Modal Review, Pricing & Features

Modal review 2026: the serverless GPU and CPU cloud for AI and ML workloads. Compare features, per-second pricing, and top Modal alternatives.

Category: AI Infrastructure & MLOps
Pricing: Freemium, from Free ($30/month in compute credits)
Verified: Not yet
Last updated: July 18, 2026
Founded: 2021
Headquarters: New York, New York, United States

Free PlanAPIAIFreemium

Overview

Modal is a serverless cloud platform purpose-built for AI, machine learning, and data workloads. Instead of provisioning servers or managing Kubernetes, developers write plain Python functions and decorate them to run in the cloud, with Modal handling containerization, scaling, and GPU allocation automatically.

Founded in 2021 by Erik Bernhardsson and Akshat Bubna, Modal reached unicorn status after an $87 million Series B in September 2025 led by Lux Capital. The company is headquartered in New York with additional offices in San Francisco and Stockholm.

Key Features

Modal's headline feature is serverless GPU compute with per-second billing, supporting GPUs like A10G, A100 and H100 alongside standard CPU functions. Containers spin up quickly and scale to zero automatically when not in use, so teams only pay for active compute time.

Beyond raw compute, Modal supports scheduled and event-triggered jobs, persistent storage volumes for datasets and model weights, one-line web endpoint deployment for serving models as APIs, and sandboxed execution environments, all driven from a Python-native SDK rather than YAML or Dockerfiles.

Pricing

Modal uses usage-based, per-second billing for CPU, GPU and memory consumption. A free Starter tier provides $30 per month in compute credits along with a handful of workspace seats and modest concurrency limits, making it accessible for individual developers and small projects.

The Team plan costs $250 per month plus metered compute usage, and includes $100 in monthly credits, unlimited seats, and significantly higher container and GPU concurrency. Enterprise plans are custom-priced and add features like dedicated support and higher limits for larger organizations.

Key Features

Serverless GPU compute — Run AI training, fine-tuning and inference jobs on A10G, A100 and H100 GPUs with per-second billing and automatic scaling.
Python-native SDK — Deploy functions to the cloud using ordinary Python decorators instead of Dockerfiles or Kubernetes manifests.
Fast cold starts and autoscaling — Containers start in seconds and automatically scale to zero when idle, minimizing wasted spend.
Web endpoint deployment — Turn Python functions into HTTP endpoints for serving models and APIs with minimal configuration.
Persistent storage volumes — Attach durable storage volumes to functions for datasets, checkpoints, and model weights across runs.
Scheduled and event-driven jobs — Trigger functions on a cron schedule or in response to events for batch processing and pipelines.
Sandboxed code execution — Run untrusted or dynamically generated code in isolated, secure containers, useful for AI agent workloads.
Per-second usage billing — Pay only for the exact compute time consumed, billed at second-level granularity across CPU and GPU resources.

Pros & Cons

Pros

Python-native developer experience removes the need for Docker and Kubernetes expertise
Fine-grained per-second billing keeps costs closely tied to actual usage
Fast cold starts make serverless GPU workloads practical for interactive use cases
Free tier with monthly credits lowers the barrier for individual developers and small teams
Backed by well-known investors and a founding team with strong open-source and infrastructure credibility

Cons

GPU costs can still add up quickly for sustained, high-volume training workloads
Primarily oriented toward Python; less native support for other languages
Team and Enterprise plans add a base subscription fee on top of usage costs
Newer platform compared to established cloud providers, with a smaller enterprise track record
Advanced networking and compliance features may lag larger hyperscalers

Pricing

Starter
Free Monthly
Team
$250/month + compute usage Monthly
Enterprise
Custom Custom

Frequently Asked Questions

What is Modal used for?

Modal is used to run AI, machine learning and data workloads, including GPU-accelerated training, fine-tuning, batch inference and data pipelines, without managing servers or Kubernetes.

How does Modal pricing work?

Modal bills by the second for CPU, GPU and memory usage. It offers a free Starter tier with monthly compute credits, a $250 per month Team plan plus usage, and custom Enterprise pricing.

Does Modal support GPUs?

Yes. Modal offers serverless access to GPUs including A10G, A100 and H100, billed per second based on actual usage.

Who founded Modal?

Modal was founded in 2021 by Erik Bernhardsson, former engineering leader at Spotify, and co-founder and CTO Akshat Bubna.

Is Modal free to try?

Yes. Modal's Starter plan is free and includes $30 per month in compute credits along with limited workspace seats and concurrency.

How much funding has Modal raised?

Modal raised an $87 million Series B in September 2025 led by Lux Capital, reaching a valuation of about $1.1 billion.

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