AI FinOps

The only platform built specifically for the massive financial overhead of AI infrastructure. Manage token costs, inference efficiency, and training ROI across the fragmented AI provider landscape.

Optimize AI Spend

AI Ecosystem Integration

OpenAI & Anthropic

Token Attribution

Granular tracking of prompt and completion tokens. Identifying high-cost users and optimizing model selection for Claude 3.5 and GPT-4o.

AWS Bedrock

Model Orchestration

Managing provisioned throughput and optimizing costs for Llama, Titan, and Claude models within the AWS VPC.

Azure OpenAI

Enterprise Quotas

Monitoring PTU (Provisioned Throughput Units) and managing shared capacity across global business units.

GCP Vertex AI

MLOps Efficiency

Tracking costs for Gemini models and managing the financial overhead of custom training pipelines on Vertex.

Oracle Data Science

GPU Clusters

Optimizing the cost of bare-metal GPU clusters for large-scale training and inference workloads.

Private LLMs

Inference Density

Maximizing throughput per dollar for self-hosted models on Kubernetes clusters with H100/A100 support.

AI-Native Features

Token-level cost attribution for LLMs (OpenAI, Anthropic, Llama)

Inference vs. Training cost analysis and forecasting

Model efficiency benchmarking and performance tracking

Automated scaling for inference clusters based on ROI

Cross-provider model cost comparison engine

LLM Intelligence

ZOLIX identifies the most cost-effective model for your specific workload, balancing latency, accuracy, and cost.

50%

Reduction in Inference Costs