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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Software as a Service (SaaS)
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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
Customer service
Cost effectiveness
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Mixed reviews
Negative reviews
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
Atharva S.
Arize AX Makes AI Observability and LLM Tracing Easy
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
What I like best about Arize AX is its comprehensive AI observability and evaluation capabilities that make monitoring machine learning and generative AI applications much easier. The platform provides detailed insights into model performance, data quality, drift detection, and inference behavior through intuitive dashboards, helping identify issues before they impact users. I also appreciate its strong tracing features for LLM applications, flexible evaluation tools, and seamless integrations with popular ML frameworks. Overall, Arize AX improves model reliability, accelerates debugging, and gives teams greater confidence when deploying and maintaining AI systems in production.
What do you dislike about the product?
One area where Arize AX could improve is offering more advanced customization for dashboards, alerting, and evaluation workflows to better support organizations with complex AI deployments. While the platform provides excellent observability and tracing capabilities, configuring monitoring for large-scale or highly customized models can involve a learning curve. I'd also like to see broader integrations with additional MLOps tools, richer historical analytics, and more flexible reporting options for enterprise teams. Overall, the experience has been very positive, but greater customization, expanded integrations, and enhanced reporting would make Arize AX even more valuable for monitoring and optimizing AI systems in production.
What problems is the product solving and how is that benefiting you?
Arize AX solves the challenge of monitoring, evaluating, and improving machine learning and generative AI applications after deployment by providing centralized observability into model performance, data quality, inference behavior, and LLM traces. Instead of relying on manual debugging and fragmented monitoring tools, it helps teams detect model drift, identify performance regressions, analyze user interactions, and evaluate AI outputs with actionable insights. This reduces troubleshooting time, improves model reliability, accelerates issue resolution, and enables more confident deployment of AI systems. As a result, it has streamlined AI monitoring, increased operational efficiency, and helped maintain consistent performance across production machine learning and LLM applications.
Taiba B.
Real-Time Monitoring and Explainability That Catch Issues Early
Reviewed on Aug 07, 2026
Review provided by G2
What do you like best about the product?
Real time Model monitoring and explainability features make it easy to understand performance and catch issues early
What do you dislike about the product?
Some advanced features have learning curve, especially when setting up custom monitoring or debugging workflow
What problems is the product solving and how is that benefiting you?
It helps us monitor model performance, detect drift, and identify issues in real time
pankaj y.
Comprehensive LLM Monitoring with Stellar Capabilities
Reviewed on Aug 06, 2026
Review provided by G2
What do you like best about the product?
I use Arize AI to monitor and improve the performance of our AI and LLM application, and it solves several challenges by giving us visibility into how our models and LLMs are performing. What I like most about Arize AI is its comprehensive observability and debugging capability for LLM applications. Arize AI consolidates various capabilities into a single platform, offering end-to-end tracing, built-in LLM valuations, prompt and response analysis, experiment tracking, and production monitoring. The initial setup was smooth, and I also appreciate that it integrates well with our LLM application stack, including OpenAI APIs, LongChain/LongGraph for orchestration, and cloud platforms like AWS.
What do you dislike about the product?
One area Arize AI could improve is the onboarding experience, especially for teams that are new to LLM observability.
What problems is the product solving and how is that benefiting you?
I use Arize AI to monitor and improve AI and LLM performance, providing visibility into model operations. It combines capabilities like end-to-end tracing, built-in evaluations, and experiment tracking in one platform, enhancing observability and debugging, especially for LLM applications.
Aditi P.
Sleek, Near-Zero Latency Observability with Deep Integrations and Top-Tier Support
Reviewed on Aug 06, 2026
Review provided by G2
What do you like best about the product?
Arize AI stands out for its sleek, developer-friendly UI and near-zero latency performance, making enterprise observability seamless at scale. With deep integrations across the stack, sharp drift/bias intelligence, and transparent ROI, it backs its platform with top-tier onboarding and support to give teams complete confidence in production AI.
What do you dislike about the product?
While powerful, Arize struggles with opaque enterprise pricing structures and setup friction for teams without dedicated MLOps support. Custom API integrations and real-time dashboard performance can feel sluggish under heavy production volumes, while its complex AI evaluation suites demand a high baseline of machine learning knowledge to yield actionable insights.
What problems is the product solving and how is that benefiting you?
Arize AI solves production "black box" failures—such as hidden agent regressions, data drift, and unmonitored LLM token costs—by providing continuous tracing and automated evaluation suites. Through intuitive UI dashboards, seamless data stack integrations, and fast query performance, it benefits teams by drastically reducing time-to-root-cause, accelerating deployment velocity, and maximizing AI ROI with dedicated onboarding support.
Taibaa B.
Model Monitoring and Observability That Make Troubleshooting Fast
Reviewed on Aug 06, 2026
Review provided by G2
What do you like best about the product?
I like its model monitoring and observability features. They make it easy to detect performance issues and troubleshoot models
quickly
quickly
What do you dislike about the product?
Some advanced features and dashboards could be more intuitive
What problems is the product solving and how is that benefiting you?
It help detect model performance issues, data drift and anomalies easily. This save troubleshooting time and help keep models reliable in production