Choosing AI inference: hosted APIs, endpoints, serverless GPUs vs self-hosting

On November 10, 2026, the AI section of DEV Community published a guide to choosing an AI inference deployment option. The article first lays out the dimensions to evaluate: model type (proprietary vs open-weight), traffic shape, latency target, model features, data control requirements, and cost model. Building on those dimensions, it compares four deployment approaches: managed model APIs, managed ML endpoints, serverless GPU services, and self-hosted serving on containers. Its stated trade-offs are that managed model APIs are the fastest way to get started, that managed ML endpoints and serverless GPU services trade some degree of control for convenience, and that self-hosted serving on containers offers the strongest control over cost, data residency, and portability. The piece also notes that Control Plane can run containerized model servers in AWS, GCP, and Azure regions, as well as in environments the user operates. As of the reporting date, the public discussion of this topic remains at the level of the guide's decision framework and the trade-offs it sets out; there is no follow-up material on implementation or revision.