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Free AI Inference Audit

Find the first AI inference cost leak.

Send monthly spend, provider, token volume, model stack, latency targets, and your current serving setup. NavyaAI will reply with the first cost levers to inspect and whether API, hybrid, or self-hosted infrastructure deserves a deeper audit.

Best fit

$20K-$200K/mo AI spend

Signal

RAG, agents, GPUs, APIs

Outcome

Audit-ready next steps

What we look for

  • Token volume growing faster than per-token price cuts.
  • Agent loops, retries, or RAG retrieval multiplying hidden spend.
  • GPU underutilization, overprovisioned serving, or poor batching.
  • Private AI, data residency, or latency constraints that change the build-vs-buy math.
Book a Qualified Call

Audit Intake

Get a written leak map before a call.

Best fit for teams spending $20K+/month on OpenAI, Azure OpenAI, Anthropic, Bedrock, Vertex, RAG, agents, or self-hosted LLMs.

  • Where token, retry, RAG, agent, or GPU waste is most likely.
  • Which metric to inspect first before buying more capacity.
  • Whether the next step is a written question set, estimator review, or call.
Add stack details — optional, but it makes the leak map sharper

Takes 3 minutes. No call required — I reply with where to look first, even if we never speak.

Already have a private deployment question? Try the On-Prem LLM Cost Estimator first, then attach the output in the stack notes.