10 Best AI Model Monitoring Platforms to Use in 2026

The deployment of AI models by businesses is widespread. However, the work does not end with the deployment. Businesses have to track the performance of the deployed models and address several challenges like model drift and data and errors.

In this article, I review and compare some of the model monitoring platforms that help businesses assess the quality of the data used to build AI models, operational issues, and other challenges.

I consider the products’ main functions and features and discuss real world situations that illustrate the value of the products. In addition, I consider factors that help businesses evaluate the products.

What Is AI Model Monitoring?

AI model monitoring refers to the process of examining the performance of a deployed AI model. Constant monitoring of the model becomes essential for various reasons including but not limited to, the fact that models may start drifting over a period of time, biases may be introduced in the model, the input data may change, etc.

It is essential that organizations monitor the performance of the models in order to be proactive and address the issues that may be experienced by end users. Most of the monitoring solutions for AI provide recommendations for improvements and provide alerts to the user.

Historical performance data is essential for quick and easy troubleshooting. Continuous performance monitoring ensures that the models deployed remain of high quality and help the organization remain compliant.

Key Points

Platform	Focus Area	Best Fit Use Case
Evidently AI	Drift detection, CI/CD gates	Teams needing open-source monitoring with tabular/text data
NannyML	Performance estimation without labels	Long label latency, tabular data monitoring
Alibi Detect	Outlier & adversarial detection	Kubernetes/Seldon stacks, multivariate drift
whylogs	Statistical profiling	High-throughput or privacy-constrained pipelines
Arize AI	Embedding drift, LLM tracing	Enterprise teams running multimodal & agent models
Fiddler AI	Explainability + monitoring	Regulated industries needing SHAP-based transparency
LangSmith	LangChain ecosystem observability	Debugging agent workflows, span-level tracing
Langfuse	Prompt & completion monitoring	LLM trace observability with cost tracking
Helicone	Proxy-based LLM monitoring	Quick integration for token usage & latency
Datadog AI Observability	AI observability within APM	Enterprises already using Datadog for infra monitoring

1. Evidently AI

Evidently provides monitoring capabilities for ML flows, especially for detecting various types of drifts and degeneration. It can evaluate different kinds of distribution shifts and missing data. It offers different metrics for model evaluation.

It is highly configurable, and can be integrated in a Python environment (including CI/CD pipelines) and in production. It can work with other solutions (e.g. MLflow, Airflow, and Jupyter). Its automation capabilities enable the creation of dashboards, which help provide different types of quality and drift metrics. It offers different levels of depth and simplicity.

It supports integration with other products, and can be further extended with new plugins. It offers flexibility for users and other organization’s IT staff. It can be embedded in other solutions.

Features

• Data and model performance monitoring
• Data drift and prediction drift detection
• Data quality checks and reports
• LLM and GenAI evaluation
• Open-source monitoring workflows

Pros

• Strong support for traditional ML monitoring
• Open-source option available
• Useful visual reports and dashboards
• Supports customizable evaluation metrics
• Suitable for development and production workflows

Cons

• Advanced enterprise capabilities may require additional setup
• Can require ML knowledge for customization
• Large monitoring workflows may need infrastructure management
• Some features are more technical for non-ML users
• Pricing can vary based on deployment and usage

2. NannyML

NannyML is one-of-a-kind in that it uses a subset of techniques from model monitoring to continuously assess model performance in production. As of now, NannyML can only monitor tabular models (classification and regression), and only within a few environments (Python and containerized). However, NannyML can alert users of model performance drift and provide confidence intervals.

Model drifts are alerted using statistical process control charts. Part of NannyML’s strength is its ability to utilize statistical monitoring, as well as model explainability to help build and maintain user trust in a production model, while limiting the unavailability of model labels.

