Sold by: Arize AI
Deployed on AWS
Arize is the all-in-one AI Agent Engineering platform to develop, observe, evaluate, and continuously improve AI agents and applications at scale. With enterprise-grade features like the Alyx AI assistant, online evaluations, automated prompt optimization, role-based access control (RBAC), and robust support, Arize AX empowers both technical and non-technical teams to build and manage self-improving agents from development through production.
4.3
Overview
Arize AX is the all-in-one AI Agent Engineering platform that powers the next generation of self-improving agents and applications - from development to live production. With tools for prompt optimization, full trace observability, agent evaluation, and live monitoring, Arize helps AI teams build generative AI systems faster, improve performance, and scale with confidence.
Built for modern agent architectures and deployed in your AWS environment, Arize AX integrates seamlessly with Amazon Bedrock Agents and popular open-source frameworks.
-Prompt IDE for Optimization: Design, test, compare, and evolve prompts in a powerful environment with live inputs, outputs, and integrated evaluation results.
-Application Agent-Level Observability and Tracing: Visualize every step of agent behavior - prompts, tools, memory, routing, and LLM outputs - with minimal code using the Arize OpenInference instrumentation.
-LLM and Agent Evaluation: Run offline and online LLM-as-a-Judge evaluations to assess accuracy, tool-calling, planning, and goal achievement.
-Self-Improving Agent Workflows: Drive closed-loop improvement by combining trace analysis, evaluation feedback, and golden data sets into continuous iteration.
-Datasets and Experiments: Use curated and/or human-annotated datasets to run controlled experiments across prompt strategies, agent configurations, or toolchains, and measure performance impact over time with built-in analytics
-Copilot Assistant (Alyx): Navigate traces, surface anomalies, and ask natural-language questions about agent performance - all in-product.
-Real-Time Monitoring & Alerts: Define custom metrics, monitor latency, token usage, or failures, and set alerts to stay ahead of production issues.
-Machine Learning Observability and Computer Vision: Monitor, troubleshoot, and improve traditional ML and CV models alongside LLM agents - tracking drift, bias, and performance across tabular, image, and multimodal datasets.
Highlights
- Agent and LLM Application Observability: Gain full visibility into the behavior of your AI agents and LLM-powered applications. Arize captures and visualizes every step - user inputs, routing logic, tool calls, memory access, and model outputs - using tree-structured traces. With native support for Amazon Bedrock Agents and open frameworks, observability is seamless and code-light.
- Enable Self-Improving Agents: Go beyond static deployments. Arize enables closed-loop agent improvement by combining observability, online evaluation, and structured experimentation. Debug issues faster, test changes safely, and continuously evolve agent behavior in response to real-world usage and feedback.
- Prompt IDE and Evaluation: Optimize prompts with Prompt IDE, purpose-built for fast iteration and testing. Compare prompt versions side by side, analyze agent responses, and apply online or offline LLM as a Judge evaluations to measure quality, correctness, and performance at scale.
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Pricing
Pricing is based on the duration and terms of your contract with the vendor. This entitles you to a specified quantity of use for the contract duration. If you choose not to renew or replace your contract before it ends, access to these entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/12 months |
|---|---|---|
Arize Pro Edition | Tracing, Prompt IDE, evaluations, Alyx co-pilot. Subscription based. | $1,200.00 |
Dimensions summary
This listing offers one option: Arize Pro Edition, billed as a subscription and measured in units. You commit under a contract, and pricing scales with the number of units you buy. The subscription covers tracing, the Prompt IDE, evaluations, and the Alyx co-pilot. Tracing captures each step of a request so you can see where it broke. Evaluations score how your AI application performs. Alyx is a built-in AI agent that runs evaluations and helps debug issues. Because there is a single dimension, you scale cost by adjusting unit quantity rather than switching plans.
Top-of-mind questions for buyers
A unit is a subscription quantity you commit to under contract. Your total cost scales with the number of units you buy. The listing does not tie a unit to a single fixed metric, so confirm the exact unit mapping with the vendor before purchase.
The subscription includes tracing, the Prompt IDE, evaluations, and the Alyx co-pilot. Tracing captures each step of a request, including retrieval, tool calls, and model outputs. Evaluations score how your AI application performs. Alyx is a built-in agent that runs evaluations and helps debug agent issues.
