API bill pressure
OpenAI, Azure OpenAI, Anthropic, Bedrock, and Vertex AI spend grows faster than traffic or revenue.
LLM Cost Optimization Services
Last reviewed Reduce OpenAI, Azure OpenAI, and Bedrock costs before buying more capacity.
NavyaAI audits the full cost of production AI workflows: API calls, tokens, RAG, agent loops, retries, routing, latency targets, GPU utilization, and self-hosting break-even points.
42%
inference cost cut
2.3x
throughput gain
72%
of AI spend hides outside inference
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? |
Service 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 pageDeep Dive
Why LLM bills rise while token prices fall
Per-token prices have fallen roughly 99.7% since GPT-3-era rates, yet enterprise AI spend tripled over the same period. The mechanism is the Jevons Paradox: cheaper tokens invite longer contexts, more retrieval, more tool calls, and agents that retry — each a reasonable product decision that multiplies the calls behind one user action by 50-500x.
Worse, most of the bill never appears on the model invoice: our AI Cost Report found 72% of production AI spend sitting in orchestration, retrieval, retries, observability, and the engineering operations around the model. A provider invoice reports totals; it cannot say which feature, team, or retry storm caused them. That attribution gap — not the price sheet — is where optimization work starts.
The optimization sequence: tune, route, then re-platform
The cheapest savings come first. Prompt compression, cached prefixes, output budgets, and retry control attack tokens that were pure waste — no architecture change, no quality risk. Next comes routing: most production traffic is not frontier-difficulty work, and moving classification, extraction, and formatting to mini-class models cuts the affected spend several-fold once an eval harness proves quality holds.
Only then does infrastructure enter the conversation. For steady, predictable volume, our Token Tax benchmark measured optimized self-hosted Llama 3 70B at roughly $0.47 per million tokens — against $0.82 unoptimized and $1.80-$2.50 for frontier APIs at testing time. That spread is the business case for the break-even analysis, and it only gets recommended when your traffic shape and operations capacity support it.
How It Works
How the audit works
Step 1
Share spend and workload shape
Submit monthly spend range, provider mix, token volume, and what your workloads do. Takes minutes, no production access needed.
Step 2
Map cost to workflows
We break the invoice into cost per completed user action: tokens, retries, retrieval, tool calls, and orchestration overhead.
Step 3
Rank levers by friction and savings
Routing, caching, prompt compression, retry control, quantization, or a private break-even case — ranked by savings against effort.
Step 4
Get the written read, then decide
The first cost-leak read arrives in writing. Deeper work is scoped only if the economics justify it; either way you keep the findings.
$47K → $28K
Case study: a Llama 3 70B production workload moved from 4 GPUs to 2 with quantization, KV-cache tuning, and serving changes — a 42% monthly cost cut with 2.3x throughput.
Read the full auditFAQ
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.
How much can LLM cost optimization save?
Results depend on the starting architecture, but a recent NavyaAI audit cut a Llama 3 70B serving bill 42% — from $47K to $28K per month — while improving throughput 2.3x, by moving from 4 GPUs to 2 with quantization and serving tuning.
How long does an LLM cost audit take?
The free audit intake takes minutes: share monthly spend, provider, token volume, and workload shape. The first written read on likely cost leaks typically follows within a few business days, before any paid engagement is discussed.