Evidently AI

A one-sentence summary

Clearly, AI is an open-source framework for AI evaluation and observability, which enables the assessment of the quality of LLMs, RAG systems, AI agents, data pipelines, and traditional machine learning models in experiments, regression tests, and production monitoring.

Tool Introduction

Obviously, Evidently AI is maintained by Evidently AI, Inc.; its core product is the Evidently library, which can be installed in a Python environment, along with a lightweight monitoring platform that can be deployed independently.

It is not responsible for training the underlying models; instead, it converts inputs, outputs, labels, and reference data into metrics, reports, test results, and trend panels, thereby helping teams identify issues such as hallucinations, declining retrieval quality, data drift, performance regression, and data quality problems.

Current product composition

Main functions

1. Reports: Evaluation reports

Reports are used to calculate and aggregate data, as well as to determine quality metrics for machine learning and LLMs; they are suitable for experimental analysis, debugging, baseline comparison, and batch testing in production.

• Use predefined quick combinations to set a set of common evaluation metrics.
• Add individual metrics or custom Python metrics per task.
• View the interaction results in the Notebook, or export them as JSON, Python dictionaries, and HTML files.
• Compare the current data with the reference data to analyze changes in distribution, quality, and model performance.

2. Test Suites – Sets of tests

Test Suites add pass/fail criteria to the reporting metrics, and can be used for data validation, model regression testing, and quality checks in continuous integration processes.

• Set threshold conditions of greater than, less than, or other values for the indicators.
• Some test conditions are automatically generated based on reference data, reducing the effort required for initial setup.
• Use the test results to make decisions regarding releases, rather than just displaying charts.
• Repeat the same test after updating the model, prompts, or retrieval path.

3. Evaluation of LLMs, RAG, and agents

Evidently can analyze chatbots, RAG, Copilot, AI agents, and other generative applications. Teams can combine rules, classifiers, similarity methods, and LLM reviewers.

4. Traditional machine learning evaluation

In addition to generative AI, Evidently also supports classification, regression, sorting, recommendation, and table data quality analysis, making it suitable for linking experimental evaluation with production monitoring.

5. Data drift and data quality

The platform can compare the current data with reference data to identify changes in field distribution, missing values, duplicate values, new categories, and abnormal ranges. Drift does not necessarily mean that the model has failed; it still needs to be interpreted in conjunction with labels, business metrics, and model quality.

6. Monitoring panel

The self-hosted UI allows saving evaluation snapshots per project, and it enables the creation of monitoring dashboards that show metrics and test results over time. The open-source version is suitable for lightweight deployments; the team is responsible for handling access control, backups, upgrades, and scaling.

7. Tracking and production evaluation

Tracely is used to capture the inputs, outputs, and intermediate steps of LLM applications, thereby enabling the transformation of production processes into analyzable datasets. Advanced timing evaluations, alerts, and code-free workflows are part of the capabilities offered by the commercial platform.

8. Synthetic data and prompt optimization

The current ecosystem includes capabilities for generating synthetic data and optimizing prompts, which can be used to create normal, boundary, or adversarial examples; these examples can then be used to compare different combinations of prompts, models, and parameters. Generated examples cannot replace real user data or manual red-team testing.

The workflow from experimentation to production

1. Identify the specific risks that need to be verified, such as hallucinations, retrieval relevance, data drift, or a decline in classification performance.
2. Prepare the current data, reference data, prediction results, labels, or LLM interaction records, and define the meaning of the fields.
3. Select the preset and generate a report with metrics; first, examine the distribution, failed examples, and abnormal groups.
4. Convert key metrics into Test Suites with thresholds, thereby creating quality checkpoints that can be run repeatedly.
5. Re-evaluate after making changes to the prompts, models, data, or code, and compare the differences in the results.
6. Write the reports to a self-hosted Workspace, and use the Dashboard to continuously monitor trends and test status.
7. Link failure outcomes to troubleshooting, rollback, retraining, or manual review processes.

Installation and Getting Started with the Open Source Version

The current project requires Python 3.10 or a higher version, which can be installed using Python’s package manager. For production projects, it is necessary to specify a particular version, and it is important to read the change notes and conduct regression tests before making any upgrades.

1. Create an independent Python environment and prepare a Pandas dataset or data that can be converted into a table.
2. Install the Evidently package; if LLM evaluation, Spark, or cloud storage is required, select the corresponding optional dependencies.
3. Define data fields, current data, and optional reference data, and select preset or individual indicators.
4. Run the Report and view it in the Notebook, or save it as HTML, JSON, or a Python dictionary.
5. Add test conditions for key metrics, and include the Test Suite in continuous integration or batch processing tasks.
6. First, verify the calculation costs and field mappings using sample data, and then integrate it with production data.

