Home / Companies / Vast.ai / Blog / Post Details
Content Deep Dive

GPU Cloud Comparison: Vast.ai vs. Runpod vs. Modal (2026)

Blog post from Vast.ai

Post Details
Company
Date Published
Author
Team Vast
Word Count
1,926
Company Posts That Month
3
Language
English
Hacker News Points
-
Post removed?
No
Summary

Amid rising demand for AI inference and GPU compute, Runpod, Vast.ai, and Modal offer different approaches to affordable cloud GPU access: Runpod provides managed persistent instances, serverless endpoints, and clusters across Community and Secure Cloud tiers; Vast.ai combines a large marketplace of community and data-center providers with secure cloud, serverless, and cluster options; and Modal offers a Python-native, serverless platform that automatically provisions GPUs for applications, agents, and inference. The comparison cites October 2026 starting RTX PRO 6000 prices of $1.07 per hour for Vast.ai, $1.69–$2.09 for Runpod, and $3.03 for Modal, while noting that Modal’s per-second scaling can reduce costs for intermittent workloads. Vast.ai is presented as having the broadest hardware and location availability, Runpod as a polished and turnkey managed option, and Modal as best suited to teams prioritizing infrastructure-free serverless deployment. All three are described as SOC 2 Type II certified and developer-friendly, with differing strengths in reliability, automation, configurability, and workload fit. The author ultimately favors Vast.ai for sustained, cost-sensitive, and highly configurable AI workloads, though Modal is positioned strongly for variable demand and Runpod for managed production deployments.

Trends Found in this Post
Trend Post Mentions Total Month Mentions Posts Companies MoM
Serverless 15 878 233 106 +5%
AI Agents 2 5,997 1,304 256 -18%
Developer Experience 2 492 247 92 -18%
LLM 2 5,236 1,063 243 -33%
AI Model Fine-tuning 1 568 161 65 -42%
Use This Data

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.