API bill pressure
OpenAI, Azure OpenAI, Anthropic, Bedrock, and Vertex AI spend grows faster than traffic or revenue.
Workflow-level leaks
RAG retrieval, agent loops, retries, tool calls, and long context windows multiply cost outside the model invoice.
Infrastructure decisions
Cloud GPUs, on-prem hardware, and private models need break-even math before procurement or migration.
Optimization levers we check before recommending a call
The goal is not to push every team into self-hosting. The first pass is to find the lowest-friction cost leak in the current stack and decide whether the economics justify deeper work.
Prompt compression and context window control
Semantic caching and response reuse
Model routing by task complexity and user tier
Retry, timeout, and agent-loop control
Batching, KV-cache, and throughput tuning
RAG retrieval, reranking, and vector-store overhead
Quantization and smaller specialist model options
Cloud GPU, on-prem, and API break-even modeling
Provider Coverage
We optimize around the bill you already have.
| Workload | Common cost leak | First audit question |
|---|---|---|
| OpenAI / Anthropic | Large models used for simple tasks | Can traffic route by task difficulty? |
| Azure OpenAI | Enterprise usage grows without workflow attribution | Which team or feature is driving the bill? |
| Bedrock / Vertex | Provider mix hides per-workflow unit cost | What is cost per completed user action? |
| RAG / Agents | Retries, tools, and retrieval multiply calls | Where do loops and context expansion occur? |
| Self-hosted LLMs | Low utilization or overprovisioned GPUs | What throughput and latency does each GPU deliver? |
Reduce OpenAI API costs
Audit prompt, retry, RAG, agent, and model routing leaks in OpenAI workloads.
Open pageAzure OpenAI cost optimization
Map enterprise Azure OpenAI spend to teams, features, and workflow unit cost.
Open pageAWS Bedrock cost review
Review Bedrock model mix, RAG, agents, guardrails, and self-host break-even signals.
Open pageService Lanes
When the audit points beyond provider tuning.
AI infrastructure consulting
Architecture review for LLM, RAG, agent, and deployment decisions.
Open pageMLOps consulting services
Evaluation, observability, release, and cost telemetry for production AI.
Open pageSelf-hosted LLM deployment
Private LLM, vLLM, GPU sizing, and self-host vs API break-even planning.
Open pageFAQ
LLM cost optimization questions
Why is my OpenAI bill so high?
OpenAI bills often rise because token volume grows through long prompts, verbose context, retries, tool calls, agent loops, RAG retrieval, and model choices that are larger than the task requires.
How do you reduce LLM API costs?
LLM API costs can usually be reduced through prompt compression, caching, model routing, batching, retry control, shorter context windows, smaller specialist models, and workflow changes that avoid unnecessary calls.
When should a team self-host an LLM?
A team should evaluate self-hosting when usage is predictable, volume is high, latency or data residency matters, and the full GPU, operations, security, and engineering cost can beat API economics.
Can NavyaAI optimize Azure OpenAI, Bedrock, or Vertex AI costs?
Yes. NavyaAI reviews Azure OpenAI, AWS Bedrock, Vertex AI, Anthropic, OpenAI, RAG, agent, and self-hosted workloads through the same cost-per-workflow lens.