Arize AI raised $70M to bring evaluation and observability into the 'agentic AI' era
Arize AI has raised $70 million in Series C funding to expand its AI evaluation and observability platform. Arize's platform helps companies ensure their AI systems work reliably in real-world applications by empowering them with testing, debugging, and optimization tools.
Arize AI has announced a $70 million Series C funding round, marking the largest investment ever in AI observability. The round was led by Adams Street Partners, with participation from notable investors including Microsoft's M12 venture fund, Datadog, and PagerDuty, alongside existing investors Foundation Capital, Battery Ventures, TCV, and Swift VC.
The company, founded in 2020 with the mission to "make AI work," is focusing on a critical challenge in the AI industry: ensuring large language models (LLMs) and AI agents operate reliably at scale in real-world applications. Arize's platform offers tools for testing, debugging, and optimizing AI systems before deployment.
The company's product suite includes Arize AX for enterprise customers, the open-source Phoenix observability tool (which has reached two million monthly downloads), and Arize AI Copilot, launched in 2024 with over 50 built-in skills for AI engineering tasks. Arize is also expanding its partnership with Microsoft through deeper integrations with Azure AI Studio and the Azure AI Foundry.
Looking ahead, Arize plans to use the funding to scale its evaluation and monitoring capabilities for LLMs and multi-agent systems, expand its open-source offerings, and grow its engineering team. The company's founders, Jason Lopatecki and Aparna Dhinakaran, emphasize their commitment to building AI infrastructure that promotes reliability, transparency, and accountability as AI systems take on increasingly critical roles across industries.
Ellie Ramirez-Camara is the News Editor at Data Phoenix, where she writes the daily AI newsdesk — covering model releases, research, funding rounds, and policy across the AI and machine-learning industry. She tracks announcements from labs and startups alike and distills them into clear, source-linked reporting for practitioners.
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