SERVICES · COMPLIANCE

LLM Cost Forensics

Forensic analysis of where your LLM API spend actually goes — and how to cut the 80% that drives only 20% of value.

LLM Cost Forensics is an audit engagement focused on operational efficiency rather than security. We analyze your LLM API spend at the prompt level, identify the patterns driving disproportionate cost (context bloat, retry loops, unnecessary model upgrades, prompt inefficiency), and produce a prioritized optimization plan. Typical engagements find 30–60% potential cost reduction without affecting output quality.

// THE PROBLEM

What we're solving when you hire us for this

LLM API costs in 2026 are growing faster than the budgets allocated for them. Most teams don't know where the spend actually goes — they see the monthly invoice, not the per-prompt economics. Context windows have inflated to 100K+ tokens for routine tasks. Retry-on-failure logic burns through tokens. Production code calls GPT-5 when GPT-5-mini would suffice. Each pattern is invisible in aggregate but enormous in cumulative impact.

LLM Cost Forensics audits this systematically. We instrument your API usage at the prompt level, analyze the patterns driving cost, and produce specific optimization recommendations — not generic 'use a smaller model' advice, but per-pattern, per-team, per-deployment recommendations with quantified expected savings. Cost work that's measurable, not aspirational.

// HOW WE RUN IT

The five phases of an LLM Cost Forensics engagement

Usage Instrumentation

We work with your engineering team to capture detailed LLM API usage data: prompts, contexts, models, tokens, costs, latencies. Some data may already exist; some requires lightweight logging additions.

Duration 3–5 days · Output: instrumented usage data

Pattern Analysis

We analyze the captured data for cost-driving patterns: context bloat, retry loops, suboptimal model selection, redundant calls, inefficient prompt templates. Each pattern is quantified by cost contribution.

Duration 5–7 days · Output: pattern analysis

Optimization Design

For each high-cost pattern, we design the specific optimization: prompt compression, context pruning, retry-logic changes, model-downgrade thresholds, caching strategies. Each optimization is paired with expected savings and implementation effort.

Duration 3–4 days · Output: optimization plan

Validation Sample

We implement the highest-impact 2–3 optimizations in a controlled sample to validate the projected savings. Real-world validation prevents over-promising and confirms that output quality holds under the optimization.

Duration 5–7 days · Output: validation results

Reporting & Roadmap

Final deliverable is a prioritized optimization roadmap with quantified expected savings, implementation effort, and risk for each item. Your engineering team has clear next steps with budget justification built in.

Duration 3–4 days · Output: roadmap + runbook

// WHAT YOU RECEIVE

Deliverables, named and specific

Spend Forensics Report

Detailed breakdown of where your LLM API spend goes: by team, by use case, by model, by prompt pattern. Per-dollar visibility into cost drivers.

30–50 pages · Markdown + PDF

Pattern Analysis

Each cost-driving pattern documented: scope, frequency, cost contribution, and root cause.

Pattern catalog + data

Optimization Roadmap

Prioritized list of optimization opportunities with expected savings, implementation effort, and risk for each.

Roadmap document + spreadsheet

Validated Sample Implementations

Code or configuration for the 2–3 highest-impact optimizations, validated against real usage.

Sample code + validation data

Cost Operations Runbook

Documentation for ongoing cost monitoring: what to track, what thresholds to alert on, how to evaluate new optimization opportunities.

Runbook + monitoring templates

Engineering Walkthrough

Working session with your engineering and finance teams to walk through findings, validate priorities, and plan rollout.

90-minute session

// ENGAGEMENT SHAPE

Specific numbers, not approximations

// DURATION

3–5 weeks

Total engagement window

// TEAM SIZE

2 practitioners

Engineering-fluent, both senior

// CADENCE

Daily async updates

By 18:00 client timezone

// TYPICAL FINDINGS

30–60% potential savings

Range based on prior engagements

// SCOPE

Per-deployment or org-wide

Written in SOW

// STARTING PRICE

$19,500

Single-deployment engagement

// VALIDATION SAMPLE

2–3 optimizations

Implemented and validated in engagement

// POST-ENGAGEMENT

30-day implementation support

For the optimizations you deploy

// WHEN THIS IS RIGHT

Honest fit criteria

// THE RIGHT FIT

—

Your LLM API spend has grown past the point where the finance team is asking hard questions about it.

—

Engineering knows there's waste but doesn't have bandwidth to systematically audit it — you need outside instrumentation and analysis.

—

You're scaling AI features to more users or use cases and need confidence the per-user economics work.

—

You're considering migrating to a different model or provider and want a baseline of current spend before evaluating alternatives.

// THE WRONG FIT

—

Your LLM spend is under $5K/month — engagement value is proportional to spend, and small budgets don't recover the engagement cost.

—

Your AI usage is internal-only and experimental — cost optimization matters when there's production scale to optimize against.

—

You want vendor-specific cost analysis (only OpenAI pricing, only Anthropic pricing) — we work multi-provider; single-provider audits are simpler with vendor-supplied tooling.

—

You expect us to negotiate with vendors on your behalf — that's procurement work, not forensic analysis.

// RELATED ENGAGEMENTS

Where this connects to the rest of our work

Neural Hardening

Cost guards and rate limits are part of Neural Hardening; for runaway-cost prevention (not optimization), that's the engagement.

DETAILS →

Shadow-AI Recon

If you suspect cost is going to AI systems you don't know about, run this discovery engagement first.

DETAILS →

AI Risk Assessment

Pairs well when cost optimization is part of a broader AI governance initiative.

DETAILS →

LLM Cost Forensics engagements start from $19,500. Reply within 24h. NDA before scope.

BOOK THIS ENGAGEMENT →