Where should enterprises host their AI agents?
Blog post from Northflank
Enterprise AI agent hosting depends on each workload’s data residency, compliance, isolation, compute, networking, supporting services, security, deployment, and operational-management requirements. Agents are typically composed of a runtime plus services such as databases, queues, storage, model APIs, GPUs, private networking, and secrets management, with stronger sandboxing needed when they execute generated or untrusted code. AWS, Google Cloud, and Microsoft Azure are positioned for organizations already invested in their respective cloud, AI, identity, and data ecosystems, while Modal targets Python-oriented serverless CPU and GPU workloads. Kubernetes and self-hosted infrastructure provide the greatest control over infrastructure, networking, and security policies but require substantial platform engineering to operate supporting capabilities. Northflank is presented as a full-stack alternative that combines agent runtimes, isolated sandboxes, GPUs, managed databases, CI/CD, networking, governance, and bring-your-own-cloud deployment options through one control plane. Organizations may also use multiple platforms, provided they maintain consistent security, identity, networking, deployment, and audit controls across environments.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 29 | 2,716 | 579 | 174 | -60% |
| Kubernetes | 12 | 1,226 | 164 | 69 | -56% |
| Secrets Management | 9 | 1,002 | 214 | 87 | -60% |
| Serverless | 6 | 309 | 84 | 49 | -59% |
| Observability | 4 | 1,527 | 341 | 123 | -63% |
| Platform Engineering | 4 | 381 | 114 | 42 | -73% |
| AI Coding Assistant | 1 | 741 | 214 | 85 | -59% |
| Developer Experience | 1 | 209 | 105 | 47 | -63% |
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