Your AI Bill Is Growing Faster Than Your Revenue

AI features are easy to launch and easy to overspend on. Every prompt, retry, retrieved document, and agent step costs tokens, and small inefficiencies multiply with every user.

An LLM cost optimization audit is the right step if:

• Your OpenAI, Anthropic, or Gemini bill has grown month after month

• AI costs are eating into your margins or making your pricing unprofitable

• You use your most expensive model for every request

• Your RAG system sends large amounts of context with every query

• AI agents run multi-step loops that nobody has measured

• You can't say which feature, customer, or prompt drives the most spend

• You're preparing to scale and need predictable AI unit economics

The audit shows exactly where your money goes, what can be cut, and how much each change is likely to save.

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Where LLM Costs Usually Leak

What Our LLM Cost Audit Covers

Proven Levers to Reduce LLM Costs

The audit tells you which of these levers apply to your app, and in what order:

Model Routing

Send simple tasks to smaller, cheaper models and reserve advanced models for complex reasoning. Quality is protected by testing each route against real examples.

Prompt Optimization

Remove redundant instructions, shorten system prompts, and constrain output formats without losing accuracy.

Prompt and Response Caching

Use provider-side prompt caching for repeated context, and cache responses to common or similar queries.

RAG Context Reduction

Retrieve fewer, more relevant chunks through better chunking, reranking, and relevance thresholds.

Conversation Memory Management

Summarize or trim long chat histories instead of sending the full conversation on every turn.

Batch Processing

Move non-urgent workloads such as reports, tagging, and bulk summarization to discounted batch APIs.

Agent Guardrails

Cap agent steps, stop runaway loops, and reduce unnecessary tool calls and retries.

Usage Controls

Add per-user and per-plan limits so AI costs scale in line with revenue.

Models, Providers, and Frameworks We Audit

• Model providers: OpenAI (GPT) · Anthropic (Claude) · Google (Gemini) · Mistral · Cohere · Meta Llama · DeepSeek

• Cloud AI platforms: Azure OpenAI · AWS Bedrock · Google Vertex AI

• Frameworks: LangChain · LlamaIndex · LangGraph · CrewAI · Vercel AI SDK · OpenAI Agents SDK · LiteLLM

• Vector databases: Pinecone · Weaviate · Qdrant · Chroma · pgvector · Milvus

• Self-hosted inference: vLLM · Ollama · TGI · GPU hosting on AWS, Azure, GCP, and specialist providers

How the Audit Works

1. Scoping Call

We learn how your app uses AI, your current monthly spend, your main AI features, and any quality or latency requirements you must keep.

2. NDA and Read-Only Access

We sign an NDA and get read-only access to your code, usage dashboards, billing exports, and logs or observability tools.

3. Spend Breakdown

We map your AI costs by model, feature, and request type to find the biggest cost drivers.

4. Technical Review

Our engineers review prompts, model choices, RAG pipelines, agent workflows, caching, and infrastructure to identify waste.

5. Savings Modeling

Each recommendation includes an estimated monthly saving, implementation effort, and any quality or latency trade-off.

6. Report and Walkthrough

You receive a prioritized cost reduction roadmap and a walkthrough session with your team.

What You Receive

• Executive summary: where your AI budget goes and the total estimated savings opportunity

• Cost breakdown: spend by model, feature, endpoint, and token type

• Findings report: each cost leak with evidence, affected components, and root cause

• Savings estimate per recommendation: the expected monthly saving for each change

• Quality and latency impact: the trade-offs to test before rolling out each change

• Prioritized roadmap: quick wins this week, deeper changes this month, and architecture changes later

• Monitoring plan: the metrics, budgets, and alerts that keep costs under control after the audit

• Walkthrough call: a findings session with your engineering and product teams

Example Savings Breakdown

Illustrative example of how findings and savings estimates appear in an audit report. Actual savings depend on your app, traffic, and quality requirements.

Example App: AI Customer Support Assistant (RAG + chat)

Example Monthly LLM Spend: $8,000

Recommended sequence:

• This week: Findings 1, 3, 6 (quick wins with low risk)

• This month: Findings 2, 4, 7 (needs quality testing)

• Next: Finding 5 (needs cache design and evaluation)

Typical LLM Cost Audit Scenarios

These examples show common situations, the cost drivers that typically surface, and how teams use the results.

Scenario 1: AI SaaS With Shrinking Margins

Stack: Next.js + OpenAI API + Pinecone

Audit: Full LLM Cost Audit

Situation: An AI writing and research SaaS grows quickly on a flat monthly subscription. Its LLM bill grows faster than subscription revenue, and heavy users now cost more than they pay.

Common findings:

• The most capable model used for every feature, including titles, tags, and short rewrites

• Long system prompts with examples that are rarely needed

• No per-plan usage limits, so power users generate unlimited requests

• No cost tracking by customer, so unprofitable accounts can't be identified

What happens next: The team routes lightweight tasks to smaller models, trims prompts, and introduces plan-based usage limits. Cost per active user drops, and pricing tiers are redesigned around real AI unit costs.

Scenario 2: Enterprise RAG Knowledge Assistant

Stack: Python (LangChain) + Azure OpenAI + pgvector · 

Audit: Full LLM Cost Audit

Situation: An internal knowledge assistant rolls out to more departments. Each expansion raises the Azure OpenAI bill, and finance asks engineering to justify the spend before approving a wider rollout.

