MLOps & Model Serving

Why Self-Host Your MLOps Platform?

Cloud MLOps services from AWS SageMaker, Google Vertex AI, and Azure ML charge for compute, storage, model hosting, and API invocations — costs that are notoriously difficult to predict and frequently result in unexpected bills of thousands of dollars. Managed ML platforms like Weights & Biases ($50/user/month), Neptune.ai, and Comet charge per-seat fees on top of cloud compute costs. More critically, training models on cloud infrastructure means your training data, model weights, and inference logs pass through and are stored on the provider’s servers — a significant concern for organizations working with proprietary datasets, healthcare records, or financial models.

Self-hosted MLOps platforms provide experiment tracking, model versioning, pipeline orchestration, and model serving on your own infrastructure. You can train models on your own GPUs, track experiments with full reproducibility, version datasets and model artifacts, and deploy models for inference — all without sending data or model weights to external services. This is essential for organizations training on sensitive data (medical images, financial transactions, classified documents) where data residency requirements prohibit cloud processing.

The cost dynamics of self-hosted ML are compelling for sustained workloads. A single NVIDIA A100 GPU server purchased outright costs roughly what 6-12 months of equivalent cloud GPU rental would cost, and it runs 24/7 without per-hour billing. For inference serving, self-hosted model endpoints eliminate the per-request costs that make cloud AI APIs expensive at scale. The combination of fixed infrastructure costs, data privacy, and full control over the ML pipeline — from data preprocessing through training, evaluation, and deployment — makes self-hosted MLOps the default choice for any organization doing serious machine learning with sensitive data or predictable compute requirements.