Inferless

Inferless is a DevOps & Infrastructure tool that deploys machine learning models on serverless GPUs in minutes. It offers scalable and effortless custom machine learning model deployment.

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At a glance

Pricing

Freemium · Usage-based · Enterprise

Free tier

Yes

API

Yes

Skill level

Technical

About

What is Inferless?

Inferless provides a blazing-fast serverless GPU inference platform designed for deploying machine learning models quickly and efficiently. It allows users to deploy models from Hugging Face, Git, Docker, or the CLI, with options for automatic redeployment. The platform is built for production workloads, scaling from zero to hundreds of GPUs with an in-house load balancer to manage spiky and unpredictable demands. Key features include custom runtime environments, NFS-like writable volumes, automated CI/CD, detailed monitoring, dynamic batching for increased throughput, and customizable private endpoints. Inferless aims to optimize high-end computing resources, enabling companies to run custom models built on open-source frameworks affordably, with benefits like zero infrastructure management, on-demand scaling, and lightning-fast cold starts.

Best used for

Ideal for developers who need to deploy machine learning models quickly, scale GPU inference on demand, and manage ML infrastructure effortlessly. Especially valuable for companies looking to optimize high-end computing resources and reduce GPU cloud bills.

Common actions

deploy machine learning models

scale GPU inference

manage ML infrastructure

optimize cloud costs

Capabilities

Key features

Serverless GPU inference
Custom runtime environments
Automated CI/CD
Dynamic batching
Private endpoints
Detailed monitoring
NFS-like writable volumes

Target Audience

developer

Integrations

hugging-facegitdocker

Pricing & Plans

Freemium · Usage-based · Enterprise

Not publicly disclosed. Check inferless.com for current pricing.

FAQs

What types of GPUs are available on Inferless?

Inferless supports Nvidia A100, A10, and T4 GPUs for blazing-fast inference. You can choose between shared and dedicated instances, with varying RAM and vCPU configurations to match your model's requirements.

How does Inferless handle billing for GPU usage?

Billing is usage-based, charged per second for the compute resources your models consume. The cost depends on the duration your models are running and the machine type selected. There are no charges when models are not actively inferring if minimum replicas are set to zero.

Can I deploy large or custom machine learning models?

Yes, Inferless supports model sizes up to 16GB. For larger models, you can contact their team for assistance. The platform allows for custom runtime environments, ensuring compatibility with various software and dependencies your models might require.

What is the difference between Shared and Dedicated GPU instances?

Shared instances allocate GPU resources among multiple users, offering cost-effectiveness for smaller tasks. Dedicated instances provide exclusive access to an entire GPU, ensuring consistent high performance for large-scale tasks or when data isolation is critical.

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