modal-com
Modal is a production-grade AI infrastructure platform that lets developers run inference, training, batch processing, and secure sandboxes with fast cold starts, instant autoscaling, and a Python-first developer experience.
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Quick Overview
Best for: Developer Tools
What it does
Developer Tools software for decision-makers comparing workflow fit and alternatives.
Best fit
Developer Tools
Pricing snapshot
Free from $30 / month (page text: 'Get Started $30 / month free compute')
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modal-com
Modal is a cloud platform that provides high-performance AI infrastructure for developers and teams, enabling inference, training, batch processing, and secure sandboxes with a developer experience that feels local. It exposes a Python-first SDK so developers can define cloud environments in code and ship workloads without changing language or tooling. The platform emphasizes fast cold starts, instant autoscaling, global GPU access, and production observability to support building and running AI systems at scale.
Serverless platform for AI and data teams to run compute at scale.
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Claim this listing for $29Key Features
Modal SDK
A Python-first SDK that defines cloud environments in code so developers can stay in Python and ship to the cloud.
AI-native runtime
Runtime engineered for heavy AI workloads with super-fast autoscaling and containers that boot instantly to achieve sub-second cold starts.
Elastic cloud capacity
Autoscale from 0 to 1000+ GPUs, routing workloads across clouds and regions in real time to get GPUs on demand with no capacity planning.
Production observability
Integrated logging and full visibility into functions, sandboxes, and containers for out-of-the-box observability.
Inference
Optimized stack for inference workloads with support for LLMs, multi-modal models, token streaming, WebRTC, and WebSocket-based online inference.
Training
Support for fine-tuning (SFT, LoRA, full fine-tunes), reinforcement learning, multi-node training, and parallel hyperparameter sweeps.
Sandboxes
Programmatically scalable, secure, ephemeral environments for running untrusted code, coding agents, background agents, and RL rollouts.
Global GPU infrastructure
Access to H100s, A100s, A10Gs, B200s and other GPUs, with automated fleet health and ability to scale with demand.
Security & governance
Team controls, battle-tested isolation, SOC2 & HIPAA compliance, and data residency controls.
Pricing
Starter
$30 / month (page text: 'Get Started $30 / month free compute')- Entry-level offering referenced on the site
- Includes mention of free compute (see site text)
Use Cases
LLM and multi-modal inference
Deploy and scale LLMs, image, audio, and video generation models with support for token streaming and low-latency online inference.
Model training and fine-tuning
Fine-tune open-source models, run reinforcement learning experiments, and execute multi-node training and hyperparameter sweeps.
Batch and async workloads
Run large-scale batch jobs such as evaluations, embeddings generation, re-ranking, and dataset generation across thousands of GPUs.
Secure sandboxes and agents
Spin up isolated sandboxes for coding agents, background autonomous agents, and large-scale RL rollouts.
GPU-accelerated research
Attach to sandboxes for research workloads, scale thousands of concurrent runs, and pay by the second with no reserved capacity.
Audio/voice applications
Transcribe speech at scale, stream transcripts in real time, and host voice-chat LLM interfaces and TTS services.
Integrations
WebRTC / WebSocket
Built-in support for low-latency streaming and online inference connections.
Whisper (example)
Example workflows include transcribing speech in batches with Whisper.
Kyutai STT (example)
Example shows using Kyutai STT to transcribe speech and stream transcripts at speed of speech.
Chatterbox (example)
Example includes deploying a TTS API with Chatterbox to generate natural audio from text.
ACE-Step (example)
Example usage for turning prompts into music with ACE-Step.
Benefits
Limitations
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Frequently Asked Questions
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Getting Started
- 1 Step 1: Create an account via the site (Sign Up / Get Started).
- 2 Step 2: Install and configure the Modal SDK in your Python environment to define your cloud environment in code.
- 3 Step 3: Deploy workloads (inference, training, sandboxes) using the SDK and monitor using the built-in observability tools.
Support
docs
Documentation accessible via the 'Docs' link on the site.
contact
Contact Us link on the site for sales or support inquiries.
community
Slack Community referenced in the footer for user discussion and community support.
API
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