AI/GPU FINOPS

GPU Cost Intelligence for AI Teams

Know exactly what every model, GPU minute, and token costs you. Real-time visibility into your AI infrastructure spend.

NVIDIA AWS GCP Azure Kubernetes

GPU & AI Costs Are Exploding

Companies spend $500 to $25,000/day on GPUs with zero attribution to teams, models, or experiments.

LLM inference costs are unpredictable, hard to track per customer or feature, and growing month over month.

GPU clusters sit idle for 40%+ of the time while invoices keep growing every billing cycle.

Full-Stack AI Cost Visibility

MetaFinOps provides end-to-end visibility from GPU hardware utilization to individual API tokens. Map every dollar of AI spend to specific models, teams, customers, and business outcomes.

Our platform ingests telemetry from NVIDIA GPUs, Kubernetes schedulers, and LLM providers to build a unified cost model that finance, engineering, and ML teams can all trust.

Whether you are running fine-tuning jobs on A100s, serving inference on T4s, or calling OpenAI APIs, MetaFinOps normalizes and attributes every cost to the right owner.

Core Capabilities

Everything you need to understand, attribute, and optimize AI infrastructure costs.

GPU Utilization Tracking

Real-time monitoring of GPU compute and memory utilization per node, pod, and job across your entire fleet.

Idle GPU Detection

Automatically identify underutilized GPU clusters and get right-sizing recommendations to eliminate waste.

Token Cost Modeling

Track cost per token, per 1M tokens, per customer, and per feature across all LLM providers in one view.

Model-Level Cost Attribution

Map GPU time to specific models, endpoints, teams, and business units with granular cost breakdowns.

Multi-Cloud GPU Arbitrage

Compare GPU pricing across AWS, GCP, Azure, and CoreWeave for optimal placement and maximum savings.

Spot GPU Optimization

Intelligent scheduling and preemption handling for spot/preemptible GPU instances to cut costs up to 70%.

Cost Formulas We Track

Transparent cost models that map every dollar to its source.

GPU Compute Cost

GPU Cost = GPU hourly rate × GPU runtime × Number of GPUs

Token Inference Cost

Token Cost = (tokens_in + tokens_out) / 1M × price_per_million

Per-Customer Attribution

Per Customer = inference cost + embeddings cost + feature cost

Live Dashboard Preview

A real-time window into your AI infrastructure costs.

GPU Fleet Overview

Last updated: 2 min ago

AI Startup Case Study

A mid-stage AI startup reduced GPU idle time by 35% and achieved full cost-per-model visibility within 2 weeks of deploying MetaFinOps, saving $18,000/month on their cloud GPU bill.

Start Optimizing Your GPU Costs

Get full visibility into your AI infrastructure spend in minutes, not months.

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