Arize AI
FreemiumAgent observability, evaluation, and improvement platform for AI engineering teams.
Arize AI is an AI engineering platform that helps teams observe, evaluate, and improve AI agents and LLM applications in production. It combines distributed tracing, automated evaluations, prompt management, and datasets into a single workflow so engineers can debug failures, compare experiments, and ship better models faster. The platform is built on open standards like OpenTelemetry and OpenInference, and includes Phoenix, an open-source observability and evaluation tool for development-time use.
What is it
Arize AI is an agent observability and evaluation platform that traces LLM calls, tool use, and agent workflows to help teams debug and improve AI applications.
What it can do
Collect traces at scale, run span and session evaluations, build datasets and experiments, manage prompt versions, detect hallucinations and PII leaks, and integrate with 40+ models and frameworks.
Who is it for
AI engineers, product managers, and platform teams building production LLM applications, RAG systems, copilots, and agentic workflows.
Key Features
Agent Tracing — End-to-end visibility
Capture every model call, retrieval step, tool invocation, and agent decision in a trace so teams can see exactly what happened inside a request.
Online and Offline Evaluations — LLM-as-a-Judge and custom evals
Run pre-built or custom evaluators on spans, traces, and sessions to measure hallucination, toxicity, relevance, and other quality metrics.
Prompt Playground and Management — Version and test prompts
Compare prompt variants side by side, version prompts centrally, and replay production calls against new prompt versions.
Datasets and Experiments — Systematic iteration
Group traces into datasets, rerun them through different application versions, and compare evaluation scores to validate changes before shipping.
Use Cases
Debug Failing Agent Workflows
Engineers trace multi-step agent executions to find where tool calls fail, retrievals return bad chunks, or models generate incorrect outputs.
Evaluate Prompt and Model Changes
Teams run offline experiments on curated datasets to compare prompt versions or model swaps before deploying to production.
Monitor Production LLM Applications
Operations teams watch for regressions in latency, cost, hallucination rate, and user satisfaction across live traffic.
Detect and Block Unsafe Outputs
Safety teams use guardrails to identify PII leaks, jailbreak attempts, toxic responses, and other risky content in real time.
Pricing plans
Frequently Asked Questions
Discussion
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