Why does observability matter?

Understanding latency, cost, and token usage is vital when evaluating LLMs due to their impact on user experience, operational efficiency, and financial viability.

  • Latency influences the model's responsiveness, crucial for user satisfaction in real-time applications.

  • Cost is important for budgeting and scalability since it relates directly to computational resource use, and managing these costs is essential for economically scaling AI solutions.

  • Analyzing token usage provides insights into a model's efficiency by indicating the number of text pieces it processes to complete tasks. Optimizing token usage helps organizations reduce costs and improve processing times.

Regular evaluation of these factors ensures a balance between performance and cost, enables customization to specific enterprise needs, and supports ongoing improvements in AI deployments. This strategic assessment allows development teams to make informed decisions on model training, system design, and resource management.

Evaluable AI's Approach

Evaluable AI's dashboards offer a set of analytics tools that empower development teams and enterprises to delve deeply into key performance metrics such as latency, cost, and token usage. These dashboards provide real-time insights and detailed breakdowns, enabling teams to monitor and analyze the efficiency of their large language models with precision. Leveraging these data visualizations can help organizations identify specific areas for improvement and optimize their models to reduce operational costs.

The following three pages dive deeper into Evaluable AI's functionalities in this realm:

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