# Lessons from Teams Running High-Volume AI Inference in Production

DevFeed: [Lessons from Teams Running High-Volume AI Inference in Production](<https://devfeed.tech/articles/built-for-mass-scale-hard-won-lessons-from-teams-running-high-volume-inference-workloads-in-production-19900.md>)

Original publisher: [Read original article](<https://www.digitalocean.com/blog/lessons-running-inference-workloads>)

Author: Hasan Nabulsi

Published: 2026-07-02T10:00:00Z

Content type: article

Language: en

Sources: [DigitalOcean](<https://devfeed.tech/sources/digitalocean.md>)

Topics: [AI Inference](<https://devfeed.tech/topics/ai-inference.md>), [Inference Performance](<https://devfeed.tech/topics/inference-performance.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>), [Security](<https://devfeed.tech/topics/security.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>)

Tags: [2026](<https://devfeed.tech/tags/2026.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-inference](<https://devfeed.tech/tags/ai-inference.md>), [ai-ml](<https://devfeed.tech/tags/ai-ml.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [inference](<https://devfeed.tech/tags/inference.md>), [latency](<https://devfeed.tech/tags/latency.md>), [orchestration](<https://devfeed.tech/tags/orchestration.md>), [performance](<https://devfeed.tech/tags/performance.md>), [security](<https://devfeed.tech/tags/security.md>), [video](<https://devfeed.tech/tags/video.md>)

## AI overview

This article summarizes lessons from engineering leaders at Workato, Hippocratic AI, and ISMG on operating AI inference in production. It focuses on latency management, orchestration, agent permissions, governance, security guardrails, and infrastructure decisions needed to move from prototypes to reliable high-volume systems.

## Source excerpt

Moving AI from a flashy demo to a high-volume production environment is a transition filled with hidden technical debt and infrastructure challenges. There's a difference between calling the OpenAI API in a weekend prototype and serving 50,000 concurrent users who need sub-200ms latency, graceful fallbacks, and reliable output every single time. It is rarely a "model problem." Instead, it is a problem of decisions, trade-offs, and architecture. At DigitalOcean Deploy 2026, we hosted a panel of engineering leaders from Workato, Hippocratic AI, and ISMG. Moderated by Karnik Modi, DigitalOcean's Senior Manager of Engineering, panelists shared the lessons they've learned while running inference workloads at scale. The session focused on managing P99 latency spikes in real-time interactions, restricting agent permissions to prevent "admin" vulnerabilities, and ensuring infrastructure is policy-aware before production traffic hits. These insights move beyond model performance to address the orchestration and security guardrails required for reliable, mass-scale AI. Watch the full recorded session from Deploy 2026: View YouTube video The Built for Mass Scale Panelists Each panelist represents a company operating at the frontier of production AI, where the gap between a working prototype and a reliable system serving real users is the entire challenge. From orchestrating autonomous agents across thousands of enterprise applications to running real-time clinical voice conversations where latency is a patient-safety issue to deploying AI-powered intelligence across a global cybersecurity media network, these teams have confronted the infrastructure, governance, and architectural decisions that only surface at scale. Oscar Wu -- AI Research Technical Lead, Workato Research Lab Workato is an enterprise integration platform that connects over 14,000 applications and has orchestrated more than one trillion automated tasks, and its AI focus has shifted to agentic orchestration'--bui