Free AI Inference Audit
Find why your AI bill is too high.
For teams spending $20K+/month on OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, RAG, agents, or self-hosted LLMs. Share the stack shape and we will identify the first cost leaks to inspect before a paid audit or capacity decision.
Case signal
42% cost reduction
Throughput
2.3x improvement
Bill path
$47K to $28K/month
What the audit intake qualifies
- Monthly AI/LLM spend range and whether the account is audit-ready.
- Primary provider or workload: API, cloud LLM, RAG, agents, or self-hosted.
- Token volume, latency target, and the parts of the stack causing cost pressure.
- Whether the next step should be optimization, routing, self-hosting math, or a call.
FAQ
Common questions before requesting the audit
Who is the free AI inference audit for?
The free AI inference audit is for teams spending $20K or more per month on OpenAI, Azure OpenAI, Anthropic, AWS Bedrock, Vertex AI, RAG systems, agents, or self-hosted LLMs.
What does NavyaAI check in an inference audit?
NavyaAI checks provider spend, token volume, model mix, retry patterns, RAG overhead, agent loops, latency targets, batching, caching, routing, and GPU utilization signals.
Can NavyaAI reduce OpenAI or Azure OpenAI costs?
NavyaAI looks for practical ways to reduce OpenAI and Azure OpenAI costs, including prompt compression, caching, model routing, workload shaping, retry control, and private deployment break-even analysis.
Do teams below $20K per month qualify?
Teams below $20K per month can still submit the form, but the fastest next step is usually the on-prem LLM cost estimator or a focused technical note instead of a live audit call.