Arize AX Features

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Allows users to input models built in a variety of languages.

Allows users to choose the framework or workbench of their preference.

Records versioning as models are iterated upon.

Provides a way to quickly and efficiently deploy machine learning models.

Offers a way to scale the use of machine learning models across an enterprise.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Records and organizes all machine learning models that have been deployed across the business.

Tracks the performance and accuracy of machine learning models.

Control model usage and performance in production

Deploy mission-critical ML applications where and when you need them

Easily compare experiments—code, hyperparameters, metrics, predictions, dependencies, system metrics, and more—to understand differences in model performance.

Ensures that the service is reliable and available when needed, minimizing downtime and service interruptions.

Offers the user the flexibility to deploy across multiple cloud providers, reducing the risk of vendor lock-in.

Provides the user the ability to seamlessly connect with various data sources and pipelines, simplifying data ingestion and pre-processing.

Allows the user to easily integrate the generative AI models into existing workflows and systems via APIs.

Helps the user maintain compliance with GDPR and other data protection regulations, which is crucial for businesses operating globally.

Allows the user to set up access controls based on roles within the organization, enhancing security.

Ensures that data is encrypted during transit and at rest, providing an additional layer of security.

Provides the user with comprehensive and clear documentation, aiding in quicker adoption and troubleshooting.

Allows the user to gauge the level of community support and third-party extensions available, which can be useful for problem-solving and extending functionality.

Provides users with the ability to test and optimize prompts to improve LLM output quality and efficiency.

Gives users a collection of reusable prompt templates for various LLM tasks to accelerate development and standardize output.

Offers tools for users to compare multiple LLMs side-by-side based on performance, speed, and accuracy metrics.

Gives users tools to integrate LLM functionality into their existing applications through SDKs and APIs, simplifying development.

Gives users notifications when the LLM performance deviates significantly from expected norms, indicating potential model drift or data issues.

Provides users with live insights into model accuracy, latency, and user interaction, helping them identify and address issues promptly.

Provides users with encryption capabilities for data in transit and at rest, ensuring secure communication and storage when working with LLMs.

Offers users tools to set access permissions for different roles, ensuring only authorized personnel can interact with or modify LLM resources.

Tracks changes to prompts over time.

Helps users run test outputs across different models.

Tracks execution time of prompts across models.

Helps in monitoring token consumption and API call frequency.

Identifies expensive prompts to help teams optimize for cost efficiency.

Helps in A/B testing of prompts across models.

Helps to plug into CI/CD for automated testing, validation, and deployment of prompts.

Helps generating reliable and consistent AI outputs.

Helps identifying how different LLMs respond to same prompts

Enables step-by-step inspection of agent reasoning, tool calls, and intermediate outputs to identify failure points.

Displays agent execution paths, decision trees, and tool call sequences in a visual, navigable format.

Traces multi-step AI agent workflows including LLM calls, tool invocations, retrieval steps, and intermediate reasoning states.

Detects behavioral regressions by comparing agent performance across model versions, prompt changes, or configuration updates.

Identifies instances where the agent generates factually incorrect or unsupported content.

Assesses agent output quality using methods such as LLM-as-judge, rule-based checks, or custom evaluators.

Notifies teams of quality degradations, error spikes, or anomalous agent behavior in production.

Tracks response times across agent steps, LLM calls, and tool invocations to identify performance bottlenecks.

Monitors token consumption and attributes costs across models, agents, and workflows.

Maintains comprehensive logs of agent actions, decisions, and data access for compliance and governance purposes.

Monitors agent behavior against defined organizational policies, guardrails, and regulatory requirements.