Overview
Modal is a Python-first serverless cloud computing platform designed for AI/ML workloads. Its core philosophy is "just write Python decorators, Modal handles all cloud infrastructure"—automatically managing containers, GPUs, caching, storage, and scheduling, billed by the second.
Compared to Baseten, which focuses on "model serving," Modal is more general-purpose: it can run training, batch processing, web scraping, data processing, and inference. Used in production by companies like Suno, Substack, and Ramp, it is one of the most popular AI cloud platforms among Python developers.
Key Features
- Python Decorator Deployment: Use @stub.function() to run Python functions on cloud GPUs
- Per-Second Billing + Sub-Second Cold Start: Cold start takes about 1-2 seconds, billed per second, no cost when idle
- Full GPU Model Support: Choose on demand from T4/L4/A10G/A100/H100
- Persistent Storage: Storage primitives like Volume, NetworkFS, and Dict
- Scheduled Tasks/Cron: Supports scheduled triggers and event-driven execution
- Webhooks/REST API: Expose Python functions directly as HTTP endpoints
Use Cases
- Rapid prototyping of AI inference APIs
- Large-scale data processing (ETL/features)
- Model training and fine-tuning
- Individual developers who want to use GPUs without managing infrastructure
- Scheduled batch processing jobs
Pros
- Top-tier Python development experience
- Per-second billing + fast cold start
- Rich GPU resources including H100
- Great debugging experience
- Generous free tier ($30 per month)
Pricing
$30 free credit per month. CPU $0.000038/second, memory $0.00000667/GB·second, GPU by model: T4 $0.000164/second, A10G $0.000306/second, A100 40G $0.000583/second, H100 $0.001267/second. Billed per second, no charge when idle.
Summary
Modal is an AI cloud playground for Python developers—deploy with decorators, pay per second, access rich GPUs, and enjoy sub-second cold starts. Ideal for AI prototyping, batch processing, and inference services for individuals and small teams.
Version History
- LoRA Speedrun 公开排行榜：6分05秒微调Qwen2.5-1.5B达GSM8K 61.1%准确率 (2026-07-20): The LoRA Speedrun project has launched a public leaderboard, competing on the fine-tuning runtime of Qwen2.5-1.5B on fixed hardware (single L40S). The current record is held by @Saivineeth147 at 6 minutes and 5 seconds, achieved using sequence packing and completion-only loss masking techniques, resulting in approximately 2x speedup compared to the baseline of 11 minutes and 57 seconds, with higher accuracy (61.1%). The project provides a free Modal sandbox for verification, and any submission must be confirmed through three independent reproductions.