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.
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.
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 appreciate.
The drawbacks are real, though. At sustained high utilization, cloud inference costs grow linearly with volume, and margins compress quickly. You also take on data gravity and governance risk. Agents that reach into sensitive systems push regulated data through provider tenancy, and compliance teams ask hard questions, as they should. Finally, there’s lock-in. Agent frameworks, proprietary model endpoints, and provider-specific tools are easy to establish and painful to unwind.
In context
- Topic: Cloud y Arquitectura — Nube pública, híbrida, costos y decisiones de infraestructura.
- Source: InfoWorld
- Published: 13/10/2026
Continue reading at the original source →
Excerpt published automatically by the site radar. The full text belongs to its publisher and is linked above.
Why it matters
The cloud conversation moved from migrate everything to deciding what goes where. That maturity is a good sign, but it also makes the discussion harder: there is no single answer any more, it has to be argued case by case, and very few people have the numbers to do it.
What I look at in these announcements is portability. The more comfortable a platform is, the harder it usually is to leave, and that comfort gets paid for in negotiating power three years later, exactly when renewal comes up and there is no credible alternative left.
What usually goes wrong
The mistake that repeats is not sizing egress traffic. The numbers get done on compute and storage, which are the visible ones, and the surprise arrives with data transfer. It is the line on the bill I have most often seen dismantle a well-built business case.
What to watch
- How hard it would be to leave or move a piece to another provider, which is future negotiating power.
- Who sees the bill and in what detail: with no owner for the spend, the spend grows on its own.
- Where the data physically sits and what local regulation demands about that.
How I read this entry
What I would install alongside any move to the cloud is the discipline of switching things off. Tag everything by owner, review monthly what is running unused, and give somebody the authority to turn it off. Without that, waste eats the promised savings within two years.
This entry is an excerpt from the original source, selected by the site radar. The commentary above is the site's own and does not belong to the cited publisher.
Comments
No comments yet. Yours can be the first.
Living through this in your own team?
Open the chat and tell me how you're handling it. I'm interested in comparing notes.
