Cloud or local? Wrong question. ☁️💻

Every AI agent pitch comes with a promise. Cloud vendors promise elastic scale and frontier models. Terminal-based “local” agents promise your sensitive work never leaves the building. Then someone looks at the network traffic and finds that the “local” agent has been shipping your schemas, code and query results to a cloud-hosted model the whole time. Meanwhile, the cloud agent everyone was afraid of turns out to be easier to lock down than the developer laptop it was supposed to replace.

In this week’s newsletter, we investigate what each runtime actually exposes, and why the debate itself misses the point. Most of what gets said about cloud and local agents is either overstated or outright wrong. Where your agent runs matters far less than what sits underneath it: the data foundation it reasons over, the identity it acts under, and whether you can trace every tool call it makes.

Here are 3 things the marketing won’t tell you:

☁️ What really leaves the building in the cloud: Learn why the tools a cloud agent calls usually run inside your own environment, and why the real exposure is the context you send out. You’ll also see how least-privilege agent identities, private connectivity like AWS PrivateLink or GCP Private Service Connect, and MCP gateways with allowlists and kill switches keep that flow contained.
💻 Why “local” isn’t airtight: Discover why many terminal agents run their harness on your machine but send their context to a cloud model. Even fully local models don’t make the attack surface disappear. It moves to shell access, prompt injection hidden in files, and credentials sitting on developer workstations.
🧭 Per-workload routing decisions: See why the answer isn’t picking a side. It’s deciding, workload by workload, which data may cross which boundary, under which identity, and with what evidence left behind.

It also points to two resources that go deeper on the foundations underneath: Ignition’s article on why your data architecture is holding your AI back, and the Scalefree expert sessions on getting your data platform ready for agentic AI and orchestrating agentic workflows.

Choosing cloud or local decides where your agent runs. It doesn’t decide whether you can trust it. That comes from a governed data layer, accountable identities and a full audit trail, and with those in place either runtime holds up.

Check out the video below 👇

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Cloud or local? Wrong question. ☁️💻

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