Self-hosted monitoring tutorial

1. Create a Workspace, choosing to use a local directory, a SQL-based database, or an object storage system compatible with S3.
2. In the evaluation task, the Report snapshots, tracking data, or datasets are written to the specified Workspace.
3. Start the Evidently UI service and verify that it can read the project data in the Workspace.
4. Create projects and Dashboard panels, and select the metrics and tests that need to be monitored on a continuous basis.
5. Add reverse proxies, authentication, encryption, backup, logging, and resource limits in the production environment.
6. Run batch evaluations on an hourly, daily basis, or whenever new tags arrive, and route any anomalies to the team’s response process.

Which users are it suitable for

• Data scientist: Compares changes in data, models, and features, and generates shareable analysis reports.
• Machine Learning Engineer: Integrate quality testing into the training, deployment, and batch processing pipelines.
• LLM application developers: Assess the quality of outputs from chatbots, RAG, agents, and Copilot.
• MLOps and platform team: Self-hosted unified evaluation results, tracking, and monitoring panels.
• AI product managers and quality teams: define quality criteria, examine failed samples, and track version regressions.
• Regulated enterprises: Deployed in a private environment, with additional permissions and collaboration features provided by the commercial version.

Typical use cases

• Run a fixed evaluation set before releasing new prompts or models to prevent significant regression.
• Compare the contextual relevance, factual accuracy, and quality of responses among various RAG retrieval approaches.
• Monitor the changes over time in the distribution of production data, the rate of missing values, and model performance.
• Check LLM responses for personal information, toxicity, sensitive words, and formatting errors.
• Generate batch quality reports for classification, regression, sorting, or recommendation systems.
• Generate boundary and adversarial test inputs to expand the coverage of traditional manual test sets.
• Aggregate the evaluation snapshots in a self-hosted dashboard for review by the development, product, and governance teams.

Product advantages

• The same framework covers generative AI, traditional machine learning, and data quality simultaneously.
• It offers over 100 built-in evaluation metrics, and allows the use of Python to create custom indicators for specific business needs.
• Report is suitable for exploration, while Test Suite is appropriate for automated access control; the two modes can be used in conjunction.
• The core libraries and underlying platform are licensed under the permissive Apache 2.0 license, allowing them to be run in local and private environments.
• The report can be retained in the Python environment, or it can be exported as structured data or HTML.
• By default, batch monitoring allows only the aggregated summaries and test results to be saved, thereby avoiding the need to save all original predictions repeatedly.
• Official repositories, documentation, examples, and community resources are quite comprehensive, which facilitates developers in troubleshooting and making enhancements.

Usage restrictions and precautions

• Evidently provides evaluation tools rather than correct answers; poorly designed metrics, thresholds, and reference data can be misleading.
• LLM reviewers may exhibit biases, randomness, and additional model costs, requiring calibration through manual annotation.
• Data drift merely indicates a change in the distribution; it cannot on its own prove that the model’s performance has declined.
• Open-source platforms do not include the features of the commercial version such as authentication, role-based permissions, alerts, scheduled tasks, and full no-code capabilities.
• The original Evidently Cloud is no longer available as a SaaS service; old blogs, old login pages, and certain remaining documents may cause confusion.
• Self-hosted teams are responsible for handling the costs related to infrastructure, security, backups, upgrades, availability, and scaling.
• Python libraries are suitable for technical personnel; for non-technical teams, setting up and interpreting metrics can be challenging in the absence of a commercial platform.
• Before processing production prompts, responses, and personal data, it is necessary to implement minimization, data masking, access control, and define retention periods.

Price and version

As of August 22, 2026, the official documentation states clearly that Evidently Cloud is no longer available as a SaaS product; therefore, it is no longer possible to rely on the previous free cloud plans or old payment tiers.

Open-source software does not incur any license fees, but costs are associated with databases, object storage, computing resources, logging, as well as maintenance. There is no fixed price for the commercial version; it is necessary to contact sales to determine the cost based on the scale of deployment and the required support and security features.

Comparison between the open-source version and the commercial version

Supports environment and input/output.

API and integration capabilities

Obviously, it is first and foremost a Python library API that can be used in Notebooks, scripts, continuous integration processes, Airflow-like orchestration tasks, as well as custom evaluation pipelines. Self-hosted platforms also provide interfaces for writing to and reading evaluation results.

• Place the Report and Test Suite in the training, validation, or deployment pipeline.
• Batch monitoring is triggered by scheduled scripts, task schedulers, or tag-based events.
• Send the structured results to existing logs, alerts, dashboards, or governance systems.
• Use Tracely to capture the inputs, outputs, and intermediate calls of LLM applications.
• Connect object storage to remote Workspaces via a compatible file system interface.
• Custom Python metrics are used to encapsulate internal corporate rules, classifiers, or external evaluation models.

