Track your LLM costs

Cost Tracking

Track every penny, query, and token to control your LLM costs and streamline budget planning.

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Unforeseen LLM costs impacting your bottom line?

Over 80% of organizations struggle with unexpected LLM-related costs. API calls can be costly, in-house model maintenance comes with a hefty price, and token usage adds up quickly. these unforeseen expenses impact budgeting, challenge operational efficiency, and threaten the value and dependability of your Gen-AI product.

Segmented budget insights

Comprehensive cost analysis for optimized LLM deployment

  • Analyze costs by task type, region, GPT version, and other pivotal segments.
  • Compare cost tracking against performance metrics for a 360-degree view of your LLM’s usage and costs.
  • Customize token tracking and cost management visibility to meet the needs of your LLM and align with your goals.

Token-based financial clarity

Decoding token trends for LLM budget planning

  • Understand token consumption patterns, ensuring no pricey surprises.
  • Real-time insights into average tokens per request, prompt, and completion, tailored for executive decision-making.
  • Leverage latest token consumption methods to convert current financial benchmarks into actionable cost management strategies.

Precision in LLM cost analysis

Spotting cost anomalies in real-time

  • Drive efficiency by surfacing non-cost-effective clusters early before they impact your business.
  • Immediately identify unexpected cost spikes, pinpoint their origin, and gain insights on how to mitigate them.
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Look what our customers have to say about us

“In a space that is developing fast and offerings multiple competing solutions, Aporia’s platform is full of great features and they consistently adopt sensible, intuitive approaches to managing the variety of models, datasets and deployment workflows that characterize most ML projects. They actively seek feedback and are quick to implement solutions to address pain points and meet needs as they arise.”

Felix D.

Principal, MLOps & Data Engineering

“As a company with AI at its core, we take our models in production seriously. Aporia allows us to gain full visibility into our models' performance and take full control of it."

Orr Shilon

ML Engineering Team Lead

“ML models are sensitive when it comes to application production data. This unique quality of AI necessitates a dedicated monitoring system to ensure their reliability. I anticipate that similar to application production workloads, monitoring ML models will – and should – become an industry standard.”

Aviram Cohen

VP R&D

“With Aporia's customizable ML monitoring, data science teams can easily build ML monitoring that fits their unique models and use cases. This is key to ensuring models are benefiting their organizations as intended. This truly is the next generation of MLOps observability.”

Guy Fighel

General Manager AIOps

“ML predictions are becoming more and more critical in the business flow. While training and benchmarking are fairly standardized, real-time production monitoring is still a visibility black hole. Monitoring ML models is as essential as monitoring your server’s response time. Aporia tackles this challenge head on.”

Daniel Sirota

Co-Founder | VP R&D

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