Running AI Agents Inside Your Own VPC: How to Pick a Self-Hosted Agent Platform in 2026

Selecting a platform for AI agents in a private VPC requires separate choices for the agent framework or application layer, such as LangGraph, Dify, Agno, or CrewAI, and the runtime layer that operates workloads, state, secrets, and logs. The comparison distinguishes true BYOC runtimes, including Qovery, TrueFoundry, and Northflank, which deploy into customers’ cloud accounts, from self-hosted frameworks that require teams to operate their own infrastructure and managed services such as Amazon Bedrock AgentCore and Google Vertex AI Agent Engine, which provide private connectivity but run model inference in provider accounts. It argues that compliance assessments center on compute location, data and trace storage, KMS key control, and auditable evidence, while common exposure points include hosted tracing, vector stores, tool egress, secrets, memory stores, evaluation data, and non-production environments. For many SOC 2 and GDPR use cases, managed model APIs accessed through AWS PrivateLink or Google Private Service Connect may be sufficient, whereas self-hosted models are more relevant for strict residency, air-gapped, no-subprocessor, or regulated fine-tuning requirements. The recommended architecture places agent services, databases, observability, network controls, and delivery environments inside the customer VPC while allowing either private managed-model access or self-hosted inference, and the cost analysis notes that managed APIs are generally cheaper at lower utilization while GPU self-hosting can become economical at sustained high volume, though Kubernetes operations and compliance staffing remain significant considerations.

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