Free AI Inference Audit · No call required
Get a written map of your AI cost leaks before a call.
Send the spend range, workload, and stack shape. I will reply with the first places to inspect: token waste, retry storms, RAG overhead, model routing, or GPU utilization.
Your intake goes to me — and I read your whole stack.
I work where AI meets HPC, with a view that runs from transistor-level architecture to cloud-scale inference. That full-stack span is how I find cost leaks others miss — in the silicon, the serving layer, and the system design at once.
Transistor→Kernel→GPU→Cluster→Cloud
Vikas Chamarthi · Founder, NavyaAI
LinkedInTrack record
M.S. Electrical & Computer Engineering, UNC Charlotte
Selected work — benchmarks & audits
We publish our numbers so you can check our work, on technical depth and on cost saved.
$47K → $28K
Case study
Llama 3 70B inference audit: A100s to a leaner H100 plan
Quantization, KV-cache tuning, batching changes, and an accurate GPU capacity plan cut monthly spend from $47K to $28K at 2.3x throughput.
Read the case study
Token prices −99.7%, bills up 3×
Report
Why token prices collapsed but your AI bill tripled
The per-token cost curve fell 99.7% since 2022, yet production AI bills keep climbing. Where the money actually goes.
Read the report
AI Economics 2026
Report
Token collapse and the sustainability question
Our 2026 read on inference economics: token deflation, margin pressure, and which deployment models actually survive.
Read the report
Engineering
158 tok/s at 8 watts
We Benchmarked LLMs on a $499 Jetson Orin Nano: 158 Tokens/sec at 8 Watts — and Where the Board Breaks
Read the write-up· 12 min read
Engineering
The Token Tax
The Token Tax: A Comparative Audit of Inference Optimization Techniques
Read the write-up· 17 min read
Engineering
70% faster, 75% less memory
Threads Beat Multiprocessing for RAG: 70% Faster, 75% Less Memory
Read the write-up· 12 min read
Engineering
Rust vs Python, measured
Embedding Rerank Gateway: Rust vs Python Cost and Performance
Read the write-up· 10 min read
Engineering
Same model, lower bill
Python vs Rust for Transformers: Performance and Cost
Read the write-up· 25 min read
Sound familiar?
The bill went up. Usage didn't.
- Retry storms and bloated prompts
Uncontrolled retries, oversized contexts, and uncached system prompts silently multiply token spend on every request.
- Agent loops and RAG overhead
Agents that call themselves, plus retrieval pipelines stuffing irrelevant chunks into context. Cost grows faster than output quality.
- Wrong model mix and idle GPUs
Frontier models doing work a small model could handle, and self-hosted clusters running under 30% utilization.
Illustrative breakdown based on a real engagement — a high-volume Llama 3 70B deployment. Your leak profile will differ; that is exactly what the audit maps. Read the case study.
Audit scope
Eleven places inference budgets go to die
The intake qualifies your spend range, provider, token volume, and latency targets. Then I inspect the signals that actually move the bill.
- Provider spend and account shape
- Token volume and growth trend
- Model mix and routing decisions
- Retry and timeout patterns
- RAG context overhead
- Agent loop behavior
- Prompt caching coverage
- Batching opportunities
- Latency targets vs. cost trade-offs
- GPU utilization (self-hosted)
- Self-hosting break-even math
How it works
Intake to findings in three steps
- 01
Share your stack shape
Spend range, primary provider or workload, name, and a work email. More stack detail helps, but no deck is required.
~3 minutes
- 02
I send the first leak map
I match your shape against known leak patterns — retries, caching, routing, RAG overhead, GPU utilization — and identify what to inspect first.
Written reply
- 03
Get the right next step
A written question set, an estimator review, or a qualified call — whichever is genuinely fastest for your situation.
Your call from there
Proven in production
Trusted by teams shipping real AI
- Blade Dynamics
Free tools we built and shipped
The same sizing and break-even math the audit uses, available as self-serve tools.
FAQ
Common questions before requesting the audit
Answers written and last reviewed by Vikas Chamarthi, Founder of NavyaAI, on .
Who is the free AI inference audit for?
This is for teams where I can actually move the needle — usually $20K+/month, where the cost leaks are large enough to justify the time on both sides. If you're on OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, or running self-hosted LLMs or RAG, that's the right profile.
What does NavyaAI check in an inference audit?
I look at provider spend, token volume, model mix, retry patterns, RAG overhead, agent loops, latency targets, batching, caching, routing, and GPU utilization. Usually two or three of these dominate the bill — the intake helps me figure out which ones before we talk.
Can NavyaAI reduce OpenAI or Azure OpenAI costs?
In most cases, yes. Prompt compression, response caching, model routing, workload shaping, and retry control are the common levers. For larger workloads I also run private deployment break-even math — sometimes moving off the API entirely is the right call.
Do teams below $20K per month qualify?
You can still submit, but below $20K the fastest path is usually our on-prem estimator or a focused technical note rather than a full audit call. I'll let you know which makes sense when I review the intake.
Is this a disguised sales call?
No. The intake decides whether the fastest next step is a written audit question set, an estimator review, or a qualified call. You get written findings either way — a call only happens if it's genuinely the right next step and you want one.
Free · 3-minute intake
Find the leaks before you buy more capacity.
Written findings, no call required. The worst case is you confirm your stack is already tight.
No credit card · No call required · Work email only