Sold by: Modal
Deployed on AWS
Modal is a serverless compute platform for AI, ML, and data teams.
3.9
Overview
Modal is a serverless compute platform for AI, ML, and data teams. We make it easy for developers to run workloads like ML inference, fine-tuning, and batch data jobs in the cloud. Our custom infrastructure allows us to spin up GPU-enabled containers in as little as one second, helping you iterate fast and scale up to large production workloads. We scale resources up and down for you so you only ever pay for what you use.
For custom pricing options and private offer please contact here .
Please contact sales@modal.com to discuss pricing before purchasing Modal.
Highlights
- Autoscale to hundreds of GPUs and back down to zero in seconds, without managing and configuring boilerplate infra.
- Deploy Python functions to the cloud using infrastructure-as-code to define custom container images and hardware requirements.
- Pay as you go and only pay for the resource time you use.
Details
Sold by
Delivery method
Deployed on AWS
New
Introducing multi-product solutions
You can now purchase comprehensive solutions tailored to use cases and industries.
Features and programs
Trust Center
Access real-time vendor security and compliance information through their Trust Center powered by Drata or Vanta. Review certifications and security standards before purchase.
Financing for AWS Marketplace purchases
AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
Pricing
Pricing is based on the duration and terms of your contract with the vendor, and additional usage. You pay upfront or in installments according to your contract terms with the vendor. This entitles you to a specified quantity of use for the contract duration. Usage-based pricing is in effect for overages or additional usage not covered in the contract. These charges are applied on top of the contract price. If you choose not to renew or replace your contract before the contract end date, access to your entitlements will expire.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/month |
|---|---|---|
Enterprise Platform Fee | The Enterprise tier monthly platform fee covers access to the full enterprise feature set, including >50 GPU concurrency, region selection, a private support channel, SSO, HIPAA BAAs, and more. This is separate from the usage component of billing, which is described in detail below. Usage pricing can be discounted based on volume commits. | $1,000,000.00 |
Dimension | Description | Cost/unit |
|---|---|---|
Modal Add-ons | Add-ons | $0.01 |
Modal Usage | Usage | $0.01 |
Dimensions summary
Your bill combines three separate components. The Enterprise Platform Fee is a fixed monthly charge for the enterprise feature set, including higher GPU concurrency, region selection, a private support channel, SSO, and HIPAA BAAs. Modal Usage covers actual compute you consume, billed per second by the resources you run. You can discount usage rates through volume commitments. Modal Add-ons cover extra items you elect beyond standard usage. The platform fee is flat regardless of usage, while the usage and add-on components scale with what you actually run.
Top-of-mind questions for buyers
Usage bills per second of actual compute you run. You are charged by the resource type, such as GPU seconds, physical CPU cores, memory per gigabyte-second, and storage per gigabyte. You never pay for idle capacity, because Modal autoscales down to zero when there is no request volume.
The three components bill independently on one invoice. The Enterprise Platform Fee is a fixed monthly charge unaffected by how much you run. Modal Usage scales per second with the resources you consume. Add-ons cover extra items you elect. Usage typically drives most of the bill for heavy compute workloads.
These capabilities come with the Enterprise Platform Fee, a fixed monthly charge. It covers over 50 GPU concurrency, region selection, SSO, HIPAA BAAs, and a private support channel. This fee stays flat regardless of usage. Your separate usage charges still scale per second with the compute you run.
www.modal.com+2
Helpful?
Vendor refund policy
All fees are non-refundable and non-cancellable except as required by law.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
Software as a Service (SaaS)
SaaS delivers cloud-based software applications directly to customers over the internet. You can access these applications through a subscription model. You will pay recurring monthly usage fees through your AWS bill, while AWS handles deployment and infrastructure management, ensuring scalability, reliability, and seamless integration with other AWS services.
Resources
Vendor resources
Support
Vendor support
Private Slack channel with the Modal team.
support@modal.com
support@modal.com
AWS infrastructure support
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Updated weekly
Top
10
In Serverless Workloads
Top
10
In High Performance Computing
Top
50
In High Performance Computing
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
GPU Container Provisioning
Custom infrastructure enables GPU-enabled container spin-up in approximately one second for rapid iteration and scaling.
Autoscaling Capability
Automatic scaling to hundreds of GPUs and down to zero resources within seconds without manual infrastructure configuration.
Infrastructure-as-Code Deployment
Python functions deployable to cloud using infrastructure-as-code approach with custom container image and hardware requirement definitions.
Resource Optimization
Dynamic resource allocation that scales up and down based on workload demands to optimize resource utilization.
Serverless Compute Architecture
Serverless platform supporting ML inference, fine-tuning, and batch data job execution without infrastructure management overhead.