You scale cost by adjusting the number of units, not by switching plans. Because this is a single subscription dimension, adding capacity means adding units under your contract. Confirm mid-term quantity changes and any minimum commitment with the vendor.
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Support
Vendor support
Email: marketplace@arize.com
Enterprise Support: Includes onboarding, instrumentation guidance, custom evaluation setup, and prompt optimization strategies.
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
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By Arize AI
By Fiddler AI
Top
25
In Observability, Software Development
Top
50
In Computer Vision
Top
100
In Data Governance
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
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Mixed reviews
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AI generated from product descriptions
Agent and Application Observability
Full visibility into AI agent behavior through tree-structured traces capturing user inputs, routing logic, tool calls, memory access, and model outputs with native support for Amazon Bedrock Agents and open-source frameworks
Prompt Optimization and Testing
Prompt IDE environment enabling design, testing, and comparison of prompt versions with live inputs, outputs, and integrated evaluation results for iterative improvement
LLM and Agent Evaluation
Offline and online LLM-as-a-Judge evaluations assessing accuracy, tool-calling, planning, and goal achievement across agent workflows
Closed-Loop Improvement Workflows
Self-improving agent capabilities combining trace analysis, evaluation feedback, and golden datasets for continuous iteration and performance enhancement
Real-Time Monitoring and Alerting
Custom metrics definition and monitoring of latency, token usage, and failures with alert configuration for production issue detection and prevention
Multi-Model Type Support
Supports monitoring and observability for tabular, deep learning, computer vision, natural language processing, and large language model deployments
Performance and Drift Detection
Identifies and mitigates model performance degradation, data drift, data integrity issues, hallucination, accuracy, safety, and security issues in production deployments
Root Cause Analysis and Diagnostics
Provides powerful root cause analysis and diagnostic capabilities with 3D UMAP visualization for macro-level trend analysis and micro-level issue identification
Enterprise Security and Access Control
Implements SOC2 Type 2 security compliance and role-based access control (RBAC) for level-specific user permissions across protected environments
Customizable Analytics and Metrics
Offers customizable dashboards, reports, and custom metrics to track model performance aligned with business KPIs and enable data-driven decision-making
Data Quality Monitoring
Automated monitoring and alerting across data vitals with out-of-the-box anomaly detection and configurations for identifying data quality issues.
Multi-Data Type Support
Capability to monitor tabular, image, and text data types across machine learning applications and data pipelines.
Privacy-Preserving Architecture
Platform operates on processed data summaries rather than raw data, enabling privacy preservation and no-configuration deployment at scale.
Comprehensive ML Observability
Unified monitoring of model inputs, outputs, performance metrics, data drift, concept drift, and upstream data quality issues in a single platform.
Broad Integration Ecosystem
Integration with popular ML and data tools including Pandas, Apache Spark, AWS SageMaker, MLflow, Flask, Ray, RAPIDS, and Apache Kafka.
Standard contract
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Customer reviews
Ravindra N.
Comprehensive AI Observability That Boosts Model Reliability
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
What I like most about Arize AI is its comprehensive monitoring and observability for machine learning and LLM applications. It provides deep insights into model performance, data quality, and production behavior, making it much easier to identify issues before they impact users. End-to-end monitoring for both traditional ML models and LLM applications. Detailed dashboards for model performance, drift detection, and data quality. Strong observability with traces, predictions, and inference analysis. Built-in evaluation tools that help measure model quality over time. Easy integration with modern ML and AI workflows. For me, the most valuable feature is the combination of model monitoring and root cause analysis. It helps quickly identify whether an issue is caused by data drift, model behavior, or changes in the application. The biggest benefit is improved reliability of AI systems. Arize AI makes it easier to detect production issues early, optimize model performance, and maintain confidence in AI applications as they evolve.
What do you dislike about the product?
The biggest drawback is the complexity of configuring comprehensive monitoring. While the platform provides excellent insights, it takes some effort to set up meaningful metrics and alerts for production workloads. There is a learning curve to fully understand the monitoring dashboards, drift metrics, and evaluation features. Large-scale deployments can generate a significant amount of telemetry, requiring careful configuration to avoid information overload.
What problems is the product solving and how is that benefiting you?
