You will get a full cost audit of your AI product, with a plan to cut the bill in half
Project details
Your AI product has two bills, and nobody looks at them together. There is what it costs to run: model API, vector database, hosting, GPU. And there is what it costs to build: how long each feature takes to ship.
Cloud consultants know infrastructure but not token economics. AI developers know tokens but never touch infrastructure. I do both. The money hides in between.
I trace your real usage and billing, then show you where it leaks and what each fix returns in dollars, before you commit to anything. Typical audits find 20-40% of spend addressable: whole documents resent on every call, oversized context, no caching, wrong model routing. Every number comes from your own token trace and a measured before/after, never a blanket percentage.
You get a prioritized report you can execute this week, with any team. Not a slide deck. If it surfaces something worth fixing properly, 50% of the fee is credited toward my AI Project Rescue within 30 days.
You work directly with me. Senior architect, 15 years of production systems, including real-time infrastructure for Amazon and research platforms. No agency, no account managers.
Message me your stack and monthly spend before ordering.
Cloud consultants know infrastructure but not token economics. AI developers know tokens but never touch infrastructure. I do both. The money hides in between.
I trace your real usage and billing, then show you where it leaks and what each fix returns in dollars, before you commit to anything. Typical audits find 20-40% of spend addressable: whole documents resent on every call, oversized context, no caching, wrong model routing. Every number comes from your own token trace and a measured before/after, never a blanket percentage.
You get a prioritized report you can execute this week, with any team. Not a slide deck. If it surfaces something worth fixing properly, 50% of the fee is credited toward my AI Project Rescue within 30 days.
You work directly with me. Senior architect, 15 years of production systems, including real-time infrastructure for Amazon and research platforms. No agency, no account managers.
Message me your stack and monthly spend before ordering.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language UnderstandingAI Development Language
PythonAI Models
ChatGPT, GPT-4What's included
| Service Tiers |
Starter
$350
|
Standard
$800
|
Advanced
$1,400
|
|---|---|---|---|
| Delivery Time | 2 days | 5 days | 7 days |
Number of Revisions | 1 | 1 | 2 |
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
+$150 - $500
Additional Revision
+$150Frequently asked questions
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AR
Alex R.
Mar 11, 2021
Editor Developer
About Alexander
Senior RAG / LLM / AI-Agent Engineer: LangGraph, FastAPI, Deployed
Berlin, Germany - 2:40 pm local time
I build production AI features and the backends that run them. I rescue the ones that broke on the way. And I cut the bill when it quietly triples. 15 years of distributed systems sits underneath that, so when the problem turns out to be infrastructure rather than the model, it does not become someone else's job.
What I do:
BUILD: cited-RAG assistants and in-app agents, shipped as deployed, monitored backends. FastAPI, pgvector or Pinecone, LangGraph orchestration, guardrails, CI/CD, handoff docs. One scoped feature or a full product backend.
RESCUE: production AI that hallucinates, retrieves badly, stalls, or burns budget. I triage, root-cause, fix, add an eval harness so the fix is provable, then redeploy.
OPTIMIZE: your LLM and RAG spend and architecture. Model routing, token cost, caching, retrieval quality, reliability gaps, delivered as a prioritized report with measured savings, not a slide deck.
Most AI projects do not fail because the model is bad. They fail because retrieval is wrong, the architecture is over-built, nobody can measure whether an answer is correct, and the bill quietly triples. I find which of those is actually happening and tell you honestly what is worth saving.
Recent work:
- Technical Lead on a production LangGraph multi-agent platform: 20+ integrated tools, source-cited RAG over research data, real production use.
- Designed the GPU data center for AI/ML, with the Prometheus, Grafana, and ELK observability that kept it honest.
- Built real-time monitoring infrastructure for Amazon warehouses across Europe.
- Built distributed low-latency trading systems.
- Built web graphics editors used by around a million people.
How I work: I take full ownership, keep the stack simple, and use AI-assisted development heavily, which means I ship complex systems faster than a traditional workflow. Best fit for startups and small teams that need one senior person to go from architecture to production without hiring a whole team.
Stack: LangGraph, LangChain, RAG, pgvector, Pinecone, vector search, OpenAI API, Anthropic API, eval harnesses, prompt engineering, Python, FastAPI, Django, Flask, Node, TypeScript, React, React Native, Java, AWS, Docker, Kubernetes, Terraform, PostgreSQL, Prometheus, Grafana, ELK, GitLab CI.
Start with a fixed-scope AI Cost Audit for a senior second opinion before you spend more, or tell me the problem you are trying to solve and I will tell you honestly whether I am the right fit.
Steps for completing your project
After purchasing the project, send requirements so Alexander can start the project.
Delivery time starts when Alexander receives requirements from you.
Alexander works on your project following the steps below.
Revisions may occur after the delivery date.
Measure where the money goes
Included in every tier. I map your real spend by feature, across the model API, vector database, hosting, and GPU, using your own logs and billing rather than averages.
Savings report with a fixed quote
You get the exact number: what leaks, what it costs to fix, and what you get back per month. Starter ends here. You can hand the report to your own team and act on it without me.