Cloud Cost Optimization & Infrastructure Audit
Most companies pay for capacity they never use, managed services they could replace, and LLM calls that cost 10× what they should. I find the waste in five business days and hand you a plan your team can execute the same week.
If the audit doesn't identify at least $50,000/year in combined payroll, cloud, and LLM savings, you pay nothing.
Cloud cost optimization is the practice of cutting infrastructure spend — compute, storage, egress, managed services, and increasingly LLM API bills — without hurting reliability. Oleg Sotnikov reviews your bills and architecture as part of the fixed-price Team & AI Audit ($5,000, five business days) and delivers a prioritized savings plan. He runs his own production platform, serving users in 190+ countries, on about $650/month of AWS.
Where the Money Usually Leaks
Six places I look first when the goal is to reduce cloud costs. Most audits find money in at least four of them.
Oversized compute
Instances sized for a launch-day spike that never came, autoscaling groups that only ever scale up, dev environments running around the clock. Kubernetes cost optimization is its own line of work here: requests and limits set from what the containers really use, honest bin packing, and spot nodes wherever an interruption is safe.
Storage and egress
Old snapshots, logs kept forever at hot-tier prices, and cross-zone traffic patterns that quietly turn into five-figure line items.
Managed-service sprawl
Paying platform markup for databases, queues, and monitoring that a leaner setup handles for a fraction of the price. I run GitLab, Sentry, Grafana, and Loki myself and include that stack with my engagements.
LLM and API bills
Wrong model for the job, no caching, no batching, prompts carrying dead context. Token spend is the fastest-growing line on modern bills, and routing and caching changes bring it down quickly.
CI/CD and tooling
Per-seat SaaS and slow hosted runners. In-memory runners turn minutes into seconds and cut both the bill and the waiting.
No cost ownership
Nobody sees the bill until finance does. I set up spend dashboards and alerts so costs stay visible to the people who create them.
How It Works
Access
Read-only access to your cloud billing, infrastructure configs, and repos (NDA on request). Nothing changes in production.
Savings map
Five business days of analysis across compute, storage, networking, managed services, tooling, and LLM spend — every finding quantified in dollars per year.
Plan and execution
You get a prioritized plan with effort estimates. Your team executes it, or I do it with you as your fractional CTO.
Why Me
- I run AppMaster's production platform — users in 190+ countries, 99.99% uptime — on about $650/month of AWS
- Cut a client's AWS bill by nearly 50% (Tech Solutions LLC — quote below)
- AppMaster processes 11B+ tokens a month, so LLM cost engineering — model routing, caching, batching — is daily work, not theory
“Oleg was great to work with. He quickly got up to speed with our setup, spotted where we were overspending on AWS and helped us reduce those costs by nearly 50%. He's knowledgeable, practical and communicates in a very straightforward way which made the whole process easy for our team.”
FinOps: Cost as an Engineering Practice
An audit finds the savings once. Without someone owning the number afterwards, the waste creeps back with the next few features. FinOps is the operating practice that keeps cost in front of the engineers who create it.
What FinOps is
The practice runs as a loop: inform, optimize, operate. Every team sees what it spends, the trimming happens on a schedule rather than in a panic, and cost sits in the same dashboards and alerts as latency and errors.
FinOps for AI
Token spend behaves like compute spend, so it belongs on the same dashboards: a budget per feature, an alert when a prompt change doubles the bill. Model routing then becomes a cost lever instead of a quality debate. AppMaster runs 11B+ tokens a month this way.
FinOps consulting vs. a one-off audit
The $5,000 audit is the entry point — it maps the waste and puts a number on it. Keeping that number where engineers can see it is ongoing work, and I do it inside the fractional CTO retainer rather than as a separate engagement.
Frequently Asked Questions
How much can you cut our cloud bill?
It depends on how much waste is in there — I've seen anywhere from 20% to 80%, and one client's AWS bill dropped by nearly half. That's why the audit carries a guarantee: at least $50,000/year in identified savings across payroll, cloud, and LLM spend, or you don't pay for it.
Do you only work with AWS?
No, although AWS cost optimization is the most common request. GCP, Azure, Hetzner, bare metal, or a mix all respond to the same review: the waste patterns — oversized compute, forgotten storage, managed-service markup — look the same everywhere; only the console differs.
Can you reduce our OpenAI and Anthropic API costs?
Yes. LLM cost optimization is where the fastest wins usually are, and the OpenAI API cost is the line item people ask about first. Most products send every request to a frontier model with an oversized prompt. Model routing, prompt caching, batching, and trimming dead context routinely cut token bills by half or more without any visible quality change.
What access do you need?
Read-only: billing exports, infrastructure configs or IaC repos, and a look at the architecture. I sign an NDA on request, and nothing changes in production during the audit.
How does this relate to the Team & AI Audit?
It's the same $5,000 audit — infrastructure and LLM spend is one of its lenses, alongside team structure and workflows. If your pain is purely the cloud bill, the audit can lead with that; the guarantee covers the combined savings either way.
Do you do FinOps consulting?
Yes, as an ongoing practice rather than a report. FinOps consulting here means someone owns the cloud and LLM number after the audit: budgets per team, cost on the same dashboards engineers already watch, and a monthly review of what moved. It normally runs inside a fractional CTO engagement, and the $5,000 Team & AI Audit is the usual entry point because it sets the baseline.
Find Out What You're Overpaying
Five business days, a quantified savings map, and a plan your team can execute. Backed by the $50,000/year guarantee.
At least $50,000/year in identified savings — payroll, cloud, and LLM bills — or the audit is free.
Related reading
Cloud economics, LLM cost engineering, and infrastructure that stays cheap.
