Modal
Serverless high-performance infrastructure for running CPU, GPU, and data-intensive compute.
What is Modal?
Modal (developed by Modal Labs) is a high-performance serverless infrastructure platform built to run CPU, GPU, and data-intensive compute at scale. While alternative solutions like Baseten or Fireworks AI focus on hosting pre-packaged model APIs, Modal AI is built for developers who want to bring their own Python code and run it without managing complex cloud infrastructure. The platform features a custom Rust-based container stack designed for sub-second container starts, allowing users to scale resources up instantly. Whether you are running model evaluations, fine-tuning large systems like GLM 5, or running batch processing workloads, the platform manages the compute layer. Developers can also take advantage of Modal Sandboxes to securely execute generated code, and dynamically allocate custom Modal GPU instances on demand.
Best Modal use cases by task, role, industry, and platform
These use cases show where Modal fits best, ranked by fit score before popularity or pricing.
Modal Pricing Plans
Compare Modal free options, Modal paid pricing plans, and usage notes before you choose the best way to use this AI tool in 2026.
Free plan with $30/mo credit, Team from $250/mo plus compute
Designed for small teams and independent developers. Includes $30/month of free compute credits, up to 3 workspace seats, 100 containers, and up to 10 GPU concurrency.
Designed for startups and scaling organizations. Includes $100/month of free compute credits, unlimited seats, 1000 containers, up to 50 GPU concurrency, custom domains, and static IP proxies.
Designed for organizations requiring dedicated support and advanced compliance. Features volume-based pricing, custom GPU concurrency limits, Okta SSO, audit logs, HIPAA compatibility, and private Slack support.
Pricing updated:Jun 11, 2026
Modal AI Features
Modal Pros and Cons
Pros
- Serverless pricing model ensures you only pay for active compute down to the second
- No cost for idle resources, reducing overall GPU expenses
- Robust developer experience with seamless local-to-cloud transition
- Generous monthly free tier for testing and personal projects
Limitations
- Requires structuring code around the platform's specific Python decorator paradigm
- Cold boot times, although optimized, are still present when spinning up from zero instances
- Using non-standard regions incurs a pricing markup