ML observability platform specialized for LLM applications, providing evaluation, monitoring, and debugging tools for AI agents in production.
An open-source tool for understanding and debugging your AI — visualize what's happening inside your AI pipeline.
Phoenix by Arize is an open-source observability platform specifically designed for LLM applications and AI agents. Unlike general-purpose monitoring tools, Phoenix provides specialized instrumentation and evaluation frameworks for the unique challenges of production AI systems including prompt drift, hallucination detection, and performance degradation.
The platform offers both real-time monitoring and offline evaluation capabilities. Phoenix automatically captures traces from popular frameworks like LangChain, LlamaIndex, and OpenAI, providing detailed visibility into agent execution flows, token usage, latency, and failure patterns. The tracing system supports complex multi-agent workflows and provides dependency mapping across agent interactions.
Phoenix's evaluation engine includes pre-built evaluators for hallucination detection, relevance scoring, toxicity assessment, and custom business metrics. The platform supports both automated evaluation during development and continuous evaluation in production, with alerts for performance degradation or safety violations.
For debugging and optimization, Phoenix provides detailed execution traces, comparative analysis across model versions, and A/B testing capabilities. The platform integrates with experiment tracking tools and supports both cloud-hosted and self-hosted deployment options for data privacy requirements.
Phoenix excels in scenarios where AI applications require production-grade reliability, safety monitoring, and performance optimization. Enterprise teams use it to ensure AI agent safety, optimize costs, and maintain quality standards across large-scale AI deployments.
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ML observability platform specialized for LLM applications, providing evaluation, monitoring, and debugging tools for AI agents in production.
Automatic trace collection from 20+ frameworks including LangChain, LlamaIndex, OpenAI, Anthropic, with detailed execution flows and token-level analysis.
Use Case:
Tracing complex multi-agent workflows to identify bottlenecks, debug failures, and optimize prompt chains across different agent roles and interactions.
Built-in evaluators for hallucination, relevance, toxicity, and custom metrics with continuous monitoring and automated alerting on quality degradation.
Use Case:
Monitoring customer service agents for hallucinations and inappropriate responses, with automatic alerts when quality scores drop below thresholds.
Vector drift detection, clustering analysis, and retrieval performance monitoring for RAG systems with visual drift detection and performance analytics.
Use Case:
Detecting when document embeddings drift over time, causing retrieval quality degradation in knowledge-based agents, and triggering re-indexing workflows.
Token usage tracking, cost attribution by agent/workflow, latency analysis, and optimization recommendations across multiple LLM providers.
Use Case:
Analyzing which agents consume the most tokens, identifying cost optimization opportunities, and balancing performance vs cost across different model choices.
Side-by-side comparison of prompts, models, and agent configurations with statistical significance testing and automated winner selection.
Use Case:
Testing different prompt variations for sales agents to optimize conversion rates while maintaining quality standards and measuring statistical significance.
Real-time detection of prompt injection attempts, data leakage, bias indicators, and policy violations with customizable safety guardrails.
Use Case:
Monitoring customer-facing agents for attempts to manipulate behavior, extract training data, or bypass safety constraints, with immediate blocking and alerting.
Free
Based on span counts and data volume
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View Pricing Options →Production LLM applications requiring hallucination detection and monitoring
Teams needing systematic evaluation of LLM outputs with multiple scoring methods
Organizations wanting OpenTelemetry-based observability for AI systems
Development teams iterating rapidly on prompts and model configurations
Phoenix by Arize works with these platforms and services:
We believe in transparent reviews. Here's what Phoenix by Arize doesn't handle well:
Phoenix provides LLM-specific metrics like hallucination detection, prompt drift, and semantic similarity that general monitoring tools don't support. It understands AI-specific concepts like tokens, embeddings, and retrieval quality, while general tools focus on infrastructure metrics.
Yes. While Phoenix provides automatic instrumentation for popular frameworks, it also supports custom instrumentation via Python SDK and REST API for monitoring any LLM application or custom agent implementation.
Phoenix includes hallucination detection, factual accuracy, relevance scoring, toxicity detection, bias assessment, and retrieval quality metrics. You can also define custom evaluators using LLM-as-a-judge patterns or traditional ML evaluation methods.
Both. Phoenix supports real-time trace collection and monitoring with sub-second latency, plus offline batch evaluation for deep analysis. Real-time alerts can trigger on quality degradation or safety violations.
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