Athena AI Cost Optimization™

Cut Your AI Infrastructure Costs by Up to 40%.

We audit your GPU workloads, AI agent infrastructure, and cloud spend — then deliver a concrete savings roadmap in 72 hours. No long retainers. Proven to save $1M+/year for AI-first companies.

Typical First-Year Savings$200K – $1M+
Read-only access 72h turnaround No agents to install
$1M+Saved for single client / year
40%Average GPU cost reduction
72hFrom first call to savings plan
0Service disruptions during optimisation

Where Your Savings Come From

Four areas where AI-first companies consistently overpay — and how we fix them.

GPU Workload Optimisation

Migrate on-demand GPU instances to Spot with Karpenter dynamic scheduling and automatic checkpointing.

Up to 70% GPU cost reduction

Inference Endpoint Efficiency

Right-size KServe and Triton endpoints, enable scale-to-zero, and optimise batching strategies.

Up to 60% inference cost reduction

Storage Lifecycle Optimisation

Migrate cold AI datasets, model artefacts, and logs to S3 Glacier with intelligent tiering policies.

Up to 80% storage cost reduction

FinOps Governance

Kubecost dashboards, per-team GPU budget alerts, and monthly cost review cadence.

Ongoing waste prevention

How It Works

From first call to savings roadmap in 72 hours.

01
Day 1

Connect

Read-only cloud access. No agents. No code changes. We connect to AWS Cost Explorer, CloudWatch, and your Kubernetes cluster.

02
Day 1–2

Audit

Full profiling of GPU workloads, inference endpoints, AI agent pipelines, and storage buckets.

03
Day 2–3

Analyse

Identify waste: oversized instances, idle GPUs, hot storage with cold access patterns, unoptimized inference batching.

04
72h total

Deliver

Concrete savings roadmap with prioritised actions, estimated impact per item, and implementation guide.

What You Get

A complete savings roadmap — not a slide deck. Concrete actions with estimated impact per item.

  • Full AI infrastructure cost breakdown by service and workload
  • GPU utilisation report with idle & oversized instance identification
  • Karpenter + Spot instance migration plan with projected savings
  • Inference endpoint optimisation (KServe, Triton batching)
  • S3 lifecycle policy recommendations & Glacier migration plan
  • FinOps dashboard setup guide (Kubecost / CloudHealth)
  • Prioritised savings roadmap with effort/impact matrix
  • Expected annual savings estimate — before you commit to anything
O
Outrider
AI-First Autonomous Logistics · North America
Challenge

GPU inference costs and unoptimised AI agent infrastructure consuming over $1M/year in avoidable AWS spend. On-demand GPU instances running 24/7, hot S3 storage for cold model artefacts, and no FinOps visibility.

What We Did
  • Migrated GPU inference fleet to Karpenter-managed Spot instances with dynamic CPU+GPU node pools
  • Implemented S3 Glacier lifecycle policies for model artefacts and training datasets
  • Optimised Triton inference batching and enabled scale-to-zero for low-traffic endpoints
  • Deployed Kubecost with per-team budget tracking and anomaly alerts
$1M+/year saved · 40% GPU cost reduction · Zero service disruption
KarpenterGPU SpotS3 GlacierKServeKubecostTriton

Who This Is For

Any organisation with meaningful AI infrastructure spend.

AI-First Startups

Series A–C with GPU-heavy inference or training workloads

Enterprise AI Teams

Internal AI platforms with growing cloud bills

CTOs & VPs of Infra

Accountable for cloud costs in AI-driven organisations

PE-Backed Companies

Improving EBITDA margins ahead of due diligence or exit

Find Out How Much You're Overpaying

Free 72-hour audit. No commitment. We show you the savings before you decide to act.

Read-only access only No agents or code changes Savings estimate before you commit
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