AI DevOps & Model Deployment Software Pricing 2026

AI DevOps (MLOps) covers everything needed to take a trained model from a notebook to reliable production: packaging, serving behind an API, autoscaling, versioning, monitoring, and CI/CD for retraining and redeployment. Platforms like BentoML, Baseten, Modal, and Replicate streamline serving and scaling so teams don't build deployment infrastructure from scratch.

Costs are driven by compute, especially GPUs, billed per second or hour while your model is serving, plus storage and bandwidth. Serverless model platforms charge per request or per compute-second, which suits bursty traffic, while reserved GPU instances suit steady high volume. Many platforms add a management subscription on top of the raw compute.

Serverless GPU platforms (pay-per-use) are cheaper for spiky or low-volume inference because you avoid idle costs, though they add cold-start latency. Dedicated GPUs are cheaper at sustained high utilization. The right choice depends on your traffic pattern and latency tolerance; many teams mix both.

Watch for idle GPU time, cold-start over-provisioning, data egress, model storage, and monitoring/observability fees. Retraining pipelines, autoscaling tuning, and the engineering time to maintain deployment infrastructure are ongoing costs often underestimated in initial budgets.