Features

• Model performance monitoring
• Data drift detection
• Performance estimation without immediate ground truth
• Concept and data-shift analysis
• Production ML monitoring

Pros

• Useful when labels are delayed
• Focuses strongly on ML model performance
• Supports monitoring without continuous ground-truth data
• Helps identify performance degradation
• Suitable for production ML use cases

Cons

• Primarily focused on traditional ML monitoring
• LLM capabilities are not its main focus
• Requires understanding of statistical monitoring
• Advanced customization can require technical expertise
• May need complementary tools for broader observability

3. Alibi Detect

Alibi Detect focuses on outlier detection, adversarial detection, and drift evaluation, in addition to other abilities. It also provides support for varying data structures like tabular and text data. In addition, it provides evaluation of models based on various statistical methods. The library is built to be run on top of a scalable ML infrastructure.

It provides several detection models and allows the user to extend it with additional models. It can be used to build systems that require models with a certain level of adversarial robustness. It provides a multitude of models to detect drifts in data and be integrated in production systems that use several models to be trained on varying data.

Features

• Data drift detection
• Outlier detection
• Adversarial detection
• Online and offline detectors
• Support for multiple data types

Pros

• Open-source detection library
• Broad selection of detection algorithms
• Useful for custom ML pipelines
• Supports different data modalities
• Can be integrated directly into applications

Cons

• More developer-oriented than dashboard-oriented platforms
• Requires implementation work
• Not a complete enterprise observability platform by itself
• Monitoring workflows need additional infrastructure
• Less suitable for users looking for a fully managed interface

4. whylogs

Whylogs is a logging library for ML model monitoring. Whylogs was made to scale for large volumes of logging. Whylogs’s models can operate on images, text, and tables. Whylogs can give logging for missing values, value distributions, and validation schema.

The logging provided by whylogs can also be added at several points in a user’s pipeline and deployed locally, through cloud native pipelines, or can be integrated into observability products.

Whylogs integrates with MLFlow, Databricks, and major cloud products, which makes it well suited for monitoring at an enterprise level. A unique feature of whylogs is that logs can be added without allowing access to the raw data. Whylogs is optimized to log at high velocities to scale to large numbers of predictions, making it well suited for monitoring to satisfy compliance.

Features

• Analysis of data quality.
• Identification of data drift.
• Statistics summarization of data.
• continuous logging of data.

Pros:

• Open source project.
• Compact data profiling.
• Data summarization preserves privacy.
• Easily used with large data sets.

Cons:

• Focused on data health as opposed to model health.
• Requires additional effort for installation.
• Visualization requires additional effort.
• Higher-level observation requires integration with other products.
• Unsuited for non-technical users.

5. Arize AI

Arizie AI is a vendor for commercial observability. It provides models for tabular data, text, images and LLMs. It offers solutions for embedding drift and bias. It also provides LLC models and additional metrics for model performance and fairness.

The metrics for embedding similarity are also provided. The solutions are provided mainly through SaaS and hybrid cloud solutions. It provides out-of-the box integration for development frameworks for ML. As a result, it provides solutions for multimodal AI. It provides dashboards and tools for the health and performance of models.

It provides real-time insights andtrace tools for LLM prompts and completions. It focuses on enterprise models for agent based architectures and provides end-to-end solutions for observability for embedding and predictions. It primarily provides solutions for user interaction models.

Features:

• Surveillance of ML models.
• Tracing of Large Language Models.
• Identification of data and model drift.
• Evaluation of AI models.
• Monitoring of embeddings.

Pros:

• Integrated computing suite for ML and GenAI model observability.
• Rich suite for production model observability.
• LOMs and tracers for LLMs.
• Sufficient for enterprise AI teams.

Cons:

• Advanced features complex for new users.
• Enterprise features complex for new users toconfigure.
• Expensive for large scale use cases.
• Advanced users only.
• Complex for new users to setup and integrate with other products.