GitHub and open source

Obviously, the core repository is publicly maintained and is licensed under the Apache License 2.0. As of August 2026, the repository had around 7,800 stars; its latest official version is 0.7.21, released on March 10, 2026.

Apache 2.0 permits use, modification, and distribution, but when redistributing the material it is necessary to retain the license as well as any related copyright, patent, and attribution statements, and to indicate that the file has been modified. Trademark rights are not automatically granted under an open-source license.

Privacy, Telemetry, and Security

When generating reports in a Notebook or script using only Python libraries, the official documentation states that telemetry data from open-source products is not collected. When the self-hosted Monitoring UI is launched, anonymous usage telemetry is enabled by default, but it can be disabled by using the DO_NOT_TRACK environment variable.

• UI telemetry includes information on the operating system, Python and tool versions, time, anonymous user identifiers, and function usage events.
• Officials state that they will not collect the content of datasets, field names, parameters, code, or data patterns.
• Self-hosting does not equate to automatic security; open-source platforms lack built-in authentication and role-based permissions, which the team must implement on its own.
• The original LLM tracking data may contain customer text, personal information, and business secrets; therefore, rules for data masking, access control, and deletion should be established.
• The privacy policies of the official website and the historical cloud services also cover email addresses, names, companies, Cookies, usage data, and location data obtained with permission.
• When deploying in a corporate environment, it is necessary to verify the data processing protocols, sub-processors, logging mechanisms, encryption methods, backup procedures, vulnerability response strategies, and support frameworks.

Basic information

Recommendation score

Recommendation score: 4.6 / 5. Evidently covers LLMs as well as traditional machine learning; it provides a seamless connection between metrics, reports, testing, and monitoring, making it suitable for technical teams that wish to have control over data and evaluation processes.

The main drawback is that open-source deployment requires investment in engineering and operational tasks; the code-free and collaborative features that are available for non-technical users are found only in the commercial version. After Cloud exits the SaaS market, the team will also need to reassess the hosting costs and migration options.

Frequently Asked Questions

Is AI obviously free?

The core Python libraries, Tracely, and the basic platform can be used for free, under the Apache 2.0 license. Self-hosting still incurs costs related to servers, storage, maintenance, and security.

Can Evidently Cloud still be registered and used?

The latest official documentation clearly states that Cloud is no longer available as a SaaS product. The free cloud packages mentioned on older pages or articles should no longer be considered part of the current offerings.

Is it possible to evaluate LLMs and RAG?

Yes. The platform supports evaluations based on factual accuracy, search relevance, semantic similarity, PII presence, toxicity, format, as well as custom LLM assessments; it also allows for comparing different models and prompt phrases.

Is it possible to monitor traditional machine learning models?

Yes. Evidently supports classification, regression, sorting, recommendation, data quality assessment, and detection of data drift, and it allows batch results to be saved in a self-hosted Dashboard.

Is it necessary to upload data to an external cloud?

Open-source libraries and platforms can run entirely within one’s own environment. Whether to use external LLM reviewers, remote databases, or object storage depends on the team’s own configuration.

Does the open-source monitoring interface offer login and permission management?

The official comparison table shows that the open-source version does not include authentication and role-based permissions; when it is used in external environments or with multiple users, gateways, identity services, and network policies are needed to implement proper access control.

Does Evidently collect model data?

Officials state that self-hosted UI telemetry does not collect the content of datasets, field names, parameters, or code; nor does it send any telemetry when reports are generated using Python libraries alone. UI anonymous telemetry is enabled by default, but can be disabled through configuration.

Can Evidently clearly be used as a quality gate for continuous integration?

Yes. By including the key metrics in the Test Suite and setting thresholds, it is possible to obtain pass or fail results whenever the model, prompts, or data are updated.

What has been added to the Enterprise version for business use?

The business version adds features such as code-free data generation and evaluation, code-free dashboards, side-by-side comparison, alerts, scheduled tasks, authentication, role-based permissions, an extended backend, and dedicated support. For pricing details, please contact sales.

Summary

Clearly, AI is suitable for teams that wish to define the quality of AI through code, to convert offline evaluations into regression tests, and to continuously monitor LLMs and machine learning systems in their own environments.

The current approach should focus on open-source, self-hosted solutions or Enterprise-level private deployments, rather than relying on the previous cloud-based package models. When implementing such solutions, it is necessary to take into account factors such as the validity of metrics, manual calibration, data governance, and operational security.