AI observability tools are essential for monitoring the performance, data quality, security, and compliance of AI systems throughout their lifecycle, accommodating unique challenges such as non-deterministic outputs and model drift. These tools differ from traditional application monitoring by addressing AI-specific issues like bias and prompt injection vulnerabilities, ensuring real-time detection and prevention of threats. As the demand for AI observability grows, solutions like Openlayer stand out by unifying evaluation, observability, and compliance across various AI systems, offering real-time security guardrails, automated compliance mapping, and integration into CI/CD pipelines. While other tools like LangSmith, Braintrust, Langfuse, Arize AI, and Fiddler AI provide various features like evaluation capabilities, drift detection, and explainability, they often require manual setups or additional tools to achieve full enterprise governance and security. The market's expansion is driven by increasing model complexity and regulatory pressures, creating a need for comprehensive platforms that enable organizations to manage AI deployments efficiently without juggling multiple tools.