Distributed Computing Runtime
Unified runtime that distributes Python code and AI libraries across thousands of CPUs, GPUs, or both, scaling from single machine to large clusters
Multi-Framework Support
Support for distributed execution of XGBoost, PyTorch, vLLM, and other AI libraries within a single platform
Infrastructure Deployment Flexibility
Deployment options including fully managed hosted experience, bring-your-own-cloud (BYOC) in customer VPC, VM-based infrastructure (EC2), and Kubernetes environments (AWS EKS and SageMaker HyperPod)
Enterprise Security Integration
Native integration with AWS Identity and Access Management (IAM) for access controls, policies, and governance standards, with data and processing isolation in private cloud
Workload Optimization and Resilience
Built-in head node resilience, intelligent autoscaling, advanced GPU sharing scheduling, and safe rollout mechanisms to maximize resource utilization and prevent cost overruns
Workflow Orchestration Framework
Open-source Metaflow framework for designing and developing data science and ML/AI applications
Managed Kubernetes Infrastructure
Scalable, cost-optimized, fully managed Kubernetes cluster specifically tuned for data-intensive batch workloads and GPU-intensive AI workloads
High Availability Management
Enterprise-grade infrastructure with managed high availability for business-critical ML and data workloads
Data Isolation and Compliance
SOC2 compliant security architecture ensuring no data or code leaves the customer's account
Compute Resource Optimization
Cost-optimized compute allocation with support for demanding GPU requirements in modern AI applications
Standard contract
No
Customer reviews
TANISHQ Y.
Smooth Developer Experience for Scaling Python & ML Workloads
Reviewed on Sep 30, 2026
Review provided by G2
What do you like best about the product?
modal has honestly been a good game changer for running and scaling python workloads and ml models . i am as developer experience is super smooth . modal is highly recommend it if you want to focus on code rather than ops..
What do you dislike about the product?
sometimes it is hard to keep track of the billing when running heavy GPU tasks and the guides could use a few more examples for complex setups....
What problems is the product solving and how is that benefiting you?
it helps us run python and ai models on heavy GPUs without dealing with complicated server setups. It saves us a lot of time on infrastructure and lets us launch code instantly...
Mohd Kaif K.
Simplified GPU Access for AI/ML Workloads
Reviewed on Sep 28, 2026
Review provided by G2
What do you like best about the product?
I really like how simple Modal Labs makes running AI workloads. The platform handles a lot of the infrastructure and scaling, allowing me to focus more on building and testing models. I also appreciate the flexibility of accessing GPU compute when needed without having to manage dedicated hardware.
What do you dislike about the product?
One area that could be improved is the learning curve for new users. Some of the setup and configuration can take time to understand, especially if you are not very familiar with cloud infrastructure. More beginner-friendly documentation and examples would make it easier to get started. For beginners, the initial setup and understanding how to structure deployment can take some time. It would be helpful to have more step-by-step guides, clearer examples for common use cases, and simpler explanations of configuration options. A more guided onboarding experience would make the platform easier to navigate for first-time users.
What problems is the product solving and how is that benefiting you?
Modal Labs simplifies AI workload management by handling infrastructure and scaling, making it easier to deploy and test models without managing servers. The flexibility in accessing GPU compute helps me focus more on development than on maintenance.
E-Learning
Amazing When It Clicks, But Often Confusing and Unhelpful
Reviewed on Sep 09, 2026
Review provided by G2
What do you like best about the product?
It's amazing and helps alot really ans literally
What do you dislike about the product?
Sometimes it is very very confusing and doesn't help
What problems is the product solving and how is that benefiting you?
Optimising my time
Ronit B.
Effortless Python Deployment for Compute-Heavy AI Workloads
Reviewed on Sep 01, 2026
Review provided by G2
What do you like best about the product?
I like that Modal makes it easy to run and deploy Python workloads without having to manage the underlying infrastructure. It is especially useful for running AI and compute-heavy tasks when I need extra resources.
What do you dislike about the product?
It can take some time to understand the platform and its deployment model. Debugging can also be a little difficult when something goes wrong in a remote environment.
What problems is the product solving and how is that benefiting you?
It simplifies running compute-heavy and AI workloads without needing to manage servers manually. This helps us run jobs on demand, scale resources when needed, and spend less time managing infrastructure.
shantanu g.
Best-in-Class Python Developer Experience with a Very Generous Free Tier
Reviewed on Aug 28, 2026
Review provided by G2
What do you like best about the product?
Modal provider is best-in-class for developer experience when working with Python. I used it to host my own STT and TTS models, and the overall workflow felt smooth and straightforward. On top of that, their free tier is very generous—like, very, very generous. look guys if you are looking for provider to host your agent or model like wisper and other then its really good option to use model i will higly suggest as its native support for paython deployment can give trouble on paid plan (might be withme bit still)
What do you dislike about the product?
Deployment can be a bit unpredictable sometimes. For example, it failed on me once when I tried to deploy it.
What problems is the product solving and how is that benefiting you?
As I said previously, it failed once during deployment.