10 best LLM observability tools to know in February 2026

In the context of deploying AI at scale, LLM observability tools are crucial for ensuring model compliance with regulations like the EU AI Act by offering beyond-traditional metrics such as automated safety tests, real-time guardrails, and regulatory framework mapping. These tools track AI system behavior in real-time, capturing traces from model inputs and outputs, monitoring costs, and flagging risks like hallucinations or PII leaks, thus providing a deeper analysis compared to traditional software monitoring. The text evaluates various LLM observability tools, such as Openlayer, Braintrust, LangSmith, Langfuse, MLflow, Deepchecks, Galileo, Arize, Credo AI, and IBM Watsonx, highlighting their strengths and limitations in areas like evaluation depth, governance, security, and integration. Openlayer is noted for its comprehensive approach, offering 100+ automated tests, real-time security guardrails, and compliance mapping, making it ideal for enterprises needing unified evaluation, security, and compliance capabilities. The selection of an LLM observability tool should align with an organization's specific deployment needs and regulatory requirements, especially for those in regulated industries requiring real-time security controls and governance.