Built for Mass Scale: Hard-Won Lessons from Teams Running High Volume Inference Workloads in Production

Transitioning AI from prototypes to high-volume production environments involves significant challenges related to technical debt, infrastructure, and decision-making rather than model performance. At DigitalOcean Deploy 2026, a panel of engineering leaders from Workato, Hippocratic AI, and ISMG discussed their experiences with scaling AI inference workloads, emphasizing the need for robust orchestration, security measures, and infrastructure readiness. Workato focuses on agentic orchestration to manage enterprise applications efficiently, while Hippocratic AI addresses latency challenges in healthcare voice interactions, and ISMG leverages AI for cybersecurity intelligence. The panelists highlighted the importance of preparing enterprise stacks to support AI at scale, noting that AI has shifted from being a competitive advantage to a fundamental infrastructure component. They warned against the risks of inadequate AI integration and stressed the necessity of organizing data and workflows to maximize AI's potential. DigitalOcean’s AI-Native Cloud is positioned as a solution to these challenges, offering a platform that integrates inference, compute, data, and agent runtime to facilitate seamless scaling and deployment of AI applications.

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