Model Serving Services
Packages & Pricing.

ML model deployment services for fast inference

ML model deployment services turn a trained model into an API that answers quickly and scales on demand. Omeiro serves open-source models on vLLM and NVIDIA Triton, adds GPU autoscaling and inference caching, and speeds things up with TensorRT.

You bring the model, and we deliver the endpoint, scaling rules and monitoring.

Choosing a model serving setup for production

Model serving decisions start with traffic shape. A chat assistant needs fast first tokens and streaming, while a batch classifier needs throughput. Bring the model name and size, expected requests per minute and your target latency. These decide the GPU type, how many replicas you need and whether vLLM or Triton is the better engine. Our Basic plan deploys one open-source model on vLLM.

A common mistake is sizing for average load and then watching queues grow at peak. Standard adds GPU autoscaling and caching so capacity follows demand and repeated prompts cost less, while Premium adds TensorRT acceleration and routing between models. With sensible model serving, your application calls a stable API, expensive GPUs are not idle overnight and a new model version can be swapped in without changing client code.