Common findings:

• Large document chunks and too many retrieved passages per query

• Full conversation history sent on every follow-up question

• Documents re-embedded on every sync, even when unchanged

• No caching for frequently asked policy and HR questions

What happens next: The team improves chunking and reranking, summarizes long conversations, embeds only changed documents, and caches common answers. Cost per query becomes predictable, and finance approves the wider rollout with a clear budget.

Scenario 3: AI Agent Platform With Unpredictable Bills

Stack: Node.js + Claude API + LangGraph · 

Audit: Full LLM Cost Audit

Situation:

A workflow automation product uses AI agents to complete multi-step tasks. Monthly costs swing widely, and a few tasks occasionally cost many times more than expected.

Common findings:

• Agents without step limits, which sometimes loop on failed tool calls

• Automatic retries that repeat the full context each time

• Every agent step using the same advanced model, including simple lookups

• No alerts when a single task or customer exceeds a cost threshold

What happens next:

The team adds step and cost caps per task, smarter retry handling, and model routing by step type, plus real-time cost alerts. Runaway tasks stop, and monthly spend stays within a forecastable range.

LLM Cost Audit Pricing

Focused LLM Cost Audit

Starting at $499 · 5–7 business days

For apps with one main AI feature, or teams that want quick wins fast.

• Spend breakdown by model and feature

• Review of prompts, model choice, and caching

• Top 10 cost-saving recommendations

• Estimated saving per recommendation

• 30-minute walkthrough call

Get a Focused LLM Cost Audit →

Full LLM Cost Audit

Starting at $1,499 · 2 weeks

For apps with multiple AI features, RAG pipelines, or agents.

• Complete cost breakdown by model, feature, endpoint, and customer

• Prompt, RAG, caching, conversation, agent, and batch processing review

• Embeddings, vector database, and inference infrastructure review

• Savings model with quality and latency trade-offs

• Prioritized roadmap and monitoring plan

• 60-minute walkthrough call

• 14 days of follow-up Q&A

Get a Full LLM Cost Audit → (Most Popular)

Enterprise AI Cost Assessment

Custom pricing · 3–5 weeks

For large AI spend, multiple teams, or multi-provider environments.

• Multiple products, teams, and model providers

• Self-hosted vs API cost comparison

• GPU and inference infrastructure review

• Cost allocation and chargeback model by team or customer

• AI cost governance, budgets, and policy recommendations

• Executive and technical reports

Request a Custom Assessment →

Final pricing depends on the number of AI features, models, and integrations. Confirmed after a free scoping call.

LLM Cost Audit vs Related Services

Built your AI app with AI coding tools? Also see our AI-Generated Code Audit →

After the Audit: We Implement the Savings

If your team is focused on shipping features, our AI engineers can implement the recommendations for you and verify that quality holds.

CostControl is our LLM cost reduction implementation service:

• Model routing and fallback logic

• Prompt optimization with quality evaluation

• Prompt, response, and semantic caching

• RAG retrieval and context optimization

• Agent step limits and retry controls

• Batch processing pipelines

• Usage quotas and cost dashboards with alerts

Flexible engagement options: a fixed-price implementation project, or a savings-based model where part of the fee is tied to measured cost reduction.

Get Help Reducing AI Costs → 

Frequently Asked Questions

What is an LLM cost optimization audit?

It's a structured review of how your application uses large language models and AI APIs. It identifies where tokens and money are wasted, and recommends changes such as model routing, prompt optimization, caching, and RAG context reduction to lower costs without reducing quality.

How much can an audit save on AI API costs?

Savings depend on how your app is built and how much waste exists. Apps that use one premium model for everything, send large RAG context, or have no caching usually have the largest opportunities. Every recommendation in our report includes an estimated monthly saving, so you can see the potential before implementing anything.

How much does an LLM cost audit cost?

A Focused LLM Cost Audit starts at $499, and a Full LLM Cost Audit starts at $1,499. Enterprise assessments are priced by scope. The final price is fixed after a free scoping call.

How long does the audit take?

A Focused LLM Cost Audit takes 5–7 business days. A Full LLM Cost Audit takes about 2 weeks.

Will reducing costs hurt the quality of AI responses?

Not when changes are tested properly. Every recommendation includes its expected quality and latency impact, and changes such as model routing should be evaluated against real examples before rollout.

Which AI providers do you work with?

We audit apps using OpenAI, Anthropic Claude, Google Gemini, Mistral, Cohere, Llama, and DeepSeek, including through Azure OpenAI, AWS Bedrock, and Google Vertex AI, as well as self-hosted models.

What access do you need?

Read-only access to your codebase, AI provider usage and billing exports, and any logging or observability tools. We don't need access to modify production systems.

Is it worth it if our AI spend is small?

If your monthly AI spend is low, a free cost estimate or a Focused Audit is usually enough. The Full Audit delivers the most value for teams spending over $1,000 per month, or expecting rapid growth.

Can you implement the recommendations?

Yes. Through CostControl, our AI engineers implement the changes, from model routing to caching and usage controls, with a fixed-price or savings-based engagement.

Stop Paying for Tokens You Don't Need

Find out where your AI budget goes and how much you can save, with a clear, prioritized plan your team can act on.

Get an LLM Cost Audit →