ML Model Monitoring Tools

Last update: December 27, 2024

Machine Learning (ML) model monitoring is an important step in the deployment and management of machine learning models. +Show More

Machine Learning (ML) model monitoring is an important step in the deployment and management of machine learning models. ML monitoring tools allow data scientists to monitor the performance of their models in production. ML model monitoring tools help organizations:

Track the accuracy and performance of the models in a production environment.
Detect input data issues and report any anomalies.
Gather data about model accuracy and data issues, and store captured data for visualization and analysis.

To be categorized as a ML model monitoring tool, a product must provide:

Detect and troubleshoot ML issues Should integrate with existing ML workflow.
Track model performance in real-time.
Send timely alerts and notifications for any performance degradation.

If you’d like to learn about the ecosystem consisting of ML Model Monitoring Tools and others, feel free to check AIMultiple Machine Learning.

How relevant, verifiable metrics drive AIMultiple’s rankings

AIMultiple uses relevant & verifiable metrics to evaluate vendors.

Metrics are selected based on typical enterprise procurement processes ensuring that market leaders, fast-growing challengers, feature-complete solutions and cost-effective solutions are ranked highly so they can be shortlisted.
Data regarding these metrics are collected from public sources as outlined in the “What are AIMultiple’s data sources?” section of this page.

There are 2 ways in which vendor metrics are processed to help prioritization:
1- Vendors are grouped within 4 metrics (customer satisfaction, market presence, growth and features) according to their performance in that metric.
2- Vendors that perform high in these metrics are ranked higher in the list.

The data used in each vendor’s ranking can be accessed by expanding the vendor’s row in the below list.
This page includes links to AIMultiple’s sponsors. Sponsored links are included in “Visit Website” buttons and ranked at the top of the list when results are sorted by “Sponsored”. Sponsors have no say over the ranking which is based on market data. Organic ranking can be seen by sorting by “AIMultiple” or other sorting approaches. For more on how AIMultiple works, please see the ethical standards that we follow and how we fund our research.

Products	Position	Customer satisfaction
Aporia	Leader	Satisfactory
Aporia is the machine learning observability platform. Fortune 500 companies and industry leaders – including BSH, Munich RE, Sixt, Lemonade, and Armis – trust Aporia to ensure their production models perform at their best, always. The platform empowers data science and ML teams to monitor, visualize, improve, and scale production ML. Aporia is on a mission to help organizations manage production models and trust their AI. With Aporia’s monitor builder, data scientists can easily create customized monitors for detecting a wide range of issues including data drift, bias, data integrity issues, and performance degradation. See into your production models, and easily derive insights to improve performance and achieve business goals. Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 4.80 / 5 based on ~50 reviews Market presence Company's number of employees 300-400 employees Company's social media followers 30k-40k followers Total funding $100-250m # of funding rounds 10 Latest funding date June 1, 2022 Last funding amount $100-250m Company Type of company private Founding year 2014
WhyLabs	Leader	Satisfactory
WhyLabs is an AI observability platform that prevents model performance degradation by allowing you to monitor your machine learning models in production. If you deploy an ML model but don’t have visibility into its performance, you risk doing damage to your business because the model stops working. Machine learning engineers and data scientists rely on the platform to monitor ML applications and data pipelines by surfacing and resolving data quality issues, data bias, and concept drift. These capabilities help AI builders reduce model failures, avoid downtime, and ensure customers are getting the best user experience. With out-of-the-box anomaly detection and purpose-built visualizations, WhyLabs eliminates the need for manual troubleshooting and reduces operational costs. Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 4.80 / 5 based on ~10 reviews Market presence Company's number of employees 50-100 employees Company's social media followers 10k-20k followers Total funding $10-50m # of funding rounds 2 Latest funding date November 4, 2021 Last funding amount $10-50m Company Type of company private Founding year 2019
Arize AI	Leader	Satisfactory
Arize AI is a Machine Learning Observabililty platform that helps ML practitioners successfully take models from research to production, with ease. Arize’s automated model monitoring and analytics platform help ML teams quickly detect issues the moment they emerge, troubleshoot why they happened, and improve overall model performance. By connecting offline training and validation datasets to online production data in a central inference store, ML teams are able to streamline model validation, drift detection, data quality checks, and model performance management. Arize AI acts as the guardrail on deployed AI, providing transparency and introspection into historically black box systems to ensure more effective and responsible AI. To learn more about Arize or machine learning observability and monitoring, visit our blog and resource hub! Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 4.20 / 5 based on ~20 reviews Market presence Company's number of employees 50-100 employees Company's social media followers 10k-20k followers Total funding $50-100m # of funding rounds 3 Latest funding date September 7, 2022 Last funding amount $10-50m Company Type of company private Founding year 2020
Neptune AI	Leader	Satisfactory
Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 4.60 / 5 based on ~10 reviews Market presence Company's number of employees 50-100 employees Company's social media followers 30k-40k followers Total funding $10-50m # of funding rounds 3 Latest funding date April 12, 2022 Last funding amount $5-10m Company Type of company private Founding year 2017
Fiddler	Leader	N/A
Fiddler is a pioneer in Model Performance Management for responsible AI. The Fiddler platform’s unified environment provides a common language, centralized controls, and actionable insights to operationalize ML/AI with trust. Model monitoring, explainable AI, analytics, and fairness capabilities address the unique challenges of building in-house stable and secure MLOps systems at scale. Unlike observability solutions, Fiddler integrates deep XAI and analytics to help you grow into advanced capabilities over time and build a framework for responsible AI practices. Fortune 500 organizations use Fiddler across training and production models to accelerate AI time-to-value and scale, build trusted AI solutions, and increase revenue . For more information, visit www.fiddler.ai or follow us on Twitter @fiddlerlabs. Sign up for a 14-day free trial: www.fiddler.ai/trial Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 4.65 / 5 based on 3 reviews Market presence Number of case studies 1-5 case Study Company's number of employees 50-100 employees Company's social media followers 10k-20k followers Total funding $10-50m # of funding rounds 8 Latest funding date July 6, 2023 Company Type of company private Founding year 2018
Qwak	Challenger	N/A
Fully managed ML engineering platform that includes everything data science teams need in order to deliver! Build system, Serving, Monitoring & Analytics, Feature Store and automations Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Average rating 5.00 / 5 based on 1 review Market presence Company's number of employees 50-100 employees Company's social media followers 5k-10k followers Total funding $10-50m # of funding rounds 4 Latest funding date March 1, 2023 Last funding amount $10-50m Company Type of company private Founding year 2020
Evidently AI	Challenger	N/A
Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Market presence Company's number of employees 5-10 employees Company's social media followers 5k-10k followers
Qualdo	Challenger	N/A
Basis for Evaluation We made these evaluations based on the following parameters; Customer satisfaction Market presence Company's social media followers 400-1k followers Company Type of company private

