Arize

What is Arize AI

Arize AI is a unified platform for LLM observability and agent evaluation that connects development with production to help teams ship reliable AI applications faster. It offers tools for Generative AI, traditional ML, and Computer Vision—spanning tracing, evaluations, drift and data quality monitoring, and root-cause analysis. With Arize AX, teams iterate using real production data, align production observability with trusted evaluations, and close the loop from prompts and agents to outcomes. An open-source stack for LLM tracing & evals complements the managed platform.

Arize AI Main Features

• LLM Observability: Centralize traces, prompts, responses, tokens, and latencies to diagnose issues across chat, RAG, and agent workflows.
• Agent Evaluation: Define task-specific evals to score tool use, planning, correctness, and safety; compare agents across versions.
• Open-Source Tracing & Evals: Instrument apps with an open-source library to capture spans and run offline/online evaluations.
• Production Monitoring: Track quality, response rates, failures, and user feedback with alerts for regressions in real time.
• Drift & Data Quality: Detect schema issues, missing values, feature drift, and embedding drift for ML and CV pipelines.
• Embedding Analytics: Explore vector spaces, cluster behaviors, and identify problematic cohorts driving errors or hallucinations.
• Cohort & RCA: Slice by user, geography, prompt pattern, or model version to pinpoint root causes of degradation.
• Evaluation Workflows: Combine automatic metrics with human review, rubric-based scoring, and golden datasets.
• Versioning & Comparison: A/B compare prompts, models, guards, and agents; quantify trade-offs before rollout.
• Integrations: Connect to common LLM providers, vector databases, data warehouses, and CI/CD for continuous delivery.

Arize AI Is For

Arize AI suits AI/ML engineers, LLM and agent developers, data scientists, MLOps teams, product managers, and risk or QA functions who need reliable monitoring and evaluation. It fits use cases like chatbots, RAG search, autonomous agents, ranking and recommendation systems, fraud and risk models, and computer vision for manufacturing or retail—where continuous evaluation, drift detection, and fast debugging matter.

How to Use Arize AI

1. Instrument your app with the Arize SDK or open-source tracing library to capture prompts, spans, and metadata.
2. Define key outcomes and evaluation criteria (e.g., correctness, relevance, safety, latency, cost).
3. Log production data, predictions, embeddings, and user feedback to the platform.
4. Create dashboards for LLM quality, agent success rates, and ML/CV performance by cohort.
5. Set alerts for anomalies, drift, or degraded metrics and route them to your incident channels.
6. Run evaluations—automatic and human-in-the-loop—on production samples and curated test sets.
7. Compare versions of prompts, models, tools, and guards; validate improvements before rollout.
8. Close the loop by exporting insights to training datasets, prompt libraries, and CI/CD pipelines.

Arize AI Industry Examples

In e-commerce, teams monitor a RAG assistant’s grounding and relevance, detect embedding drift from a catalog update, and fix low-quality chunks. A fintech support bot is evaluated for accuracy and safety across intents, with alerts for hallucinations and compliance risks. In manufacturing, a vision model’s defect detection is tracked for drift caused by lighting changes, and the worst-performing cohorts are isolated for retraining. A marketplace compares agent strategies in A/B tests to raise task completion while controlling latency and cost.

Arize AI Pricing

Arize provides an open-source option for LLM tracing and evaluations at no cost. The managed platform—including Arize AX—offers enterprise capabilities such as scalable observability, governance features, and integrations. Pricing and trials are typically arranged through sales based on deployment model, data volume, and team size.

Arize AI Pros and Cons

Arize AI Popular Questions

• What models and frameworks does Arize AI support?

  Arize integrates with common LLM providers, vector databases, and ML/CV pipelines via SDKs and APIs, enabling flexible ingestion of traces, predictions, and embeddings.

• Can it evaluate autonomous agents, not just single prompts?

  Yes. You can trace multi-step tool use, define agent-specific success metrics, and compare strategies and versions with task-level evaluations.

• How does Arize handle data privacy?

  Teams control what metadata is logged and can redact sensitive fields. Deployment and retention settings should align with internal compliance policies.

• Is there an open-source option?

  An open-source library provides LLM tracing and evaluation functionality that can run locally or in your stack, complementing the managed platform.

• How do evaluations work in production?

  You can run automatic metrics and scheduled human reviews on live samples, set alerts for threshold breaches, and feed validated examples back to training or prompt libraries.