LLM Inference Cost Assessment Tool

LLM Inference Cost Assessment & Decision Framework

Evaluate your LLM deployment maturity across 5 dimensions and calculate if you should route to hosted APIs or deploy dedicated GPU VM nodes.

Company Profile

Company Name

Monthly Token Volume (Millions)

Primary API Provider

Framework Deliverables

Amortization Model

Compare VM spot prices and engineer hourly rates against linear token consumption.

Routing Optimization

Calculate savings from triaging query complexity categories.

Prompt Caching Gates

Assess performance enhancements and dollar savings using dynamic TTL caches.

Hardware & Hosting Amortization

Step 1 of 5

1. Where are your primary inference models hosted?

Public serverless API endpoints only, no local VPC infrastructure. Private VM instances running model servers (Ollama, Hugging Face) without active scale-down. Managed spot-instance clusters (Kubernetes) running optimized engines (vLLM, TensorRT-LLM) scaling dynamically.

API Routing & Load Balancing

Step 2 of 5

2. How are query requests routed to models?

Single model endpoint hardcoded (e.g. GPT-4o for all inputs). Static triage (e.g. simple rule matches switch to cheaper model sizes). Dynamic routing gateway analyzing complexity and routing to local open-weights or API models.

Cache Efficiency & Hit Rates

Step 3 of 5

3. What caching strategies are active on your prompt pipelines?

None. Full context/prompt inputs sent raw on every transaction. Static prompt prefix caching enabled via supported APIs (Claude caching). Global caching proxy active with dynamic TTL and local semantic lookup.

Evaluation & Output Integrity

Step 4 of 5

4. How is model output quality checked when using smaller models?

Manual human evaluation only, done occasionally. Static assertion checklists and structure validations in CI/CD pipeline. Automated LLM-as-a-judge regression tests checking semantic parity in real-time.

Operational FinOps Lifecycle

Step 5 of 5

5. How are your model expenditures tracked?

Part of general server/SaaS bills; no visibility on user token consumption. Daily API logs mapped to specific departments or feature flags. Real-time cost dashboards; anomalous usage patterns trigger auto rate limits.

LLM Cost Comparison Model Calculator

Calculator Inputs

Monthly Volume (M tokens)

Input API cost ($/1M tokens)

Output API cost ($/1M tokens)

Hosting Dedicated GPU ($/hour)

Engineer Support Hours / mo

Unit Economics Comparison

Total Monthly Tokens

160M

Projected API Monthly Cost

$720.00

Self-Hosted Dedicated GPU Cost

$4,420.00

Recommended Infrastructure

Hosted API Model (Lower Volume)

Decision Advice

API hosting is cheaper at this volume. Dedicated hosting will become cost-effective when traffic exceeds 980M tokens monthly.

Maturity Scoreboard Matrix — Enterprise AI Corp

Hardware

Routing

Caching

Evaluation

FinOps

Weighted Score Summary

3.0

Ad-hoc
No controls

Exploring
Basic APIs

Scaling
Managed Cache

Optimizing
Router proxy

Leading
Spot clusters

Gap Analysis & Action Matrix

Maturity Gap	Dimension	Severity	Optimization Effort	Priority
Prefix caching not fully structured for long prompt blocks	Caching	High	Low	#1
Dedicated VMs idle during night hours	Hardware	Medium	Medium	#2
Dynamic classification router not yet triaging queries	Routing	Medium	Medium	#3
CI/CD automated regression eval tests absent	Evaluation	Low	Low	#4

90-Day FinOps Execution Sprints

Days 1-30 — Quick Caching Wins

Reorder prompt structures to place static instructions first. Enable prefix caching on cloud API client objects. Establish session-level token tracking parameters.

Days 31-60 — Routing Proxy Deployment

Deploy centralized triage proxy handler. Split incoming queries: simple classifications go to smaller model endpoints, deep lookups go to premium models.

Days 61-90 — Spot Instance Scaling

Migrate high-volume pipelines to dedicated spot cloud instance GPU VMs running vLLM. Connect logging telemetry to Board level dashboard portals.

Download Completed Toolkit Files

Scorecard Snapshot

Maturity Level

Scaling (3.0 / 5.0)

Monthly Tokens

160M

Blended Priority

Prefix Prompt Caching

Download Files

📊 Download Excel Workbook (.xlsx) 📄 Download Executive Brief (.pdf) ✍ Download Facilitator Guide (.docx) 🖨 Download Scorecard (.pdf)