Modal
by Modal Labs, Inc.
The production cloud for AI
Modal is a Python-native serverless cloud platform that lets developers run AI/ML inference, training, fine-tuning, batch jobs, and sandboxed code on GPUs and CPUs with sub-second cold starts and instant autoscaling. Hardware and container images are defined directly in Python — no YAML, Dockerfiles, or DevOps required.
At a Glance
- Category
- Infrastructure & Cloud
- Pricing
- usage-based, per-second compute (separate GPU, CPU, memory, storage rates)
- Target Market
- AI startups, ML/AI engineering teams, Data teams, Generative-AI app builders (LLM, image, audio, video), Research labs and academics, Enterprises running production AI inference
- Founded
- 2021
- Headquarters
- New York, NY, USA
- Customers
- Thousands of customers (no exact public count)
Key Features
- ✓Serverless GPU access with instant autoscaling
On-demand access to T4, A10G, L40S, A100, H100, H200, and B200 GPUs; scales from 0 to 1000+ GPUs in seconds with no capacity planning or commitments.
- ✓Sub-second cold starts
A proprietary Rust container runtime and image builder boots containers and loads large models in under a second, even on in-demand GPU types.
- ✓Python-native infrastructure-as-code
Define container images, hardware, secrets, and scaling in pure Python via decorators, with no YAML, Dockerfiles, or separate DevOps scripts.
- ✓Broad workload support
LLM and multimodal inference, fine-tuning (SFT, LoRA, full), multi-node training, batch jobs, scheduled/cron jobs, web endpoints, and Sandboxes for untrusted code and agents.
- ✓Built-in storage and data primitives
High-performance distributed Modal Volumes plus the ability to mount S3, GCS, and other cloud storage directly into functions, with integrated logging and observability.
Capabilities
Use Cases
- •AI music and media generation at scale
Suno generates AI music and Substack transcribes podcasts at scale on Modal.
- •LLM inference and fine-tuning for production apps
Ramp deploys open-source LLMs and Lovable runs tens of thousands of concurrent containers on Modal.
- •Sandboxed execution of AI-generated code
Developers and agents run untrusted, AI-generated code in isolated sandboxes; over a billion sandboxes have been launched, including thousands of concurrent sandboxes for reinforcement learning.
Ideal For
Best For
- ✓AI/ML inference at scale
- ✓Fine-tuning and training open-source models
- ✓Batch processing and embeddings
- ✓Running AI-generated or untrusted code in sandboxes
- ✓Cron and scheduled compute jobs
Integrations
Market & Ratings
Thousands of customers (no exact public count)
Market Analysis
Pros
- ✓Excellent developer experience (Python-native, no DevOps)
- ✓Genuinely fast cold starts
- ✓True scale-to-zero and pay-per-second with no idle cost
- ✓Covers inference, training, batch, and sandboxes
- ✓Strong, named enterprise and AI-startup customer base
- ✓Well-funded and rapidly growing
Cons
- ✗You build the application layer yourself (infrastructure, not turnkey)
- ✗Usage-based GPU costs can add up at scale
- ✗Cloud-only with no on-prem or self-hosted option
- ✗Thin presence on traditional enterprise review sites
- ✗Region markups and non-preemptible premiums
Pricing
Starter
$0/month + compute
- ✓$30/month free compute credits
- ✓3 workspace seats
- ✓100 containers
- ✓10 GPU concurrency
Team
$250/month + compute
- ✓$100/month free credits
- ✓Unlimited seats
- ✓1,000 containers
- ✓50 GPU concurrency
- ✓Custom domains
- ✓Static IP
- ✓Deployment rollbacks
Enterprise
Custom
- ✓Volume discounts
- ✓Higher GPU concurrency
- ✓HIPAA
- ✓Okta SSO
- ✓Audit logs
- ✓Private Slack support
- ✓Embedded ML engineering
Pay only for active compute time, billed per second with no charge for idle resources. Sample rates: B200 $0.001736/sec, H100 $0.001097/sec, A100-80GB $0.000694/sec, T4 $0.000164/sec; Volumes $0.09/GiB/month with 1 TiB free. All new accounts get $30/month in recurring free compute credits; startup grants up to $25K and academic grants up to $10K are available.
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