AI observability tools overview

Artificial Intelligence has evolved from an experimental technology into a key tool for innovation. However, the path from prototype to successful implementation of AI development in an enterprise setting is challenging.

This article explores how the best AI observability tools help companies move from simple metric monitoring to a deep understanding of model behaviour. We will explain how enterprises can leverage these platforms to monitor LLMs, prevent errors, detect bias, and ensure the stable operation of AI systems.

What are AI observability tools, and why do they matter

AI observability is the ability to understand an AI system's internal state and behaviour based on its external data, like logs, metrics, and traces. If monitoring answers the question "What broke?" then observability answers the question "Why did it break and how can we fix it?"

Monitoring is reactive and tracks predefined indicators (known issues), while observability is a preventive approach that helps explore the system and identify previously unknown anomalies ("unknown unknowns"). The importance of AI observability tools is on the rise:

• AI systems and LLMs are complex, dynamic, and often "black boxes";
• Limited observability makes it difficult to detect anomalies, trace root causes, and maintain consistent performance at scale;
• Mature observability practices can reduce downtime costs and accelerate innovation;

This is also proven by the current market trends: the global AI observability market is expected to reach $10.7B by 2033, growing at a CAGR of 22.5% over 9 years.

Real problems that AI observability tools solve

Without proper oversight, AI systems in production face problems that can lead to significant financial and reputational losses. AI observability platforms are designed to solve these risks. Let's take a closer look at the most typical challenges.

Low performance and model degradation

A persistent challenge in AI initiatives is the high failure rate: 87 percent of projects never make it to production. Even among those that do, model performance often degrades over time due to changes in the operating environment.

Two primary contributors to this degradation are:

• Data drift: the statistical properties of the input data change over time, and they no longer match the data on which the model was trained. For example, a model for predicting demand in retail, trained on pre-pandemic data, will be ineffective in the new reality.
• Concept drift: the relationship between the input data and the target variable changes. A classic example is fraud detection models, where attackers constantly change their tactics.

The top AI observability tools continuously monitor input and output data distributions, comparing them with a baseline dataset. They then automatically notify about detected drift, allowing teams to retrain the model in time.

Lack of transparency in decision-making

AI models, especially deep neural networks, are usually "black boxes", offering little insight into how decisions are made. This lack of transparency introduces significant risk, especially in highly regulated domains such as finance and healthcare.

AI agent observability tools address this challenge by incorporating two critical capabilities:

• Explainable AI techniques: SHAP or LIME are used to interpret individual predictions, showing the contribution of each feature to the model's output;
• Algorithmic bias: Platforms support segmented performance analysis by protected attributes (e.g. race, gender, age) and track fairness metrics to ensure regulatory alignment and ethical compliance.

While AI systems face many challenges in production, model degradation and lack of transparency remain among the most urgent, making them central focus areas for observability platforms.

Explore more about the applications of explainable AI

How enterprises monitor LLMs in production

Large language models and AI agents have brought a new wave of breakthroughs and specific challenges for monitoring. The output of traditional Machine Learning models is structured, like a class or a number. But LLMs produce unstructured text, which needs special ways to be observed.

Companies use different enterprise AI observability tools for extended monitoring of LLMs in production, tracking specific aspects such as:

• Response stability and quality: monitoring for hallucinations, toxicity, and relevance of responses. This procedure uses heuristic methods and other LLMs as "judges" (LLM-as-a-Judge) to assess quality.
• Prompt drift and prompt engineering: tracking changes in user queries and their structure. Platforms allow for analyzing which prompts lead to incorrect or improper responses and provide tools for their iterative improvement.
• Performance degradation and cost: monitoring key operational metrics like latency and token usage. Since the cost of using LLMs directly depends on the number of processed tokens, tracking them is necessary for cost control.
• Security and data leakage: scanning responses for personal identifiable information (PII) and preventing attacks like prompt injection, where malicious actors try to bypass the model's safety mechanisms with specially crafted queries.

For example, financial institutions use LLMs to analyze market reports, serve customers, and provide financial advice. Observability is critical here to ensure regulatory compliance, prevent leakage of confidential customer data, and avoid hallucinations that could lead to poor financial decisions. The platform tracks every request-response pair, creating an audit trail for regulators.

