You will get audit and reduction of your LLM API costs by 30 to 50 percent
Rising Talent
Rising Talent
Project details
I cut LLM API costs by 40 to 60 percent without hurting output quality. Most teams burn money on three things: using the most expensive model for every call, no caching for repeated queries, and no telemetry to catch runaway loops. I fix all three.
My approach has three layers. Layer one is tiered routing where a cheap classifier decides if a query needs the orchestrator, then routes orchestrators to Claude Sonnet and workers to Haiku or Gemini Flash. Layer two is smart caching combining prompt caching, semantic caching, and context trimming to avoid paying for the same tokens twice. Layer three is observability with MLflow telemetry, hard budget caps, and eval suites that validate model downgrades before they ship.
What sets this apart is that I implement the fixes against real traffic and validate savings with before and after metrics. My current role at S&P Global involves production LLM orchestration at scale, and my freelance audits have consistently delivered 40 to 60 percent cost reduction with zero quality regressions. You get a written audit, hands-on implementation, and a maintenance plan.
My approach has three layers. Layer one is tiered routing where a cheap classifier decides if a query needs the orchestrator, then routes orchestrators to Claude Sonnet and workers to Haiku or Gemini Flash. Layer two is smart caching combining prompt caching, semantic caching, and context trimming to avoid paying for the same tokens twice. Layer three is observability with MLflow telemetry, hard budget caps, and eval suites that validate model downgrades before they ship.
What sets this apart is that I implement the fixes against real traffic and validate savings with before and after metrics. My current role at S&P Global involves production LLM orchestration at scale, and my freelance audits have consistently delivered 40 to 60 percent cost reduction with zero quality regressions. You get a written audit, hands-on implementation, and a maintenance plan.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Regression Analysis, Transformer ModelAI Applications
AI Chatbot, AIOps, Anomaly Detection, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Gradio, Hugging Face, StreamlitAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$200
|
Standard
$550
|
Advanced
$1,200
|
|---|---|---|---|
| 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 | - |
Optional add-ons
You can add these on the next page.
Fast Delivery
+$60 - $200
Additional Revision
+$50
Telemetry Dashboard
+$150
Eval Suite Setup
+$120Frequently asked questions
About Nihanth
Sde II
Hyderabad, India - 11:40 pm local time
I specialize in designing and deploying production-ready RAG systems, graph-based AI applications, and agentic LLM workflows. I’ve worked with global organizations like S&P Global and Perficient, delivering scalable AI solutions using OpenAI, Azure OpenAI, Gemini, LangChain, and Hugging Face
What I can help you with:
• RAG-based chatbot development
• LLM application architecture
• Vector databases
• Graph-based AI systems
• End-to-end ML pipelines
• Cloud deployment on Azure, AWS, and GCP
• Responsible and compliance-aware AI systems
• End to end agentic workflows
• MCP Servers
• n8n Automation
I focus on building solutions that are not just innovative, but scalable, secure, and production-ready. Whether you need a custom AI chatbot, an automated workflow powered by LLMs, or a full-stack AI deployment, I can help you bring it to life.
Steps for completing your project
After purchasing the project, send requirements so Nihanth can start the project.
Delivery time starts when Nihanth receives requirements from you.
Nihanth works on your project following the steps below.
Revisions may occur after the delivery date.
Usage analysis
Review API logs, cost breakdown, prompts, and current model choices to identify cost drivers.
Written audit
Deliver written audit with root cause analysis for every cost driver and prioritized savings plan.