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.
4.4
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.
Details
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Buyer guide
Gain valuable insights from real users who purchased this product, powered by PeerSpot.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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.
arize.com+2
Helpful?
Vendor refund policy
No returns or refunds.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
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.
Updated weekly
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
Positive reviews
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
No
No
No
Customer reviews
Soumyaranjan N.
Arize’s Observability Feels Like a Camera for Agent Requests and Responses
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
I like Arize because of its observability quality. It notices patterns in the faults made by the agent and reports them to me, so I can identify what problems are happening in my project. It’s like a camera that captures all the request and response activity in my project between the user and the agent. If any problem is found in a response, it reports it to me.It also called quality and performance inspector.
What do you dislike about the product?
There are some drawbacks with this model. When it records all logs and generates reports, it sometimes includes unnecessary data or low-impact issues. Because of that, I end up wasting time reviewing things that don’t really matter.I also faced another problem when I transitioned my project from phoneix cloud to this: some integrations failed. If the network connection is slow, it becomes very slow as well, and it doesn’t handle heavy load. On top of that, it charges a high price for some upgrades.
What problems is the product solving and how is that benefiting you?
Suppose my project is deployed on a server and users are using my product. This tool works there like a watchman. It captures every request and response from the user, and the AI model also checks whether the response is correct or not, and whether it is costing more than others or not. It helps me optimize my code during development. After it reads my code, it tells me which parts could create problems later, so it saves me a lot of time while developing. After deployment, I don’t need to dig through log files; it gives me a report showing what caused the issue, so I can go directly and change that part.
Anson D.
Great Visibility Into AI App Performance With Helpful Dashboards and Monitoring
Reviewed on Aug 30, 2026
Review provided by G2
What do you like best about the product?
I like the visibility it provides into AI application performance. The dashboards make it easier to look at traces and understand where an issue may be happening. The evaluation and monitoring features are also useful for getting a better idea of how the application is performing over time.
What do you dislike about the product?
There are quite a few features to explore, so it takes some time to understand the platform properly. Some of the more advanced options may also require a bit of learning before they become useful.
What problems is the product solving and how is that benefiting you?
It provides a centralized place to monitor and evaluate AI applications. Instead of relying only on application logs, it gives more visibility into traces and performance, which can make troubleshooting and quality checks easier.
Sugam S.
A Must-Have for AI/ML Monitoring & Troubleshooting
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
I really like the end-to-end visibility Arize AX provides into AI applications. The tracing feature is incredibly useful because it allows me to drill into an individual request and see the entire workflow, which is much better than guessing from application logs. I also appreciate the evaluation and monitoring capabilities, particularly when we're testing changes to prompts or models. It's handy for quickly identifying when a new prompt starts producing more irrelevant responses by comparing results. The interface is fairly easy to navigate once I got the hang of how the data is organized, which adds to the overall ease of use.
What do you dislike about the product?
The biggest improvement area for me is the initial setup and learning curve. Getting instrumentation, traces, and evaluations configured properly can take some time, especially with an existing application. Also, when there's a lot of trace data, finding the exact issue can sometimes require multiple filters and views. A simpler troubleshooting workflow would make it more efficient. A few things would help: smarter filtering and search, saved troubleshooting views, and better grouping of similar traces. For example, being able to quickly filter by model version, error type, latency, or a specific prompt would save time. An AI-assisted summary of recurring trace issues would also be useful when we are dealing with a large volume of data.
What problems is the product solving and how is that benefiting you?
I use Arize AX for monitoring AI/ML applications in production. It provides visibility into model performance, helps identify issues quickly, and makes troubleshooting much easier by tracing requests and analyzing outputs.
Consumer Goods
End-to-End Tracing & Evaluation Platform with Reproducible, Scriptable Experiments
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
- One platform that covers the full loop: tracing, datasets, evaluation, and experiments
- Evaluation is a first-class product, not an afterthought
- Broad framework instrumentation that follows OpenInference standards
- Strong CLI that makes evaluation and experimentation reproducible and scriptable
- Tight integration between live trace data, human annotation, and LLM-as-judge scoring
- Evaluation is a first-class product, not an afterthought
- Broad framework instrumentation that follows OpenInference standards
- Strong CLI that makes evaluation and experimentation reproducible and scriptable
- Tight integration between live trace data, human annotation, and LLM-as-judge scoring
What do you dislike about the product?
- Steep setup: column mappings must match real span paths, which is easy to get wrong
- Index lag (1-2 hours for evals, 6-12 hours for time-series) makes recent data slow to appear
- Custom code evaluators are brittle and fail silently (hard to debug)
- Documentation can be inconsistent between versions
- Requires careful planning before it feels smooth
- Index lag (1-2 hours for evals, 6-12 hours for time-series) makes recent data slow to appear
- Custom code evaluators are brittle and fail silently (hard to debug)
- Documentation can be inconsistent between versions
- Requires careful planning before it feels smooth
What problems is the product solving and how is that benefiting you?
Problems solved: I didn’t have clear visibility into LLM app behavior across prompts, tools, and model calls, and it was hard to tell when model quality regressed in production. Evaluation and experimentation were spread across disconnected tools, with no single source of truth for traces, datasets, and eval scores.
Benefits: This gives me one platform for tracing, evaluation, and experiments, which makes prompt optimization more data-driven instead of guesswork. I also like having reproducible evaluation workflows via the CLI, and it speeds up root-cause analysis when digging into trace data.
Benefits: This gives me one platform for tracing, evaluation, and experiments, which makes prompt optimization more data-driven instead of guesswork. I also like having reproducible evaluation workflows via the CLI, and it speeds up root-cause analysis when digging into trace data.
Yaoxb R.
Clear AI Agent Step Visibility Makes Troubleshooting Complex Workflows Easy
Reviewed on Aug 27, 2026
Review provided by G2
What do you like best about the product?
My favorite part is being able to see how an ai agent moves through different steps which makes complex workflows much easier to troubleshoot
What do you dislike about the product?
some workflows require a good understanding of instrumentation and evaluation concepts so getting everything configured correctly can take time
What problems is the product solving and how is that benefiting you?
arize ax helps connect development and production monitoring so i can test changes measure performance and make improvements based on real application data