6. Fiddler AI

Fiddler AI is an enterprise product built for monitoring and explaining AI models based on text, images, and tables. It focuses on compliance and fairness. Some metrics supported by the platform include SHAP values, bias, drift, and model performance. Supported deployments include SaaS, on-prem, and hybrid. Thus, it supports regulated markets.

It integrates with popular ML frameworks (TensforFlow, PyTorch, scikit-learn) and EDWs. The explainability dashboards are useful for banking, medical, and insurance. Overall, Fiddler AI helps with AI governance. Being a monitoring and explaining platform, it is useful to regulated enterprises.

Features

• Model performance monitoring
• Explainable AI
• Bias and fairness monitoring
• Data and model drift detection
• LLM and GenAI observability

Pros

• Strong focus on explainability
• Supports responsible AI monitoring
• Covers traditional ML and GenAI
• Useful for enterprise governance workflows
• Provides model-performance visibility

Cons

• Enterprise-oriented features can require configuration
• May be more than smaller teams need
• Advanced workflows can have a learning curve
• Pricing information may require contacting sales
• Implementation can require ML and governance expertise

7. LangSmith

LangSmith, part of the LangChain ecosystem, specializes in LLM and agent observability. It provides request/trace/response models for prompts and completions as well as agent actions. It tracks various metrics (e.g. latency) and spans. It also tracks the usage of LLM and agent tokens and computes error rates.

It provides an SDK and a cloud-based dashboard. Being integrated with other components of the ecosystem, LangSmith is geared toward agent-based applications.

It provides span-level tracking which can be used to track a variety of components of an application and hence aids in debugging applications. Thus, it benefits developers of autonomous agents. LangSmith facilitates monitoring and, more importantly, debugging of autonomous agents.

Features

• LLM application tracking
• LLM prompt and response tracking
• LLM assessment
• Management of MLM datasets and experiments
• Agent observation

Pros:

• Supports LLM application ecosystem
• Offers detailed traces
• Fosters prompt level experimentation
• Assessment of ML models and agents

Cons:

• Limited to LLM application ecosystem
• Not focused on conventional ML application ecosystem
• Technically complex to use
• Expensive because of tracing
• Limited to LLM assessment

8. Langfuse

Langfuse is an open-source tool that helps monitor prompts and completions for LLMs. It can work with models like GPT and Hugging Face models and also supports user-provided LLMs. It provides metrics like the average cost per prompt and response time, as aswell as error rates. It has models available via both, self-hosted solutions and cloud.

It supports integrations with LLMs from OpenAI and Anthropic as well as LangChain. Its dashboards allow users to track and optimize the trade off between cost and reliability. It focuses on helping organizations that use a significant number of LLMs, typically larger organizations, to track the number of prompts and responses their organizations use as well as the cost associated with that use.

Features

• Tracing of LLM applications
• Management of prompts
• Assessment of LLM applications
• Tracking of tokens and costs
• User and session analytics

Pros:

• Free and open source LLM application observation
• Excellent tracing
• Token and cost information
• Conventional ML application ecosystem not supported

Cons

• Prompts and experiments level analysis
• Requires additional configuration for advanced features
• Enterprise level customizations are available

9. Helicone

Helicone provides users with a way to monitor their interactions with Large Language Models (LLMs) via proxy. This means that it can be used to monitor interactions with GPT and other API-based LLMs.

Helicone’s main strength is its quick implementation, which is achieved by acting as a proxy between its users and LLM providers. Metrics tracked include token usage, latency, error rates, and costs.

Cloud deployment is provided by Helicone, and no further configuration is needed. Because of its fast provisioning, and quick observability of LLM provider usage, Helicone is geared more towards business usage.

Helicone provides dashboards to show usage, allowing teams to adjust performance and costs. Developers can use Helicone to monitor LLM provider usage without the need to integrate an SDK.

