Inferless

Inferless is a serverless GPU inference platform for deploying custom machine learning models from Hugging Face, Git, Docker, or the CLI. Autoscaling and per-second billing.

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Serverless GPU inference for model deployment

Inferless is a serverless GPU inference platform for deploying custom machine learning models. It is built to take a model from file to endpoint in minutes and is positioned for production workloads that need GPU-backed inference without managing infrastructure directly.

The platform supports deployment from Hugging Face, Git, Docker, or the CLI, and it adds operational controls such as automatic redeploy, custom runtimes, writable volumes, monitoring, dynamic batching, and private endpoint settings. The pricing page shows pay-per-second billing on NVIDIA T4, A10, and A100 GPUs, with shared and dedicated instance options.

Key capabilities

Multiple deployment paths

Deploy models from Hugging Face, Git, Docker, or the CLI, then use automatic redeploy to keep endpoints in sync with code changes.

Serverless GPU autoscaling

Scale model endpoints from zero to hundreds of GPUs, with an in-house load balancer that adjusts capacity based on demand.

Custom runtime support

Customize the container environment to include the software and dependencies your model needs at runtime.

Writable volumes

Use NFS-like writable volumes with simultaneous connections to multiple replicas for model files and other shared data.

Dynamic batching

Enable server-side request combining to increase throughput for workloads that benefit from batching.

Monitoring and endpoint controls

Inspect call and build logs, and configure endpoint behavior such as scale-down, timeout, concurrency, testing, and webhook settings.

Common use cases

Production model serving
Deploy GPU-backed inference for computer vision, NLP, recommendation, or scientific computing models without managing GPU clusters directly.
Spiky traffic workloads
Handle workloads that fluctuate heavily by scaling endpoints from zero to hundreds of GPUs as requests increase or fall off.
High-throughput inference
Serve models that benefit from request combining, using dynamic batching to improve throughput under load.
Continuous model updates
Run model updates through automated redeploy flows so changes in Hugging Face, Git, Docker, or CLI-based workflows reach the endpoint with less manual re-importing.
Cost- and isolation-aware deployments
Choose shared or dedicated GPU instances based on whether the priority is lower cost or more consistent resource allocation and isolation.

Pros and Cons

Pros

Supports deployment from multiple sources, including Hugging Face, Git, Docker, and CLI.
Offers serverless scaling from zero up to hundreds of GPUs.
Provides operational controls such as custom runtimes, writable volumes, dynamic batching, and private endpoint settings.
Uses pay-per-second billing with no upfront costs.
Documents security measures including isolated execution environments, separate log streams, and encrypted model storage.

Cons

The source notes a 10–20 second cold start for first-time calls.
Pricing and performance vary by machine type and whether you choose shared or dedicated instances.
The platform is in private beta according to the pricing FAQ, so access criteria may apply.

FAQ

What kinds of models can I deploy on Inferless?

Inferless is designed for deploying GPU-based machine learning models. The source examples mention computer vision, NLP, recommendation, and scientific computing workloads, along with models such as Llama2 13B, Stable Diffusion ControlNet, and Vicuna 7B.

How do you get a model onto the platform?

The home page says you can deploy from Hugging Face, Git, Docker, or your CLI. It also supports automatic redeploy so model updates can be pushed without manual re-imports.

How does billing work?

Inferless uses pay-per-second billing. Charges are based on the time your model runs in a healthy state and the machine type you select, and the pricing page says there are no upfront costs.

What should I expect for latency?

The pricing page says first-time calls can have a 10–20 second cold start, while later calls depend on inference time. That means latency can vary depending on whether the model is already warm.

What is the difference between shared and dedicated instances?

Inferless says shared instances split GPU resources across users for a lower-cost option, while dedicated instances reserve the full GPU for one user and are positioned for more consistent performance and data isolation.

Quick Facts

Category: Serverless GPU inference
Primary users: Developers, small teams, startups, and larger organizations
Deployment inputs: Hugging Face, Git, Docker, CLI
GPU options: NVIDIA T4, A10, A100
Billing model: Pay per second
Website: inferless.com