Collect Video Testimonials That Build TrustModal
Serverless GPU infrastructure platform for AI workloads. Run inference, training, and batch jobs with sub-second cold starts. Scale from zero to thousands of GPUs automatically. Pay-per-second pricing.
About Modal
Modal is a high performance AI infrastructure platform that enables developers to run compute intensive workloads without managing servers. Founded in 2021 by Erik Bernhardsson and Akshat Bubna, Modal has grown to unicorn status, serving customers like Meta, Substack, and Ramp.
The platform abstracts away infrastructure complexity through a serverless model where developers write Python functions with simple decorators. Modal handles containerization, GPU allocation, scaling, and scheduling automatically. The company built its entire stack from scratch including a custom container runtime in Rust, distributed file system, and multi cloud scheduler to achieve sub-second cold starts for GPU workloads.
Modal pools capacity across AWS, GCP, and Oracle Cloud Infrastructure, dynamically routing workloads to optimize for availability and cost. This architecture allows customers to burst from zero to hundreds of GPUs in seconds then scale back to zero, paying only for actual compute time used.
Key Features
- Sub-Second Cold Starts: Memory snapshotting technology loads large models into GPU memory in seconds, not minutes.
- Multi-Cloud GPU Pool: Access to T4, A100, H100, and B200 GPUs across AWS, GCP, and Oracle Cloud without quotas or reservations.
- Autoscaling: Scale automatically from zero to thousands of GPUs based on demand, then back to zero when idle.
- Serverless Pricing: Pay only for compute time used with per-second billing and no minimum usage requirements.
- Custom Runtime: Built-from-scratch container runtime, file system, and scheduler optimized for AI workloads.
- Observability: Real-time dashboard with logs, metrics, and execution traces for debugging production workloads.
- Sandboxes: Spin up thousands of isolated secure environments for executing AI-generated or untrusted code.
- GPU Notebooks: Collaborative browser-based notebooks with serverless GPU backing and auto-idle shutdown.
- Programmable Infrastructure: Define compute, storage, and networking entirely in Python code with zero YAML configuration.
- Usage Controls: Workspace budgets, incremental billing thresholds, and programmatic billing APIs for cost management.
Pricing
-
Starter
- $0/month
- Includes $30/month free compute credits
- Pay per second for CPU at $0.00003942/core/sec and memory at $0.00000672/GiB/sec
- GPU instances billed at T4 $0.000164/sec, A100 $0.00044/sec, H100 $0.001097/sec
- Requires payment method on file
-
Team
- $250/month + usage
- Additional compute credits included
- Access to programmatic billing APIs, tagging for cost attribution, and granular billing reports
- Incremental billing with workspace budget controls
-
Enterprise
- Custom pricing
- Custom invoicing, international bank transfers, split invoices, and committed spend options
- Transact through AWS and GCP marketplaces
- Dedicated support and SLAs
Pricing last updated: March 1, 2026 at 12:00 AM
Use Cases
- LLM inference and fine-tuning at scale
- Batch processing and media transcoding
- Computational biotech and protein folding
- AI agent sandboxing and code execution
- Real-time speech-to-text transcription
Pros & Cons
Pros:
- Sub-second cold starts eliminate waiting for GPU allocation
- True serverless model scales to zero when not in use
- No infrastructure configuration or YAML required
- Multi-cloud capacity provides better GPU availability
- Per-second billing with no minimum usage commitments
Cons:
- Primarily Python focused with limited native language support
- Enterprise features require custom sales engagement
Integrations
Python, JavaScript, TypeScript, Go, AWS Marketplace, GCP Marketplace, OpenTelemetry, Stripe Billing
FAQ
What is Modal used for?
Modal is used for running AI and data workloads including LLM inference, model fine-tuning, batch processing, and computational biology. It provides serverless GPU infrastructure that scales automatically from zero to thousands of GPUs.
How does Modal pricing work?
Modal uses per-second usage-based pricing. You pay only for compute time used with no minimum commitments. CPU costs $0.00003942 per core per second, memory is $0.00000672 per GiB per second, and GPUs range from T4 at $0.000164/sec to H100 at $0.001097/sec. The Starter plan includes $30/month in free credits.
What programming languages does Modal support?
Modal primarily supports Python for building applications. JavaScript, TypeScript, and Go can be used to call Modal Functions, run Sandboxes, and manage Modal resources through the client libraries.
How fast can Modal scale GPU resources?
Modal can scale to 100 H100 GPUs in 12 seconds, 200 H100s in 60 seconds, and 300 H100s in under 4 minutes according to their benchmarks. This is enabled by their custom container runtime and memory snapshotting technology.
Does Modal require Kubernetes or Docker knowledge?
No. Modal abstracts away all infrastructure management. You write Python code with decorators and Modal handles containerization, scheduling, and scaling automatically without requiring Kubernetes, Docker configuration, or cloud account management.
What GPUs are available on Modal?
Modal offers NVIDIA T4, A100, H100, and B200 GPUs pooled across multiple cloud providers including AWS, GCP, and Oracle Cloud Infrastructure. This multi-cloud approach provides better availability than single-cloud providers.
Is there a free tier?
Yes, the Starter plan is $0/month and includes $30 in free compute credits monthly. You can run CPU and GPU workloads within this credit allowance. A payment method is required to prevent abuse but you will not be charged if usage stays within the free credits.
Last edited
September 29, 2026 at 7:35 PM by Venkatraman C
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