Features

• LLM request monitoring
• API and model analytics
• Token and cost tracking
• Latency monitoring
• LLM observability dashboards

Pros

• Easy way to monitor LLM API usage
• Strong cost and token visibility
• Useful performance analytics
• Supports multiple LLM providers
• Developer-friendly monitoring approach

Cons

• Primarily designed for LLM applications
• Traditional ML monitoring is limited
• Advanced use cases may require customization
• High-volume usage can increase costs
• Governance capabilities may require additional tools

10. Datadog AI Observability

Datadog AI Observability builds on Datadog APM to provide AI-powered observability for tabular, text, vision and LLM workloads. It tracks key metrics such as drift, latency and error rates, along with traditional resource metrics.

Currently it offers only a SaaS option, but they claim it is designed to handle large scale deployments. It provides integrations to various ML/AI frameworks (e.g. TensorFlow, Hugging Face) and is cloud-native.

Thus, it is a good choice for organizations that are already customers of Datadog and want a unified ML/AI infrastructure observability platform. The AI dashboards provide system and ML metrics integrations. From an infrastructure and application as well as ML/AI integration observability standpoint, it is very competitive.

Datadog AI Observability

Features

• AI/LLM application observability
• LLM tracing and monitoring
• Performance and latency tracking
• Token and usage monitoring
• Integration with broader infrastructure observability

Pros

• Connects AI monitoring with infrastructure monitoring
• Strong enterprise observability ecosystem
• Wide range of integrations
• Useful dashboards and alerting
• Suitable for organizations already using Datadog

Cons

• Can be complex for teams new to observability platforms
• Pricing can become complicated at scale
• Broader platform may include capabilities unnecessary for smaller teams
• Configuration can require technical expertise
• AI-specific monitoring may work best when integrated with the wider Datadog ecosystem

AI Model Monitoring vs AI Observability vs LLM Observability

Comparison Point	AI Model Monitoring	AI Observability	LLM Observability
Primary Focus	Tracks model performance and health	Provides end-to-end visibility across AI systems	Monitors LLM and generative AI applications
Main Goal	Maintain model accuracy and reliability	Understand the complete AI system behavior	Improve LLM quality, performance, and reliability
Typical Models	ML and predictive models	ML, AI agents, GenAI, and ML pipelines	LLMs and GenAI models
Key Metrics	Accuracy, drift, latency, errors	Performance, data quality, infrastructure, model behavior	Tokens, latency, cost, hallucinations, relevance
Data Monitoring	Input and output data	Data, models, infrastructure, and workflows	Prompts, responses, embeddings, and context
Drift Detection	Data drift and model drift	Data, model, system, and behavioral changes	Prompt, response, embedding, and behavior changes
Tracing	Usually limited	End-to-end AI workflow tracing	Detailed LLM request and agent tracing
LLM-Specific Analysis	Limited	Supported depending on platform	Core capability
Cost Tracking	Usually not central	May include AI infrastructure costs	Token and inference cost tracking
Best Use Case	Production ML model health	Enterprise-wide AI system visibility	LLM apps, RAG systems, and AI agents
Example Alerts	Accuracy drop or data drift	Pipeline failure or system degradation	Hallucination, latency, token spike, or poor response quality
Scope	Model-centric	System-centric	LLM application-centric

Conclusion

In summary, Evidently AI, NannyML, Alibi Detect, whylogs, Arize AI, Fiddler AI, LangSmith and Langfuse, Helicone, and Datadog AI Observability, offer a complete solution to monitor AI models and help organizations gain trust in artificial intelligence they deploy.

These solutions help detect AI model drift, and provide other statistical and insightful profiles regarding AI models. Furthermore, some of the solutions help organizations and businesses gain trust in large language models.

Some of the solutions offered are enterprise ready and help organizations gain explanation and understanding regarding their AI models. All solutions are SaaS ready and help organizations monitor their AI models deployed in various frameworks. These solutions are ready to assist organizations gain confidence and trust in artificial intelligence.

FAQ