ownership-lease system so concurrent agents never collide, and a human-approval gate — no agent action touches a file or runs a command without your explicit sign-off, logged and auditable.
Two things I can set up for you directly:
- Local LLM deployment: run models on your own hardware instead of the cloud API — real cost cuts (industry data: ~18x cheaper per token at volume) and your data never leaves your infrastructure. I already run Claude-with-local-failover in production.
- Governed agent approval middleware: if you're letting AI agents touch code or files, I build the layer that makes every action explicit, reasoned, and reversible — not a black box.
Based in Zürich, available remote worldwide.
Steps for completing your project
After purchasing the project, send requirements so Gxnovo can start the project.
Delivery time starts when Gxnovo receives requirements from you.
Gxnovo works on your project following the steps below.
Revisions may occur after the delivery date.
I review your hardware and use case to pick the right local model.
I install, configure, and test the model on your machine.