“-”: AIMultiple team has not yet verified that vendor provides the specified feature. AIMultiple team focuses on feature verification for top 10 vendors.

Sources

AIMultiple uses these data sources for ranking solutions and awarding badges in ML model monitoring tools:

2 vendor web domains

2 funding announcements

6 social media profiles

19 profiles on review platforms

ML Model Monitoring Leaders

According to the weighted combination of 4 metrics

What are ML model monitoring
customer satisfaction leaders?

Taking into account the latest metrics outlined below, these are the current ML model monitoring customer satisfaction leaders:

Which ML model monitoring solution provides the most customer satisfaction?

AIMultiple uses product and service reviews from multiple review platforms in determining customer satisfaction.

While deciding a product's level of customer satisfaction, AIMultiple takes into account its number of reviews, how reviewers rate it and the recency of reviews.

Number of reviews is important because it is easier to get a small number of high ratings than a high number of them.
Recency is important as products are always evolving.
Reviews older than 5 years are not taken into consideration
older than 12 months have reduced impact in average ratings in line with their date of publishing.

What are ML model monitoring
market leaders?

Taking into account the latest metrics outlined below, these are the current ML model monitoring market leaders:

Which one has collected the most reviews?

AIMultiple uses multiple datapoints in identifying market leaders:

Product line revenue (when available)
Number of reviews
Number of case studies
Number and experience of employees
Social media presence and engagement

Out of these, number of reviews information is available for all products and is summarized in the graph:

Aporia

Arize AI

WhyLabs

Neptune AI

Fiddler

What are the most mature ML model monitoring tools?

Which one has the most employees?

Which ML model monitoring companies have the most employees?

64 employees work for a typical company in this solution category which is 41 more than the number of employees for a typical company in the average solution category.

In most cases, companies need at least 10 employees to serve other businesses with a proven tech product or service. 6 companies with >10 employees are offering ml model monitoring tools. Top 3 products are developed by companies with a total of 268 employees. The largest company in this domain is Arize AI with more than 90 employees. Arize AI provides the ML model monitoring solution: Arize AI

Arize AI

Fiddler

Insights

What is the average customer size?

According to customer reviews, most common company size for ML model monitoring customers is 1-50 Employees. Customers with 1-50 Employees make up 68% of ML model monitoring customers. For an average Machine Learning solution, customers with 1-50 Employees make up 68% of total customers.

Where are ML model monitoring vendors' HQs located?

Trends

What is the level of interest in ML model monitoring tools?

This category was searched on average for 0 times per month on search engines in 2024. This number is still 0 in 2025. If we compare with other machine learning solutions, a typical solution was searched 3.6k times in 2024 and this decreased to 0 in 2025.

ML Model Monitoring Tools

Aporia

Basis for Evaluation

Customer satisfaction

Market presence

Company

WhyLabs

Basis for Evaluation

Customer satisfaction

Market presence

Company

Arize AI

Basis for Evaluation

Customer satisfaction

Market presence

Company

Neptune AI

Basis for Evaluation

Customer satisfaction

Market presence

Company

Fiddler

Basis for Evaluation

Customer satisfaction

Market presence

Company

Qwak

Basis for Evaluation

Customer satisfaction

Market presence

Company

Evidently AI

Basis for Evaluation

Customer satisfaction

Market presence

Qualdo

Basis for Evaluation

Customer satisfaction

Market presence

Company

Sources

ML Model Monitoring Leaders

What are ML model monitoringcustomer satisfaction leaders?

Which ML model monitoring solution provides the most customer satisfaction?

What are ML model monitoringmarket leaders?

Which one has collected the most reviews?

What are the most mature ML model monitoring tools?

Which ML model monitoring companies have the most employees?

Insights

What is the average customer size?

Where are ML model monitoring vendors' HQs located?

Trends

What is the level of interest in ML model monitoring tools?

Related Solutions

Artificial Intelligence chip

AutoML software

Machine Learning Software

ML Model Validation / Testing Tools

MLOps Platforms

No code AI tool / software

What are ML model monitoring
customer satisfaction leaders?

What are ML model monitoring
market leaders?