Map the provider bill
Break down OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, or self-hosted spend by feature, user path, model, and token shape.
Your AI bill is too high.
Find the leak.
NavyaAI audits OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, RAG, agent, and self-hosted LLM costs so CTOs and ML platform leads can reduce inference spend before buying more capacity.
Reduce OpenAI API CostsAzure OpenAI Cost OptimizationAWS Bedrock Cost ReviewLLM Inference CostRAG / Agent Cost LeaksSelf-Hosted LLM Break-EvenGPU Utilization AuditToken Spend Forecasting
Trusted by Innovative Companies
Blade Dynamics
LLM Cost Optimization
Find the cost leak before changing providers.
Start with the bill you already have. Then isolate whether the leak is provider pricing, token volume, RAG overhead, agent loops, retries, GPU utilization, or a workload that needs different deployment math.
Find workflow multipliers
Check whether context windows, RAG retrieval, reranking, agent tools, retries, or observability are multiplying cost outside the model call.
Pick the next move
Decide whether the next step is caching, routing, batching, prompt compression, GPU tuning, self-hosting math, or a deeper paid audit.
Research Reports
AI Economics and Cost Reports
Field reports for operators who care about margin, reliability, and the real economics behind production AI systems.
New Report
The AI Economics Report
Token Collapse and Sustainability
May 2026Get the Report
A data-first look at token price collapse, hyperscaler capex, provider margins, hidden AI costs, and whether cheap token pricing can last.
Includes the 99.7% token cost drop, $725B AI capex signal, margin risk, and builder guidance for 2026.
Live Now
Tokens got 99.7% cheaper.
Why did your AI bill triple?
ReportGet the Free Report
A 4-part breakdown of the cost paradox: 99.7% token price drop, 3× bill growth, and 72% spend hiding outside inference.
Includes benchmark numbers, hidden-cost anatomy, and an operator-ready optimization sequence.
AI Tools
Try Our AI Agents — Free
Interactive tools that give you answers in minutes, not meetings.
On-Prem LLM Cost Estimator
Size your on-prem GPU cluster, compare against cloud API costs, and find your break-even point — all in one tool.
- •GPU hardware recommendations for any model
- •Cloud API cost comparison (OpenAI, Anthropic, Google)
- •TCO & break-even analysis over 1–3 years
on-prem-estimator
$ estimate --model llama-3-70b
Hardware: 2× NVIDIA A100 80GB
On-prem cost: $4,200/mo
Cloud equiv: $18,600/mo
Break-even: 4.2 months
█
Coming Soon
AI Infra Sizing Agent
Right-size your GPU cluster, networking, and storage for any AI workload — training or inference.
Coming Soon
Prompt Cost Analyzer
Analyze prompt token usage across providers and find the cheapest path to production-quality output.
Decision Guides
Clear answers for expensive AI infrastructure decisions.
Direct-answer pages for self-hosting, break-even math, GPU ROI, and private RAG decisions.
self-host LLM vs API cost
Self-Host LLM vs API Cost: When Each Wins
Compare self-hosted LLM cost vs managed API cost across token volume, latency, privacy, operations, and break-even timing.
Read guideLLM break-even point
LLM Break-Even Point: API, Hybrid, or Self-Hosted
Use this LLM break-even framework to decide when API spend, hybrid routing, or self-hosted GPU infrastructure makes economic sense.
Read guideL40S vs H100 ROI
L40S vs H100 ROI for LLM Inference
Compare L40S vs H100 ROI for LLM inference across throughput, memory, utilization, capex, and workload fit.
Read guideedge self-hosted RAG vs OpenAI API
Edge RAG vs OpenAI API: When Private Retrieval Wins
Compare edge or self-hosted RAG vs OpenAI API workflows for privacy, latency, cost, throughput, and operational control.
Read guideEngineering Blog
Latest Technical Insights
Deep dives into model optimization, HPC, MLOps, DevOps, and production-grade AI/ML engineering.
Engineering
The Token Tax: A Comparative Audit of Inference Optimization Techniques
We benchmarked Llama 3 70B across FP16, INT8 quantization, and KV-cache pruning on H100 and A100 GPUs. The results: 42% lower cost per million tokens and 2.3x throughput — without quality loss you'd notice in production.
17 min read
Read
Engineering
Embedding Rerank Gateway: Rust vs Python Cost and Performance
We built the same embedding + rerank gateway in Python, Rust (ONNX), and a split architecture. Rust hit 28% more RPS with 67% less memory, lowering RAG infrastructure cost.
10 min read
Read
Engineering
Self-Knowledge Distillation for AI Efficiency: Orpheus TTS
Compress Orpheus-3B TTS with self-knowledge distillation, Unsloth, SNAC tokenization, and LoRA. Smaller adapters reduce serving memory and private AI deployment cost.
25 min read
Read
Client Results
Optimization, Measured
Production inference work should show up in unit economics, not just benchmark charts.
Llama 3 70B inference audit
From overprovisioned A100s to a leaner H100 deployment.
A client running unoptimized Llama 3 70B at roughly 200M tokens per month was carrying low utilization and duplicated GPU spend. After INT8 quantization, KV-cache pruning, and batching tuning, they consolidated from 4 GPUs to 2 while keeping quality within production noise.
42%
cost reduction per million tokens
2.3x
throughput improvement
$47K -> $28K
monthly bill after optimization
Audit the AI bill before the call.
Best fit for CTOs, founders, and ML platform leads with production LLM traffic, rising inference spend, or agentic workflows that are hard to forecast.
Good fit
$20K-$200K/month AI spend, high-volume tokens, RAG, or self-hosting decisions
Phone
+91 799 317 6634Location
Andhra Pradesh, India
Frequently Asked Questions
Common Questions About AI Infrastructure Cost
When should a company self-host an LLM instead of using an API?
A company should evaluate self-hosting when usage is predictable, token volume is high, data residency matters, or latency and margin requirements cannot be met through managed APIs. NavyaAI calculates the break-even point before recommending GPUs.
What is a free AI inference audit?
A free AI inference audit is a lightweight review of monthly AI spend, provider mix, token volume, model mix, RAG or agent workflow shape, latency targets, and deployment constraints. It identifies likely cost leaks before a paid infrastructure audit.
How do you reduce LLM inference cost?
LLM inference cost usually drops through batching, prompt compression, caching, routing, quantization, KV-cache tuning, GPU utilization improvements, and architecture changes that reduce retries and unnecessary agent steps.
What AI costs are hidden outside the model invoice?
Hidden AI costs often include retrieval pipelines, vector databases, orchestration, observability, guardrails, retries, data egress, idle GPU capacity, compliance work, and engineering time.
Who is NavyaAI best fit for?
NavyaAI is best fit for CTOs, founders, and ML platform leads spending roughly $20K-$200K per month on AI APIs, LLM serving, RAG, agents, GPUs, or private AI deployment decisions.