Originally published at https://blog.runc.ai/cost-effective-serverless-endpoints-docker-model-inference/ . Key Takeaways Cost-effective serverless endpoints for Docker-based model inference work best when traffic is bursty, uneven, or event-driven rather than constantly high. Docker makes model deployment portable, but image size, model loading, GPU compatibility, and startup behavior directly affect endpoint cost and latency. Dedicated GPU instances can still be the better choice for steady, high-throughput inference workloads that keep the GPU busy most of the day. A practical path is to package the model cleanly, test it on a persistent GPU environment, then move bursty production traffic to a serverless GPU endpoint. On RunC.ai, teams can test Docker-based inference on GPU Pods and evaluate Serverless GPU Preview when API traffic is uneven enough to benefit from elastic workers. Introduction A model that runs well in a local Docker container is not automatically cost-effective in production.…