Galileo vs LangSmith vs Arize AI: 11 Best LLM Evaluation Platforms 2026

Best for Production RAG monitoring

Galileo (#1, 9.3/9.4). The best platform for production RAG, offering powerful, real-time hallucination detection and deep system insights. It also handles Real-time hallucination detection.

Best for Debugging LangChain applications

LangSmith (#2, 9.1/9.4). The essential debugging and evaluation tool for anyone building with the LangChain framework. It also handles Tracing complex agent behavior.

Best for Enterprise-scale model observability

Arize AI (#3, 8.9/9.4). An enterprise-grade, unified platform for monitoring both traditional ML and LLM applications at scale. It also handles Unified traditional ML and LLM monitoring.

What to look for in an LLM evaluation platform?

Focus on three areas: First, the evaluation framework itself—does it support the metrics you need (e.g., RAG-specific, safety) and allow for custom logic? Second, production readiness—can it handle your traffic with low latency and provide real-time alerts? Third, integration—does it seamlessly connect with your existing stack (e.g., LangChain, OpenAI, vector databases)?

How is LLM evaluation different from traditional model monitoring?

Traditional monitoring focuses on statistical metrics like accuracy, precision, and drift in structured data. LLM evaluation deals with unstructured text, requiring new metrics to measure qualitative aspects like hallucination, relevance, toxicity, and conversational quality, often without ground truth.

How to choose

1. 1First, map your primary use case: Are you debugging complex agent chains (favor LangSmith), monitoring a high-throughput production RAG system (favor Galileo), or integrating LLMs into an existing enterprise MLOps workflow (favor Arize AI)?
2. 2Next, assess your team's resources. Managed platforms accelerate deployment but have recurring costs. Open-source frameworks like our wildcard pick, Ragas, offer maximum flexibility but require significant engineering effort to implement and maintain.
3. 3Finally, run a proof-of-concept with your top 2-3 candidates. The ease of integrating their SDK and the clarity of the insights you gain from your own data will be the ultimate deciding factor.

What is an LLM evaluation platform?

An LLM evaluation platform is a specialized tool that helps developers and MLOps teams measure, monitor, and improve the performance of large language models. It provides metrics, dashboards, and workflows to track quality, detect issues like hallucinations, and analyze user interactions, both during development (offline evaluation) and in production (online monitoring).

What's the difference between LLM evaluation and LLM observability?

They are closely related. LLM evaluation is the act of scoring a model's output based on specific criteria (e.g., faithfulness, relevance). LLM observability is the broader practice of monitoring the entire LLM-powered system in real-time, which includes evaluation as well as tracking operational metrics like latency, cost, and token usage, and providing tools for tracing and debugging.

Can I build my own LLM evaluation framework?

Yes, many teams start by building their own frameworks using open-source libraries like Ragas, DeepEval, or simply custom scripts. This offers maximum control but requires significant engineering investment to build and maintain features like data pipelines, dashboards, and alerting that commercial platforms provide out-of-the-box.

How much do LLM evaluation platforms cost?

Pricing models vary. Most offer a free tier for small projects. Paid plans typically start from a few hundred dollars per month for startups and can scale to tens of thousands per month for large enterprises, often based on the volume of data processed (e.g., number of traces or API calls).