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Peter Kirkham

AI Observability vs AI Analytics

AI Observability and AI analytics are different and in this article we deep dive into the definitions and differences between them.

June 4, 2024


Props is an AI analytics platform that enables our customers to understand their user behavior and gain insights that will help them make better product decisions. But aren't there loads of these observability tools? Well… yes, but in this article I go through the differences between observability and Analytics and why you should want both.

AI observability and AI analytics are both critical for understanding and improving AI systems, but they focus on different aspects and serve distinct purposes. Here's a breakdown of each:

AI Observability

Definition: AI observability refers to the ability to monitor and understand the internal workings and behavior of AI systems. It involves collecting and analyzing data related to the system's performance, usage, and health.

Key Aspects:

  1. Monitoring: Continuous tracking of the AI system's performance metrics, such as response time, error rates, resource usage, and throughput.

  2. Logging: Recording detailed logs of the system's operations, including inputs, outputs, and intermediate states.

  3. Tracing: Following the flow of requests and data through the system to understand dependencies and pinpoint issues.

  4. Alerting: Setting up alerts to notify when performance degrades or anomalies are detected.


  • Helps in diagnosing and fixing issues quickly.

  • Provides insights into the system's health and operational efficiency.

  • Enables proactive maintenance by detecting anomalies early.

  • Ensures compliance with SLAs (Service Level Agreements).

AI Analytics

Definition: AI analytics involves analyzing both prompts and data generated by AI systems to extract actionable insights. It focuses on understanding the system's inputs and outputs to determine effectiveness in achieving its objectives.

Key Aspects:

  1. Performance Analysis: Evaluating the accuracy, precision, recall, and other performance metrics of AI models.

  2. User Behavior Analysis: Understanding how users interact with the AI system and how it impacts user experience.

  3. Business Impact Analysis: Measuring the impact of the AI system on business goals, such as revenue growth, cost reduction, and customer satisfaction.

  4. Trend Analysis: Identifying trends and patterns in the data to inform future decisions and improvements.


  • Informs decision-making with data-driven insights.

  • See a wider picture of how changes in prompts or models affect the whole system.

  • Identifies areas for improvement.

  • Demonstrates the value and ROI (Return on Investment) of AI initiatives.



AI Observability

AI Analytics


Monitoring and understanding internal behavior

Analyzing outcomes and understanding user behavior

Primary Purpose

Ensuring system health and operational efficiency

Extracting actionable insights and informing decisions

Key Activities

Monitoring, logging, tracing, alerting

Performance analysis, user behavior analysis, business impact analysis, trend analysis


Quick issue diagnosis, proactive maintenance, SLA compliance

Data-driven decision-making, optimization, innovation, demonstrating ROI

In summary, while AI observability focuses on the internal state and operational health of AI systems, AI analytics is concerned with extracting insights from the outcomes and performance of these systems to drive business value and improvement. Both are essential for a comprehensive approach to managing and leveraging AI technologies effectively.

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