Serverless GPUs: Effortless Infrastructure that scales with you
Deploy your machine learning models on serverless GPUs in minutes
How Inferless is approaching the model deployement process
Hassle free resource management
Using GPUs effectively could be challenging as Kubernetes doesn't allow for GPU sharing and GPUs are not as elastic as CPUs. We take care of autoscaling and latency to ensure efficient utilization of resources.
Balance autoscaling and latency
Through our proprietary algorithm, we help companies get the desired latency and linear autoscaling by using a cluster of always-on machines to optimize model load while maintaining the SLA.
Keep inference cost under control
With our developer-friendly usage-based billing module, we help companies only pay for the inference seconds they use for each model so that they don’t have to worry about fixed inference costs.
End-to-End Model Deployment
With us, engineering teams can just deploy the model file, along with the pre-processing and post-processing functions. We automatically create the endpoints and provide the monitoring data.
Why Serverless GPUs?
10x Cost Savings for Deployment
Deploy Any Model with 3 Lines of Code
Reduce Model Cold Start by 99%
Simply Run and Push with Our Serverless GPUs
Run
With us, you can execute complex machine learning models with just a few lines of code, eliminating the need for in-depth knowledge of ML concepts.
Push
We've made the process easy for you so that you do not have to focus on GPU provisioning, intricating docker files