Large Language Models

Make every word count in your LLMs

Monitor and track embeddings to ensure your LLMs drive value and avoid hallucinations.

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Model Monitors & alerts

Get live alerts directly in Slack, MS Teams, or email when your LLMs experience drift, bias, performance degradation, or data integrity issues.

Detect any anomalies that may impact the quality and reliability of your LLMs, allowing for swift fine-tuning.

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ML dashboards & visibility

Gain a centralized view of all your LLM use cases under a single hub. Track activity (API calls), hallucinations, inference trends, data behavior, and model performance to ensure high response time and accuracy. 

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Production IR & explainability

Visualize your unstructured data with UMAP dimension reduction. Identify clusters to uncover patterns, detect embedding drift, perfect your prompts, and learn more about the hidden connections in your text.

Aporia Language Models Aporia LLM Model Type Graphic
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“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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