Vast.ai

Vast.ai is a GPU cloud platform for on-demand compute with live market pricing and per-second billing for training, inference, fine-tuning and rendering.

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Overview

Vast.ai is a GPU cloud platform for renting compute on demand. It is positioned for AI and machine learning workloads, but the source also points to use cases such as inference, fine-tuning, rendering, transcription, and general GPU programming.

The platform emphasizes API-native provisioning, live market pricing, and per-second billing. Users can search capacity, deploy instances in seconds, and choose between on-demand, interruptible, and reserved rentals depending on workload and budget.

Features

Live GPU market pricing

Prices are set by supply and demand across the platform, so users see live market rates rather than a fixed catalog price.

Multiple instance types

Choose among On-Demand, Interruptible, and Reserved instance types depending on whether you need uptime, lower cost, or longer-term capacity.

Programmatic deployment tools

Launch and manage GPU workloads from the terminal or code with a CLI, Python SDK, and REST API.

Search and filter capacity

Search offers and filter by model, VRAM, price, and availability before provisioning an instance.

Unified platform API

Use the same API surface to manage instances, billing, keys, volumes, templates, and serverless resources.

Fast provisioning flow

Move from sign-up to running workloads in under five minutes, with API key access and per-second billing.

Use Cases

AI and ML training
Provision scalable compute for model training, fine-tuning, and experimentation when you need to start quickly and pay only for active usage.
Inference and text generation
Run open-source LLMs or custom models as endpoints, with serverless options for automatic benchmarking, optimization, and autoscaling to zero.
Distributed training clusters
Use dedicated multi-node GPU clusters with InfiniBand networking for larger training jobs that need coordinated nodes.
Batch processing and transcription
Accelerate tasks such as transcription, batch preprocessing, and other GPU-backed data pipelines that benefit from short-lived compute.
Rendering and virtual computing
Provision GPU-enabled virtual machines or rendering instances for graphics work, virtual computing, and 3D visualization.

Pros and Cons

Pros

Live pricing across a large pool of GPUs makes it easier to compare capacity and cost.
Per-second billing reduces waste for short runs or variable workloads.
The platform exposes CLI, Python SDK, and REST API options for programmatic workflows.
Users can choose between On-Demand, Interruptible, and Reserved capacity to match workload needs.
The source describes availability across 40+ data centers and 68+ GPU types.

Cons

Interruptible instances may be reclaimed, so they are better suited to fault-tolerant workloads than jobs that cannot be paused.
The sources do not provide full detail on enterprise features, support tiers, or formal SLA terms.

FAQ

How do I access Vast.ai programmatically?

Vast.ai provides API-native GPU cloud access. The source describes a REST API, plus CLI and Python SDK, for searching offers, creating instances, managing resources, and tracking billing.

How quickly can I get started?

The source says you can start with as little as $5, then search GPUs by model, VRAM, price, and availability before deploying instances in seconds.

What kinds of workloads does Vast.ai support?

Vast.ai supports GPU cloud, serverless inference, and clusters. The use-case and developer pages indicate it is used for training, inference, fine-tuning, rendering, and other GPU workloads.

What pricing options are available?

The pricing page shows three instance types: On-Demand, Interruptible, and Reserved. Interruptible instances are preemptible and may be reclaimed; On-Demand emphasizes guaranteed uptime; Reserved is for longer commitments.