Modal vs Nebius Group

Hosted notebooks backed by Modal's GPUs, for profiling and experimenting without provisioning a machine.

Serverless GPU compute: a Python decorator puts a function on an accelerator, scales it from zero to thousands of containers and stops billing when it stops running — aimed at inference, fine-tuning and batch jobs rather than reserved clusters.

Batch execution of large jobs across Modal's fleet, described as one line of code on the product page.

Serve, scale and optimise model inference on Modal's runtime, with sub-second cold starts and autoscaling across regions.

Isolated, instantly-started containers for running untrusted or agent-generated code at scale — the primitive behind AI app-generation products.

Managed training runs on Modal's fleet, configured in Python alongside the rest of a team's code.

Fully managed MLflow deployment on Nebius AI Cloud for tracking experiments, metrics and artifacts across the machine-learning lifecycle without maintaining tracking-server infrastructure.

Nebius-managed Kubernetes operator that runs Slurm clusters on GPU infrastructure for fault-tolerant large-scale AI training, with topology-aware scheduling and automatic node health checks and recovery.

Rented GPU clusters with storage and networking for training and serving models, billed by the GPU-hour.

Nebius AI Cloud's on-demand GPU runtime that runs containerised AI workloads as Jobs and hosts custom models behind HTTP Endpoints without provisioning or managing clusters.