
Deploy and scale machine learning models on serverless GPUs in minutes.
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$0.000555/sec
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$0.000185/sec
$0.000341/sec
$0.001491/sec
$0.000092/sec
$0.000170/sec
$0.000745/sec
Free 50GB/month, then $0.3/GB/month
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ML model deployment platform

High-performance AI infrastructure for developers to deploy, train, and scale ML workloads.

GPU serverless for ML
Serverless GPUs for AI
Accelerate AI model inference with optimized compilation and serverless deployment.
Platform for web developers
Inferless addresses the challenges of GPU elasticity and sharing by utilizing a proprietary algorithm and an in-house built load balancer. This system optimizes model loading and maintains SLAs by using a cluster of always-on machines, ensuring efficient utilization of GPU resources and balancing autoscaling with desired latency, even though GPUs are not as elastic as CPUs in standard Kubernetes deployments.
Shared instances on Inferless allocate GPU resources among multiple users, offering a cost-effective solution with variable performance suitable for smaller or infrequent tasks. Dedicated instances, conversely, provide exclusive access to an entire GPU, delivering consistent high performance at a higher cost, which is optimal for large-scale tasks or when data isolation is critical. The choice depends on workload demands, performance requirements, and budget.
Yes, Inferless allows engineering teams to deploy not only the model file but also integrate pre-processing and post-processing functions. The platform automatically creates the necessary endpoints and provides monitoring data for these end-to-end model deployments, simplifying the entire inference pipeline.
Inferless is optimized for instant model loading, ensuring sub-second responses even for large models. While a model like GPT-J might take 25 minutes to cold start traditionally, Inferless can reduce this to approximately 10 seconds. This is achieved through its serverless GPU architecture and proprietary algorithms that optimize model load and resource allocation, eliminating warm-up delays.
Inferless prioritizes customer data and privacy by isolating execution environments using Docker containerization, preventing interaction between individual customer environments. Log streams are securely separated with AWS CloudWatch Logs access controls, retained for 30 days, and then deleted. Model hosting storage is encrypted using AES-256, and models and data are never shared across customers.
Inferless operates on a pay-per-second, usage-based billing model. You are only charged for the compute resources used when your models are actively running in a healthy state. If you configure your minimum replicas to zero, no machines are spun up when there are no inference requests, meaning you incur no charges during periods of inactivity. This ensures cost efficiency by avoiding idle costs.
Source: inferless.com