Private deployment or cloud for AI agents: three factors
Whether to run enterprise AI agents in a private deployment or in the cloud comes down to three factors: data, cost and operations. Data: if the internal systems and data the agent needs cannot leave the company, private deployment is the only option. Cost: cloud is pay-as-you-go with little upfront spend; private deployment needs servers and setup first but is easier to control for heavy, long-term use. Operations: the vendor runs the cloud; a private deployment needs someone for upgrades, backups and monitoring. You can trial in the cloud first, then decide whether to deploy privately.
When a company brings in AI agents, it soon faces a choice: use the vendor's cloud service, or deploy on its own servers (private deployment). Neither is better in every case. Comparing them on data, cost and operations usually makes the answer clear.
1. Data: can the systems and data the agent uses leave the company?
Agents are useful because they connect to company systems: querying the ERP, reading contracts, triggering approvals. Whether the data in those systems may be sent outside the company for processing is the first question.
- If the data may leave, or the agent only touches public or non-sensitive data: cloud is an option;
- If the data cannot leave, for example because of regulation, customer contracts, or systems that only run on the internal network: private deployment is the only option.
Consider the model as well. In a private deployment, the agent can call a model you host yourself or an external model service; whether data leaves the company depends on the model calls too.
2. Cost: upfront spend and long-term usage
Cloud is usually pay-as-you-go. There are no servers to prepare and little upfront spend, which suits a trial. As usage grows, cost grows with the number of calls.
A private deployment needs servers and setup first, and computing power if you also host the model. For heavy, long-term use, cost is easier to control and not exposed to vendor price changes.
When comparing cost, calculate "a one-month trial" and "company-wide use for three years" separately; the answers may differ.
3. Operations: who handles upgrades, backups and monitoring?
In the cloud, the vendor handles upgrades, backups and availability. A private deployment needs someone responsible for version upgrades, data backups, monitoring and troubleshooting. If you have an IT or operations team, the bar is not high; if not, either get support from the vendor or start in the cloud.
How hard it is to deploy matters too: can you bring up an instance quickly with something like Docker Compose, and are upgrades and backups clearly documented?
Trial in the cloud, then decide on private deployment
If the three factors are not clear yet, trial one concrete task in the cloud with non-sensitive data. Once the agent proves useful, decide on private deployment based on your data and usage. For sorting platforms by deployment model first, see choosing an enterprise agent platform: self-hosted, SaaS and low-code.
Summary
Private or cloud: compare whether data can leave the company, upfront spend versus long-term usage, and whether you have people to run it. If data cannot leave, deploy privately; otherwise, trial in the cloud and decide once it proves useful.
For reference, FIM One from Beijing FIM Network Technology Co., Ltd. (北京飞沐网络科技有限公司) is a self-hosted enterprise agent platform that offers both paths: try it in the cloud first, then decide between self-hosting and a private deployment. The Community edition is fully featured, free forever and self-hosted with Docker Compose; the Enterprise edition adds private deployment, dedicated support and an SLA. FIM One is source-available software and works with any OpenAI-compatible model endpoint. See cloud trial and private deployment for the two paths and pricing for editions.