Modal Pricing in 2026

Pricing Overview

Modal uses a usage-based pricing model with a generous free tier, meaning you only pay for the compute resources your code actually consumes. There are no charges for idle containers, no reserved instance commitments, and no upfront infrastructure costs. Billing is granular -- measured by the CPU cycle and GPU second -- so you scale from zero to thousands of containers and back without paying for anything sitting unused. Modal offers two main plans: a Starter plan at $0 per month that includes $30 in free compute credits, and a Team plan at $250 per month designed for collaborative workloads with higher limits and shared workspaces. For organizations with large-scale requirements, Modal offers Enterprise agreements with custom pricing through their sales team, including dedicated GPU capacity and data residency controls. This structure keeps Modal accessible for individual developers experimenting with AI workloads while scaling predictably for production teams running inference, training, and batch processing at scale.

Plan Comparison

Modal structures its plans around compute credits and team collaboration features rather than feature gating. All plans share the same core platform capabilities -- sub-second cold starts, elastic GPU scaling, and integrated observability. Here is how the plans break down:

The Starter plan is a strong entry point for solo developers and small experiments. The $30 monthly credit covers meaningful workloads -- enough to run several hundred GPU-hours on lower-end hardware or thousands of CPU-hours for batch processing and sandboxed environments. Once you exceed the included credits, you pay the standard per-resource rates for additional usage with no surprise overages or hidden minimums. The Team plan at $250 per month adds $100 in compute credits, multi-user workspaces, and priority support. Additional team members cost $25 per user per month. We recommend the Starter plan for prototyping and early development, and the Team plan once you have multiple engineers deploying production workloads that require shared visibility and collaboration features.

Hidden Costs and Considerations

Modal's per-cycle billing model is transparent, but several factors can affect your actual monthly spend. GPU costs dominate most AI workloads, and pricing varies significantly by GPU type -- an A100 costs substantially more per second than a T4. Choosing the right GPU for your workload is the single biggest lever for cost control. Storage costs apply separately for Modal's built-in storage layer, including model weights, datasets, and container images stored in their globally distributed storage system. Network egress fees can accumulate when moving large volumes of inference results or training data out of Modal's infrastructure to external services. Container startup time, while sub-second, still bills from the moment a container begins initializing -- frequent scale-to-zero patterns with high request volumes can add up in initialization overhead across thousands of invocations. Teams running multi-node training jobs should account for inter-node communication overhead, which can extend job duration and increase costs beyond the raw GPU-hour estimate. Finally, while Modal's AI-native runtime is up to 100x faster than Docker for container initialization, complex dependency trees in your container images can still add meaningful seconds to startup, increasing costs on high-frequency workloads.

How Modal Pricing Compares

Modal competes in the AI infrastructure space against both serverless platforms and traditional cloud GPU providers. Here is how it compares to alternatives in the category:

Modal differentiates itself through its developer experience and serverless GPU model. Unlike traditional cloud providers where you provision and manage GPU instances directly, Modal handles container orchestration, autoscaling, and hardware allocation automatically with no YAML or config files required. The $30 monthly free credit on the Starter plan is competitive -- it allows teams to evaluate the platform under real production-like workloads before committing budget. Compared to running your own GPU instances on AWS or GCP, Modal eliminates the overhead of managing Docker images, Kubernetes clusters, and idle GPU instances that drain budget between jobs. The tradeoff is that sustained high-utilization workloads running 24/7 may cost more on Modal than reserved instances on traditional clouds, where long-term commitments unlock significant discounts. We find Modal delivers the strongest value for bursty workloads, rapid prototyping, batch processing jobs, and teams that prioritize developer velocity over squeezing the last dollar out of GPU utilization rates. For teams already running inference, training, or sandboxed code execution, Modal's programmable infrastructure approach and multi-cloud capacity pool make it straightforward to consolidate workloads onto a single platform without managing the underlying orchestration.