Arize AI solves the challenge of monitoring, debugging, and improving AI models after deployment. Instead of relying on manual checks or limited metrics, it provides comprehensive observability into model performance, data quality, drift, and prediction behavior. Monitors AI and machine learning models in production. Detects data drift and model performance degradation early. Provides root cause analysis to investigate prediction issues. Tracks key metrics, latency, and inference quality through intuitive dashboards. Helps evaluate and improve both traditional ML models and LLM applications. In my workflow, Arize AI helps me identify performance issues before they affect users, analyze the causes of model failures, and validate improvements with confidence. Having centralized monitoring and detailed insights makes troubleshooting much faster than relying on logs alone. The biggest benefit is more reliable AI applications with faster issue detection. Arize AI reduces debugging time, improves model performance, and enables continuous optimization through real-time monitoring and actionable insights.
Jeni J.
Powerful Platform for Monitoring and Improving LLM Applications
Reviewed on Aug 04, 2026
Review provided by G2
What do you like best about the product?
I really appreciate how easy it is to move from identifying an issue to understanding its root cause with Arize AI. The tracing, evaluation, and prompt experimentation features work seamlessly together, and the visual interface makes complex LLM workflows much easier to inspect without being overwhelming. I also value that Arize AI integrates well with popular AI frameworks, fitting naturally into existing development workflows. The tracing feature is great for following each step of an LLM request, helping me pinpoint where errors originate. The built-in evaluation tools help me compare prompts and measure response quality consistently, which, along with prompt experimentation, allows me to test changes without disrupting production. Having all these capabilities in one platform saves a lot of time and boosts my confidence in improving AI applications. The initial setup was very easy.
What do you dislike about the product?
One area I'd like to see improved is the learning curve for some of the more advanced features. While the platform is very powerful, configuring evaluations and navigating complex traces can take some time for new users, and more guided onboarding or built-in templates would make it easier to get the most out of the platform. The biggest challenge for me was understanding how to structure evaluation datasets and choose the right evaluators for different LLM use cases, especially when I was first getting started. The tracing interface is comprehensive, but when dealing with applications that have multiple agents, tools, and long execution chains, it can take some time to understand how everything is connected. I think interactive onboarding tutorials, more ready-to-use evaluation templates, and contextual guidance within the UI would help new users become productive much faster.
What problems is the product solving and how is that benefiting you?
I use Arize AI to monitor and debug machine learning applications in production. It lets me trace model behavior, evaluate AI responses, and identify issues like drift or hallucinations, improving model reliability.
Sumarie N.
Alyx Makes Debugging and Knowledge-Sharing Effortless
Reviewed on Jul 31, 2026
Review provided by G2
What do you like best about the product?
Alyx, an AI assistant built into the platform to help with debugging and knowledge-sharing.
What do you dislike about the product?
The feature set has a learning curve for new users, and enterprise pricing requires a direct sales conversation
What problems is the product solving and how is that benefiting you?
Inline, expandable trace views for unpacking tool-calling workflows and tracking long-running agent loops mid-execution
Chanchal B.
Powerful AI observability platform
Reviewed on Jul 30, 2026
Review provided by G2
What do you like best about the product?
Its AI observsbility easy to use dashboard and detailed insights into model performance are what I like most.
What do you dislike about the product?
The learning curve is a bit steep,and some features could be easier to navigate better on-boarding and cleater documentation woupd improve the experience.
What problems is the product solving and how is that benefiting you?
Arize AI solves the challenges of monitoring and debugging AI models after deployment. This helps teams maintain model accuracy, identify issues quickly and improve the reliability of ai applications.
Harshul S.
Arize AI Makes Model Monitoring and Drift Detection Easy
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
What I like best about Arize AI is how easy it makes model monitoring and troubleshooting. The platform gives clear visibility into drift, performance drops, and data issues, so I don’t have to dig through logs or build custom dashboards. It saves time and helps catch problems early.
What do you dislike about the product?
The main drawback is that some parts of the platform feel complex when you’re trying to dig into deeper model behavior. Certain dashboards take time to understand, and the learning curve can slow down troubleshooting. Integrations also require more setup than expected.
What problems is the product solving and how is that benefiting you?
Arize AI helps solve the problem of monitoring ML models in production without building everything manually. It highlights drift, performance drops, and data quality issues early, so I don’t have to dig through logs or create custom dashboards. The benefit is faster troubleshooting and more confidence that the model is behaving as expected.