Our solutions offer far more competitive prices than large cloud providers such as AWS or GCP and let you quickly spin up services

Why choose containers?

  • Save time deploying models so as not to slow down ML development.
  • Build your custom runtime containers with your own software.
  • Choose between complete and fractional GPUs
  • Seamless scaling of replicas and advanced configuration for Batching
  • No need to worry about provisioning for Hardware.

How does it work?

  1. Select your model - Select the model you want to deploy. You can deploy a custom model available on the HuggingFace/AWS/ GCP for NLP, computer vision, or other tasks types

  2. Choose your model configuration - Upon completion of the call, based on your configuration, we would autoscale down your container, thus saving you in inference costs. You would be charged only for the inference used.

  3. Create and manage your endpoint - You can load your model into a machine of your choice. As of now, we offer 2 kinds of machines:

    1. NVIDIA A100: The NVIDIA A100 is a high-performance graphics processing unit (GPU) designed for a variety of demanding workloads including machine learning inference. It used Ampere architecture to provide a substantial performance boost over the T4, which is based on the older Turing architecture.

    2. NVIDIA T4: The NVIDIA T4 is designed for energy efficiency, with relatively low power consumption. It is a more cost-effective way to deploy machine learning models. If your workloads are not latency-critical and model sizes are relatively small T4 can give you much better cost efficiencies.

  4. Call your APIs in Production - You can get the endpoint details and the Model Workspace API keys. Simply call the model in production and enjoy the services.