You will get AI/LLM Cost Audit — Find What's Wasting Your API Budget
Project details
Your AI system works — but is it burning money it doesn't need to?
Most teams running LLM-powered products never check whether every API call actually needs a full model. In practice, 20-40% of that spend often goes toward tasks a lightweight script or cheaper model could handle for a fraction of the cost — nobody just looked.
I audit your LLM usage and show you exactly where the money leaks: which calls are over-engineered, which can be routed to cheaper models or deterministic code, and what that saves you per month in real dollars — not vague advice.
What makes this different: I come from a systems engineering background (Rust, backend infrastructure), not a prompt-engineering one. I don't just suggest "try a cheaper model" — I look at your actual call patterns, caching opportunities, and architecture to find savings most AI consultants miss.
You'll walk away with a clear, prioritized list of fixes ranked by savings potential and effort — so you know exactly what to fix first, and how much it's worth.
If your monthly AI bill keeps creeping up and you're not sure why — this is built for exactly that moment.
Most teams running LLM-powered products never check whether every API call actually needs a full model. In practice, 20-40% of that spend often goes toward tasks a lightweight script or cheaper model could handle for a fraction of the cost — nobody just looked.
I audit your LLM usage and show you exactly where the money leaks: which calls are over-engineered, which can be routed to cheaper models or deterministic code, and what that saves you per month in real dollars — not vague advice.
What makes this different: I come from a systems engineering background (Rust, backend infrastructure), not a prompt-engineering one. I don't just suggest "try a cheaper model" — I look at your actual call patterns, caching opportunities, and architecture to find savings most AI consultants miss.
You'll walk away with a clear, prioritized list of fixes ranked by savings potential and effort — so you know exactly what to fix first, and how much it's worth.
If your monthly AI bill keeps creeping up and you're not sure why — this is built for exactly that moment.
AI Development Type
Model Tuning, Software MaintenanceAI Tools
Azure Machine Learning, MATLAB, OpenCV, PyTorch, Sonnet, TensorFlowAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$40
|
Standard
$90
|
Advanced
$200
|
|---|---|---|---|
| Delivery Time | 2 days | 4 days | 8 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | |
Detailed Code Comments | - | ||
Knowledge Graph | - | - | - |
Model Documentation | |||
Ontology | - | - | - |
Source Code | - | - | |
Taxonomy | - | - | - |
Frequently asked questions
1 review
(1)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
BF
Ben F.
Dec 30, 2025
Python/Django dev with fast frontend skills (Bootstrap/HTML/CSS)
About Mariia
Full-Stack Engineer | Rust, Python, Next.js, AI/ML Systems
Opory, Ukraine - 11:23 pm local time
That means my technical decisions are tested against real users and real revenue, not just spec sheets: what needs to scale now vs. later, where async matters and where it's overkill, and how a frontend decision actually affects backend load.
WHAT I BUILD
— Full-stack SaaS platforms: React/Next.js frontends wired to Rust and Python backends
— Rust-based microservices for high-throughput, low-latency backend systems
— AI/ML systems: model training, fine-tuning, and inference pipelines (custom CNNs, LLM-driven AI agent workers)
— Business automation systems: AI voice/chat agents, webhook pipelines, scheduled jobs that replace manual work
STACK
Frontend: Next.js, React, TypeScript, Tailwind
Backend: Rust (Actix-Web, Tokio), Python (FastAPI), PostgreSQL, Redis
AI/ML: Custom model training & fine-tuning (CNNs, Google Colab), LLM API integration, AI agent worker design
Infra: Docker, Linux server administration, VPS deployment, CI/CD basics
HOW I WORK
— I scope before I touch code, and I say clearly what's realistic for your timeline and budget
— I build systems someone else could maintain later, not just things that work for me
— I update proactively — you won't be chasing me for status
— I care about the outcome, not just closing the ticket
Want proof before you take my word for it? I can show you a real AI agent handling a live call — ask and I'll send it.
Steps for completing your project
After purchasing the project, send requirements so Mariia can start the project.
Delivery time starts when Mariia receives requirements from you.
Mariia works on your project following the steps below.
Revisions may occur after the delivery date.
Client purchases the project and shares usage data
You'll share a summary of your monthly LLM API costs, a short description of your AI system, and which provider/models you use.
I analyze your API call patterns
I review your usage data to identify which calls are high-cost, over-engineered, or doing unnecessary heavy lifting.