Blazing fast serverless GPU inference to deploy ML models in minutes with auto-scaling and pay-per-use pricing.
Inferless is a serverless GPU inference platform that enables users to deploy machine learning models in minutes. It supports deployment from Hugging Face, Git, Docker, or CLI, with automatic scaling from zero to hundreds of GPUs. Features include custom runtimes, writable volumes, automated CI/CD, monitoring, dynamic batching, and private endpoints. It is SOC-2 Type II certified, penetration tested, and regularly scanned for vulnerabilities. Inferless is designed for production workloads, offering zero infrastructure management, pay-per-use pricing, and lightning-fast cold starts.
Key Features
check_circleDeploy from Hugging Face, Git, Docker, or CLI
check_circleAuto-scaling from zero to hundreds of GPUs
check_circleCustom runtime containers
check_circleNFS-like writable volumes
check_circleAutomated CI/CD with auto-rebuild
check_circleDetailed call and build logs
check_circleDynamic batching for increased throughput
check_circlePrivate endpoints with customizable settings
check_circleSOC-2 Type II certified
check_circlePenetration tested and vulnerability scanned
Use Cases
lightbulbData science teams deploy custom ML models from Hugging Face or Git repositories in minutes, eliminating the need to manage GPU infrastructure and reducing deployment time from days to hours.
lightbulbStartups with unpredictable traffic use Inferless to auto-scale from zero to hundreds of GPUs on demand, ensuring low latency during spikes while paying only for compute used.
lightbulbAI researchers run large language models with sub-second cold starts, enabling rapid experimentation and iteration without waiting for warm-up delays.
lightbulbEnterprise ML engineers leverage private endpoints and SOC-2 compliance to deploy models securely, meeting internal security policies and regulatory requirements.
lightbulbSaaS companies integrate Inferless APIs to serve real-time predictions to their users, achieving high throughput via dynamic batching and reducing per-request costs.
lightbulbDevelopers building AI-powered applications use custom runtimes to include specific dependencies, ensuring compatibility and reproducibility across deployments.
lightbulbProduct teams monitor model performance through detailed logs and build history, enabling continuous improvement and quick rollback if issues arise.
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