Meta’s AI site publishes current research, models, and product information.
How is Meta AI different from a hosted model provider?
Meta develops models, while another company may operate the API that serves them. With an open-weight model, model developer and inference provider can be separate choices. A team might use hosted inference or run the weights in its own environment.
That flexibility moves more operational responsibility to the deployer. Access to weights does not include monitoring, scaling, security, or support by itself.
When should a company consider Meta models?
Meta models can fit workloads that value deployment control, customization, or a choice of hosting vendors. The real comparison must include the license, serving cost, hardware, support, and measured task quality. An inference provider can reduce operational work, while self-hosting may give stronger control over data location and release timing.
What does self-hosting a Meta model require?
Running a model outside a managed API shifts responsibility to the operating team. It must select hardware, package the model, control access, monitor latency and errors, and plan upgrades without breaking downstream behavior. Fine-tuned or quantized variants also need their own evaluation records because they may not behave like the original checkpoint. Open model weights reduce one form of dependency, but they do not remove infrastructure or governance work. Compare self-hosted inference with an inference provider using total operating cost, not token price alone.