🤖
AIllowpages
AI + Yellow Pages · The AI Tools Search Engine
🤖

Arize Phoenix

MLOps Freemium

Arize Phoenix is an open-source observability and evaluation tool purpose-built for large language model applications, including retrieval-augmented generation pipelines and AI agents. It provides detailed tracing of LLM calls, embeddings, and retrieval steps so engineers can pinpoint exactly where a RAG pipeline returns irrelevant context or where an agent's reasoning goes off track. With built-in evaluation frameworks for hallucination detection, relevance scoring, and cost tracking, Phoenix helps teams debug and improve production LLM systems with the same rigor traditional MLOps brings to classical models.

💰 Pricing
Freemium
📂 Category
MLOps
🏷️ Tags
mlops, llmops, observability, rag, open source, ai agents
↗ Visit Tool 🔍 Similar Tools ← Back to All Tools
🔗 Related Tools
Weights & Biases
MLOps
Weights and Biases is a leading MLOps platform that provides experiment tracking, dataset versioning, model registry, and hyperparameter optimization tools for machine learning teams. It integrates with all major ML frameworks including PyTorch, TensorFlow, Keras, and Hugging Face, and enables teams to reproduce experiments, compare runs, and collaborate on model development at scale. AI research labs and enterprise ML teams use Weights and Biases as their central hub for model development lifecycle management.
BentoML
MLOps
BentoML is an open-source model serving framework that standardizes the packaging and deployment of ML models as production-ready API services across cloud, on-premises, and edge environments. It provides a unified interface for serving models from any framework including PyTorch, TensorFlow, scikit-learn, and Hugging Face, with built-in batching, adaptive concurrency, and multi-model serving capabilities. ML engineers use BentoML to eliminate the gap between model development and production deployment with a framework that handles serving infrastructure concerns automatically.
Neptune AI
MLOps
Neptune AI is an MLOps metadata store designed for data science teams to efficiently organize, track, and compare thousands of machine learning experiments, streamlining collaboration and model development. By leveraging its experiment tracking, model registry, and team collaboration features, data scientists and engineers can accelerate model deployment and improve overall productivity. Ideal for large-scale ML projects, Neptune AI is particularly suited for teams working on complex AI applications.