LLM Cost Optimization Services for Enterprise AI
Relevance Lab reduces LLM cost management overhead through model routing, caching, token efficiency and inference cost optimization — built around your actual LLM spend management and unit economics.
- Baseline Token & Inference SpendToken usage and inference spend broken out by model, application and use case, across every LLM provider and self-hosted deployment in use.
- Optimize Model Routing & CachingModel routing that sends each request to the cheapest model capable of handling it, plus prompt and response caching to avoid redundant calls.
- Manage Context & Token EfficiencyPrompt engineering and context-window management that reduces token consumption per request without degrading output quality.
- Govern LLM Spend with Unit EconomicsCost-per-request and cost-per-user unit economics tracked and governed so LLM spend scales predictably with usage, not unpredictably with it.
Quick answerLLM cost optimization is the practice of reducing the cost of running large language model workloads — through model routing, prompt/response caching, token and context efficiency, and inference cost management — while keeping output quality where it needs to be. Relevance Lab delivers LLM cost optimization as a managed practice across every major model provider.
Turning token spend into a managed, predictable cost
LLM cost is driven by model choice, token count and call volume in ways that don't map cleanly onto traditional compute cost optimization. A single inefficient prompt pattern, replicated across millions of requests, can quietly become one of the largest line items in a company's AI spend.
Relevance Lab runs LLM cost optimization as its own discipline — routing requests to the right model, caching aggressively, tightening context usage, and tracking unit economics so LLM spend scales predictably with usage.
What an LLM cost optimization engagement gets you
- Model routing that matches task complexity to model cost
- Prompt and response caching that cuts redundant calls
- Context and token efficiency tuning
- Cost-per-request and cost-per-user unit economics
The LLM cost problems optimization solves
LLM cost waste compounds at request volume — small inefficiencies scale into real money fast.
Every request hits the biggest model
Simple classification or extraction tasks route to the same expensive model as complex reasoning tasks.
No caching for repeated calls
Identical or near-identical prompts get sent to the model again and again, paying full price every time.
Bloated context windows
Full conversation history and unnecessarily large retrieved context get sent on every call, driving up token cost.
No cost-per-request tracking
Without unit economics, teams can't tell whether an LLM-powered feature is sustainable as usage scales.
No cost attribution by feature
LLM spend gets lumped into one line item instead of being attributed to the specific feature or product driving it.
Inference infrastructure left unoptimized
Self-hosted or fine-tuned model inference runs on unoptimized infrastructure, leaving GPU efficiency gains on the table.
Four pillars of a Relevance Lab LLM cost optimization engagement
Each pillar can stand alone or run together as a continuous, managed practice.
Baseline Token & Inference Spend
Token usage and inference spend broken out by model, application and use case, across every LLM provider and self-hosted deployment.
- Token & inference spend audit
- Cost attribution by feature & use case
- Provider & model spend comparison
Optimize Model Routing & Caching
Model routing that sends each request to the cheapest model capable of handling it, plus prompt and response caching.
- Cost-aware model routing
- Prompt & response caching
- Fallback strategy for cost/quality tradeoffs
Manage Context & Token Efficiency
Prompt engineering and context-window management that reduces token consumption per request without degrading output quality.
- Prompt & context optimization
- RAG retrieval context tuning
- Conversation history management
Govern LLM Spend with Unit Economics
Cost-per-request and cost-per-user unit economics tracked and governed so LLM spend scales predictably with usage.
- Cost-per-request / cost-per-user tracking
- Budgets & anomaly alerts
- Feature-level chargeback
Explore the rest of our AI FinOps practice
LLM cost optimization is one of four AI FinOps disciplines Relevance Lab runs together.
AI Cost Optimization
Rightsizing GPU infrastructure, committed and spot pricing, and AI infrastructure cost reduction.
Explore AI Cost OptimizationAI Cost Management
Visibility, allocation, forecasting and governance for AI and GPU spend across every provider and platform.
Explore AI Cost ManagementGenAI FinOps
Control generative AI cost — model selection, fine-tuning, RAG and GenAI platform spend.
Explore GenAI FinOpsFinOps and cloud cost optimization, tuned by industry
Every industry hits cloud cost management differently. Our FinOps services adapt the same core practice to the constraints that matter most in your sector.
Financial Services
FinOps for banks, insurers and fintechs balances aggressive cloud cost optimization with the audit trails, tagging discipline and regulatory reporting that financial services compliance demands.
- Cost governance mapped to compliance & audit needs
- Chargeback across business units and trading desks
- Optimization for high-volume transaction workloads
Hi-Tech
Fast-scaling product and engineering teams get real-time cloud cost management and AI FinOps guardrails that keep pace with rapid deployment cycles, without slowing engineering down.
- Cost visibility by product, team and environment
- AI FinOps for GPU-heavy training and inference
- Automated rightsizing that keeps up with scale
Healthcare & Life Sciences
Research computing, genomics and clinical workloads bring bursty, high-cost cloud usage. Our FinOps services bring cost accountability without compromising data governance or research velocity.
- Cost controls for research & HPC workloads
- Governance aligned to healthcare data compliance
- Grant- and project-based cost allocation
Blogs and case studies on LLM cost optimization
Research Gateway and Amazon Bedrock: Governed AI Coding Assistants for Researchers
A real deployment managing LLM usage and inference cost for AI-assisted workflows at scale.
Read the Case StudyBlogFrom Dilemma to Differentiation: Building a Hybrid AI Cloud for the GenAI Era in Higher Education
How a hybrid AI cloud approach controls LLM and inference cost while enabling GenAI adoption at scale.
Read MoreBlogFinOps for Research Computing is Complex and Frustrating. Here's a Simpler Way.
Visibility and governance for LLM and AI inference spend as part of a unified research computing FinOps practice.
Read MoreLLM spend is one piece of the puzzle — explore our full FinOps services
Pair LLM cost optimization with cloud cost optimization, management and governance across AWS, Azure and GCP.
LLM cost optimization: frequently asked questions
Content last reviewed: September 2026
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