Vast.ai connects you to a global network of decentralized GPUs, offering prices that make AWS look like highway robbery. It operates on a marketplace model—essentially the Airbnb of computing—where anyone from Tier 3 datacenters to crypto miners can rent out their idle hardware. The result is the lowest pricing in the industry, with RTX 4090s frequently available for under $0.30/hour and H100s around $1.60/hour.
For researchers and hobbyists, the economics are undeniable. Fine-tuning a 7B parameter model for 10 hours on an RTX 4090 costs roughly $3.00 on Vast.ai. The same workload on a hyperscaler like AWS or Azure would likely force you into an A100 tier costing $30-$40, assuming you could even get quota. Vast also recently introduced a serverless inference engine that supports "mixed hardware" worker groups, allowing you to route traffic to different GPU types based on load, though its core value remains firmly in raw instance rental.
The catch is variability. Because hosts range from professional facilities to residential basements, reliability and bandwidth fluctuate wildly. One host might offer 10 Gbps fiber; another might struggle to download your dataset at 50 Mbps. While you can filter for "Secure Cloud" (verified datacenters) to mitigate this, you lose some of the rock-bottom pricing that makes Vast attractive. The platform is best used for fault-tolerant workloads like batch processing or checkpointed training, where an instance interruption is an annoyance rather than a disaster.
Technically, the experience is "Docker-first." You select an image, set your launch arguments, and SSH in. The Python SDK and CLI are functional but utilitarian. Unlike RunPod, which feels like a cohesive cloud platform, Vast feels like a power tool: highly effective in the right hands, but likely to cut you if you aren't paying attention.
Skip Vast.ai if you need guaranteed uptime, uniform bandwidth, or strict compliance for sensitive enterprise data. Use it if you are a solo dev, researcher, or startup stretching a seed round, and you're comfortable managing your own checkpoints and failovers.
Pricing
There is no free tier; you pay per second from the moment an instance starts. The primary draw is the raw hourly rate—often 60-80% cheaper than major clouds. Consumer cards like the RTX 3090/4090 start around $0.15-$0.30/hr.
The hidden cost is efficiency: a cheaper host with slow bandwidth can cost you more in "idle" download time than a slightly more expensive host with fast pipes. Storage is billed separately (~$0.10/GB/month), and interruptible "spot" instances can be reclaimed abruptly. There are no surprise egress fees, but the cost of restarting a job on a new node after a failure (time lost) is the real tax.
Technical Verdict
Vast exposes a raw, functional REST API and a Python SDK (vastai-sdk) that wraps it. Documentation is adequate but assumes familiarity with Docker and SSH. Latency varies by host location. Reliability is the main friction point; you must write your code to be resilient to node failures. Setup is fast—instances usually spin up in under a minute once the Docker image is cached.
Quick Start
from vastai_sdk import VastAI
# pip install vastai-sdk
vast = VastAI(api_key='YOUR_API_KEY')
# Find an RTX 4090 cheaper than $0.40/hr
offers = vast.search_offers(query='gpu_name=RTX_4090 price_num<0.40 rented=False')
print(f"Found {len(offers)} GPUs. Best price: ${offers[0]['dph_total']}/hr")Watch Out
- Instance death means data loss; always mount persistent storage or sync to S3 frequently.
- Download speeds are not guaranteed; check the host's 'Inet Down' stat before renting.
- Consumer GPUs (RTX series) do not support NVLink, making them poor for multi-GPU training across nodes.
- The 'interruptible' pricing tier is cheaper but will kill your job instantly if a higher bidder appears.