Modal

Modal – High-performance AI infrastructure is a cloud platform that lets startups and developers run, scale, and deploy custom AI models and workloads with ultra-fast containerized compute. It offers autoscaling, seamless integration of user code, hardware options (including GPUs like Nvidia H100/A100, and CPUs), and serverless-like pricing that charges by actual compute usage. The system emphasizes fast cold boots, lightweight deployment, and the ability to bring your own code while scaling to thousands of GPUs as demand spikes. It supports running OpenAI-compatible LLM services, diffusion models, fine-tuning, batch processing, and a variety of data storage and orchestration capabilities, all accessible via Python and a minimal amount of infrastructure management. The platform targets models, inference, training, and data-heavy tasks with a focus on speed, reliability, and security (SOC 2 and HIPAA in certain configurations).

How to Use Modal

1. Define your environment and code. Bring your own Python functions, specify hardware, memory, and any dependencies.
2. Deploy your function as a web-accessible endpoint or batch job. Use simple decorators or configuration to expose APIs and run tasks.
3. Auto-scale as needed. Modal automatically scales containers from zero to thousands of GPUs to handle bursts, then scales back down.
4. Monitor and debug. Use built-in debugging, logs, and observability features; mount cloud storage and export logs to external tooling.

Disclaimer: Costs are based on actual compute usage; pricing varies by resource type and region.

Use Cases

• Language model inference and serving with drop-in OpenAI-compatible API replacements
• Fine-tuning and training with GPUs (A100, H100) on demand
• Batch processing and parallel task execution at scale
• Data processing, model evaluation, and RAG-style workflows
• Deploying web services and APIs with secure HTTPS endpoints

Core Capabilities

• Serverless-like compute: scale to hundreds or thousands of GPUs on demand, with autoscaling
• Bring-your-own-code: run custom Python functions with minimal boilerplate
• Sub-second cold boots: fast startup times for interactive development
• Fine-tuning and training: provision Nvidia A100/H100 GPUs in seconds for experiments
• Batch processing and job queues: efficient parallel execution for large workloads
• Web endpoints: deploy and manage web services with secure endpoints
• Integrations: seamless storage mounting (S3, R2, etc.) and observability/logging
• Flexible environments: choose hardware, memory, and compatibility with popular ML frameworks
• Security and governance: SOC 2 and HIPAA-ready configurations for enterprise needs
• Cost-aware pricing: pay by actual compute usage, with transparent per-second/per-CPU pricing
• Collaboration and scalability: support for teams and larger organizations with scalable infrastructure

How It Works

• Define hardware and environment alongside your Python functions; deploy as modular units (functions) that can be invoked or run in parallel.
• Modal handles container orchestration, autoscaling, and resource provisioning, enabling high-performance compute at scale without traditional infrastructure headaches.
• Logs, metrics, and storage are easily integrated for observability and reproducibility.

Safety and Legal Considerations

• Use in compliance with applicable laws and terms; validate licensing for models and data; ensure data privacy and security when handling sensitive information.