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Serverless cloud for running Python code, AI models, and GPU workloads at scale with per-second billing.
Serverless cloud for running Python code, AI models, and GPU workloads at scale with per-second billing.
Modal is a serverless platform that lets Python developers deploy functions, web endpoints, and AI workloads to managed CPU and GPU infrastructure with almost no DevOps. You decorate a function with @modal.function() — specifying memory, CPU, GPU type, container image, and secrets — and Modal handles container builds, autoscaling from zero to thousands of replicas, secret injection, persistent volumes, and request queuing. The platform is especially popular for AI workloads: hosting open-source LLMs and embedding models, running Whisper transcription, fine-tuning jobs, ComfyUI image pipelines, scheduled batch jobs, and webhook backends for agents. Modal supports a wide range of GPUs including A10G, A100, L4, L40S, H100, H200, and B200, billed by the second, with no idle charges between requests. The developer experience is its standout feature: cold starts measured in seconds thanks to a custom container runtime, native code-to-cloud sync, in-process debuggers, and a CLI that feels like running scripts locally. Pricing follows pay-as-you-go GPU rates (for example, B200 at $0.001736/sec, H200 at $0.001261/sec) with a Starter tier including $30/month of compute and Team/Enterprise plans for collaboration features and committed-use discounts. Companies use Modal as a flexible alternative to Replicate, Banana, RunPod, or self-managed Kubernetes for AI inference and data jobs.
Modal is popular with ML engineers for its Python-native developer experience and reduced Docker and Kubernetes overhead. The ratings below are editorial estimates based on the captured vendor pages, pricing data, listed security pages, and observed feature coverage rather than third-party review averages.
Modal lets developers define cloud functions, dependencies, hardware, and runtime behavior directly in Python. This is useful for AI teams that want infrastructure to live close to application code.
The platform supports LLM inference, multimodal inference, embeddings, reranking, evals, and dataset generation on GPUs such as H100s, A100s, A10Gs, and B200s.
Modal supports SFT, LoRA, full fine-tunes, parallel hyperparameter sweeps, and multi-node training. The website specifically mentions access to up to 128 B200s for demanding training runs.
Modal Sandboxes provide isolated, ephemeral execution environments for coding agents, untrusted code, and agentic systems. They can use custom images and dependencies for repeatable task execution.
Modal includes integrated logging and visibility into every function, sandbox, and container according to the website. This helps engineering teams debug jobs, monitor latency, and understand cost drivers.
$0 + $30/mo credits
Usage-based
Custom
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We believe in transparent reviews. Here's what Modal doesn't handle well:
The captured 2026 enrichment emphasizes Modal's continued focus on serverless GPU infrastructure, sandboxes for AI systems, high-end GPU availability, and usage-based pricing. Verify newly released hardware, regional capacity, and account-plan terms directly on Modal's live pricing and documentation pages.
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Pricing and features change often. If anything on this Modal page is wrong or missing, tell us and include a link to the source. We review every correction.