# The best place to run AI agents

DevFeed: [The best place to run AI agents](<https://devfeed.tech/articles/the-best-place-to-run-ai-agents-85905.md>)

Original publisher: [Read original article](<https://www.infoworld.com/article/4234013/the-best-place-to-run-ai-agents.html>)

Author: David Linthicum

Published: 2026-10-13T09:00:00Z

Content type: article

Language: en

Sources: [InfoWorld](<https://devfeed.tech/sources/infoworld.md>)

Topics: [Local AI](<https://devfeed.tech/topics/local-ai.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>), [private cloud](<https://devfeed.tech/topics/private-cloud.md>), [Serverless](<https://devfeed.tech/topics/serverless.md>), [Deployment Strategies](<https://devfeed.tech/topics/deployment-strategies.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [artificial-intelligence-cloud-architecture-cloud-computing](<https://devfeed.tech/tags/artificial-intelligence-cloud-architecture-cloud-computing.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-architecture](<https://devfeed.tech/tags/cloud-architecture.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [cost](<https://devfeed.tech/tags/cost.md>), [data](<https://devfeed.tech/tags/data.md>), [data-center](<https://devfeed.tech/tags/data-center.md>), [databases](<https://devfeed.tech/tags/databases.md>), [deploy](<https://devfeed.tech/tags/deploy.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [development](<https://devfeed.tech/tags/development.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [inference](<https://devfeed.tech/tags/inference.md>), [it](<https://devfeed.tech/tags/it.md>), [language-en](<https://devfeed.tech/tags/language-en.md>), [latency](<https://devfeed.tech/tags/latency.md>), [load](<https://devfeed.tech/tags/load.md>), [managed-services](<https://devfeed.tech/tags/managed-services.md>), [memory](<https://devfeed.tech/tags/memory.md>), [storytype-opinion](<https://devfeed.tech/tags/storytype-opinion.md>)

## AI overview

The article compares public cloud, private cloud, and owned hardware for running AI agents. It recommends matching each workload to its demand, data sensitivity, latency needs, and growth, then designing for portability as economics change.

## Source excerpt

Every few weeks, another enterprise leader asks me the same question: Should our AI agents run in the cloud or in our own data center? It sounds like a simple either/or, but it is the wrong way to frame the decision. Agents are not a single workload. They are orchestrated collections of model inference, tool execution, memory state, and integration logic. Some pieces belong in the cloud, some belong on-premises, and the failure mode I see repeatedly is choosing a location by ideology or from a vendor's sales deck rather than by the characteristics of each agent's job. Let's work through this properly, because the wrong answer gets expensive fast. The case for the cloud The cloud's core advantage for agents is elasticity combined with managed services. Agent platforms on the public clouds give you model APIs on demand, serverless functions for tool execution, managed vector databases, identity integration, and observability, all reachable in days rather than months. If your agents have bursty or unpredictable demand, which most do, the cloud lets you pay only for what you consume instead of sizing hardware for peak load that never arrives. Cost is favorable at low to moderate and spiky utilization. Per-token API pricing and consumption-based compute mean you avoid upfront hardware investment, and you offload the undifferentiated heavy lifting of GPU failures, driver updates, and capacity planning. Performance is typically acceptable for front-office use cases, such as customer-facing assistants, workflow agents, and document triage, where latency to a cloud region is negligible, and you can scale out horizontally. Development and deployment are where the cloud really wins. Your teams can iterate on agent behavior daily, leverage native integrations with your existing SaaS and data services, and deploy globally with a click. The provider handles operations--model updates, security patching, and capacity management--which your already-overloaded platform engineers will a