What value bring AI observability tools?

Detecting problems that slowly degrade AI systems is one of the key functions of AI observability. The top observability tools for AI agents use detailed statistical methods to automate this process, turning it from manual analysis into continuous monitoring.

1. Data and concept drift detection

The basis for drift detection is comparing the distribution of data in production with a baseline distribution, which is usually the training data. Platforms automatically calculate statistical divergence metrics like the Population Stability Index (PSI) or Kullback-Leibler divergence.

When these indicators exceed set thresholds, the system generates an alert. That allows teams to immediately respond to changes in data before they significantly impact model accuracy.

2. Bias detection

Platforms allow for model performance analysis across various demographic segments to identify bias. Users can define "protected" attributes like gender, race, and age to track whether there are significant differences in key metrics. They usually include accuracy and false positive/negative rates.

Fiddler, for example, enables visualization of performance discrepancies and the calculation of fairness metrics like "demographic parity" or "equality of opportunity," which is essential for complying with ethical norms and regulatory requirements.

3. Data quality monitoring

Unlike drift, which results from changes in data distributions or relationships, data quality issues typically stem from technical failures within data pipelines. These include:

• Errors in ETL processes;
• Schema changes in source systems;
• Sensor malfunctions and other upstream anomalies.

AI observability tools monitor such anomalies in real time. It sends out alerts when there are an unusually high number of null values, when new categories show up in categorical features, or when values are outside the expected range. This enables teams to quickly identify and eliminate problems at the data source, preventing useless data from being fed into the model.

Best AI observability tools enterprises use today

Several platforms stand out for their practical value in enterprise AI monitoring. Below are the AI observability tools most commonly adopted to support LLMs, agents, and production-scale models.

Arize AI

Arize is a unified AI engineering platform covering the entire model lifecycle from experimentation to production monitoring. It strongly focuses on LLMs and AI agents and supports open standards through its open-source projects Phoenix and OpenInference.

Key features include:

• Full model lifecycle support (experimentation to monitoring);
• Specialization in LLMs and AI agents;
• Open-source integration (Phoenix, OpenInference);
• Real-time model performance monitoring;
• Root cause and drift analysis;

Access and pricing models: It has free access and custom pricing for enterprise clients.

WhyLabs

WhyLabs is an AI observability platform known for its privacy-oriented approach. It works with statistical profiles rather than raw data, making it perfect for industries with strict confidentiality requirements.

Key features include:

• Privacy-first design (no raw data used);
• Statistical data profiling;
• Scalable for large ML deployments;
• Integration with the WhyLogs open-source library;
• Ideal for regulated and sensitive data industries.

Access and pricing models: It's free for individual users, but companies should expect to pay $125+ monthly.

Fiddler AI

Fiddler is a platform that strongly focuses on responsible AI, offering deep capabilities for explainability, fairness monitoring, and governance, risk, and compliance (GRC). This makes it a good choice for regulated industries.

Key features include:

• Advanced explainability tools;
• Fairness and bias detection;
• GRC features;
• Real-time AI monitoring;
• Tailored for regulated environments.

Access and pricing models: Pricing is only available on demand.

Let's take a look at these solutions and compare them to other gen AI observability tools in a comparison table.

How to choose the best AI observability platform for your needs

Choosing the right AI observability platform is a strategic decision that entirely depends on team's current needs. It requires identifying all key user groups involved in development, risk management, and operations.

• Data science and ML teams;
• Risk and compliance teams;
• Product and operations teams.

Assessing the existing tech stack and business objectives is equally important. Whether the focus is on LLMs, tabular data, or computer vision, each requires a distinct observability approach. The table below outlines recommended platforms based on specific technologies and operational needs.

Summary

Implementing AI observability is a strategic investment in your Artificial Intelligence systems' reliability, responsibility, and effectiveness. These tools transform "black boxes" into transparent assets, allowing enterprises to avoid costly mistakes and confidently scale innovation.

If you plan to implement AI observability in your project, N-iX will help you get all the expertise you need. Contact us to find the right observability strategy for your needs.