AI Cost Optimization Services for Enterprise AI
Relevance Lab reduces AI infrastructure cost optimization and AI spend management across training and inference — rightsizing GPU infrastructure, applying committed and spot pricing, and governing AI spend by model and team.
- Baseline AI & GPU SpendAn inventory of training and inference infrastructure, GPU utilization and spend broken out by model, team and project.
- Rightsize Training & Inference InfrastructureGPU instance types, batch sizing and autoscaling for inference endpoints tuned to real utilization instead of worst-case provisioning.
- Apply Committed & Spot Pricing for GPU CapacityReserved or committed GPU capacity for steady-state training, with spot/preemptible capacity for interruption-tolerant jobs.
- Govern AI Spend with Tagging & BudgetsCost allocation by model, team and project, paired with budgets and anomaly alerts scoped specifically to AI workloads.
Quick answerAI cost optimization is the practice of reducing the cost of training and running AI workloads — rightsizing GPU infrastructure, applying committed and spot pricing where it fits, and governing spend by model, team and project — without slowing down AI development. Relevance Lab delivers AI cost optimization as a managed practice across AWS, Azure and Google Cloud.
Turning AI infrastructure spend into a managed line item
AI and GPU workloads behave nothing like traditional compute — usage spikes hard during training, sits idle between runs, and scales unpredictably with inference traffic. Generic rightsizing playbooks built for EC2 or VMs don't capture that pattern.
Relevance Lab runs AI cost optimization as its own discipline — rightsizing training and inference infrastructure, applying committed and spot GPU pricing where workloads tolerate it, and governing spend so AI adoption doesn't outrun cost control.
What an AI cost optimization engagement gets you
- Training and inference infrastructure rightsized to real usage
- Committed and spot GPU capacity strategy that fits your workloads
- Idle GPU and inference capacity identified and eliminated
- Cost allocation by model, team and project
The AI cost problems optimization solves
AI and GPU spend waste hides differently than traditional cloud waste. Here's where it shows up.
Oversized GPU instances
GPU instance types picked for the largest anticipated training run keep running long after that run finishes.
Idle training capacity
GPU clusters provisioned for a project phase sit idle between training runs, burning budget with nothing to show for it.
No spot/preemptible strategy
Interruption-tolerant training jobs run on full on-demand GPU pricing because nobody's built the checkpointing to use spot safely.
Over-provisioned inference endpoints
Inference endpoints sized for peak traffic run at a fraction of utilization the rest of the time.
No cost allocation by model or team
Without tagging by model, team or project, nobody can tell which AI initiative is actually driving the GPU bill.
Unmanaged AI infrastructure sprawl
As more teams adopt AI, GPU and accelerator spend becomes one of the fastest-growing, least-governed parts of the cloud bill.
Four pillars of a Relevance Lab AI cost optimization engagement
Each pillar can stand alone or run together as a continuous, managed practice.
Baseline AI & GPU Spend
An inventory of training and inference infrastructure, GPU utilization and spend broken out by model, team and project.
- GPU/accelerator utilization audit
- Spend attribution by model & team
- Idle capacity identification
Rightsize Training & Inference Infrastructure
GPU instance types, batch sizing and autoscaling for inference endpoints tuned to real utilization.
- Training infrastructure rightsizing
- Inference endpoint autoscaling
- Serverless/batch inference evaluation
Apply Committed & Spot Pricing for GPU
Reserved or committed GPU capacity for steady-state training, with spot/preemptible capacity for interruption-tolerant jobs.
- Committed GPU capacity strategy
- Spot/preemptible strategy with checkpointing
- Coverage & utilization tracking
Govern AI Spend with Tagging & Budgets
Cost allocation by model, team and project, paired with budgets and anomaly alerts scoped to AI workloads.
- AI-specific tagging standard
- Budgets & anomaly alerts
- Model/team-level chargeback
Explore the rest of our AI FinOps practice
AI cost optimization is one of four AI FinOps disciplines Relevance Lab runs together.
AI 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 FinOpsLLM Cost Optimization
Model routing, caching, token efficiency and inference cost optimization for enterprise LLMs.
Explore LLM Cost OptimizationFinOps 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 AI cost optimization
Accelerating AI-Enabled Research with Secure AI Workspaces at a Leading Research University
GPU workspaces with built-in cost tracking — a direct precedent for governed, cost-controlled AI infrastructure at scale.
Read the Case StudyBlogFinOps for Research Computing is Complex and Frustrating. Here's a Simpler Way.
How Research FinOps brings visibility, governance and accountability to GPU-heavy research and AI computing.
Read MoreBlogStruggling with Unmanaged Cloud Assets across Providers AWS, Azure, & GCP?
A practical framework for auditing unmanaged, unmonitored assets — including idle GPU and AI infrastructure.
Read MoreAI spend is one piece of the puzzle — explore our full FinOps services
Pair AI cost optimization with cloud cost optimization, management and governance across AWS, Azure and GCP.
AI cost optimization: frequently asked questions
Content last reviewed: September 2026
Ready to control your AI infrastructure spend?
Book a discovery session and get an AI cost optimization assessment tailored to your workloads.
Get a FinOps & cloud cost assessment
Tell us about your cloud environment and a FinOps specialist will get back to you with next steps.