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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Financing for AWS Marketplace purchases
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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)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
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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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
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.
Muhammed A.
Proactive Model Monitoring and Drift Detection Made Easy with Arize AI
Reviewed on Jul 29, 2026
Review provided by G2
What do you like best about the product?
Arize AI has made it much easier to monitor model performance in production and catch issues like data or concept drift before they significantly impact output quality. Having real-time visibility into how predictions or generated outputs shift over time, instead of only discovering problems after the fact, has made our monitoring process far more proactive. The drift detection tooling flags meaningful changes clearly, which has sped up root-cause investigation when model behavior starts to degrade, and dashboards make it straightforward to track key metrics without needing to build custom monitoring infrastructure from scratch.
What do you dislike about the product?
Setting up monitoring for more complex or custom model outputs took more configuration than expected, since some of the default drift metrics don't map cleanly onto every use case. Pricing scales with usage volume, which can add up quickly once you're monitoring multiple models continuously in production. Alert thresholds sometimes needed manual tuning to reduce noise, since default sensitivity occasionally flagged minor fluctuations that weren't actually meaningful drift.
What problems is the product solving and how is that benefiting you?
Arize AI has solved the problem of models silently degrading in production without anyone noticing until users report issues. Catching drift and performance decay early has let us intervene proactively, retraining or adjusting models before quality problems affect real users, rather than reacting after the fact.
Koketso R.
Easy, Reliable Model Monitoring and Troubleshooting with Arize AI
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. It helps us catch drift, data quality issues, and performance problems early, so we can keep our ML models reliable in production.
What do you dislike about the product?
There isn’t much I dislike. The platform can be a bit overwhelming at first with so many features and metrics. The onboarding and documentation could be a little clearer for new users to get started faster.
What problems is the product solving and how is that benefiting you?
Arize AI is helping us solve model performance issues in production. It monitors for data drift, accuracy drops, and data quality problems so we can catch issues early. This benefits us by reducing downtime and keeping our AI models reliable for customers.