Headquarters
New York, NYFounded
2022CEO / Founder
Erik BernhardssonEmployees
11-50Funding
Series B — $67MProducts
1 product listedAvg. AI Score
8.2/10Website
modal.com/Profile updated
Modal is a serverless compute platform founded in 2022 by Erik Bernhardsson and headquartered in New York, NY. The platform enables developers, data scientists, and AI teams to run Python functions on cloud infrastructure—including CPU, GPU, and memory-intensive workloads—without provisioning or managing servers. Users deploy code by decorating Python functions with Modal decorators, and the platform handles scaling, containerization, and resource allocation automatically.
Modal's primary product is a Python SDK and cloud execution environment that supports GPU-accelerated workloads commonly used in machine learning model training, inference, and batch data processing. The platform targets AI researchers, ML engineers, and data teams who need on-demand access to compute resources such as NVIDIA A100 and H100 GPUs. Modal competes with services like AWS Lambda, Google Cloud Run, and specialized ML infrastructure providers such as Replicate and RunPod.
Modal raised a $16 million Series A round in 2023 and subsequently closed a $45 million Series B round in 2024, bringing total disclosed funding to approximately $67 million. The company was co-founded by Erik Bernhardsson, previously CTO of Better.com and an engineering leader at Spotify, where he created the Luigi data pipeline framework. Modal employs fewer than 50 people as of 2024.
Dated, source-cited figures. Estimates are marked.
All products by Modal reviewed by our AI panel of experts.
Who Modal competes with, and how they differ.
Focuses specifically on model serving and inference deployment with a managed infrastructure layer, whereas Modal offers a broader general-purpose serverless compute platform for arbitrary Python and GPU workloads.
1 product reviewed on TopReviewedBuilt around the Ray distributed computing framework for large-scale ML training and serving, targeting teams already invested in Ray, while Modal provides a simpler function-based serverless model without requiring a specific framework.
1 product reviewed on TopReviewedA full-stack managed ML platform tied to Google Cloud's ecosystem with broader MLOps tooling (pipelines, feature store, model registry), priced via GCP consumption, compared to Modal's lightweight, cloud-agnostic serverless execution model.
2 products reviewed on TopReviewedA hosted platform for running and deploying open-source ML models via API with per-second GPU billing, more narrowly focused on model inference than Modal's general-purpose compute platform for arbitrary Python code.
Offers on-demand and serverless GPU cloud infrastructure with a more infrastructure-centric, container-based deployment model, competing directly with Modal on GPU workload pricing and availability rather than developer-experience abstractions.
An open-source-first MLOps suite emphasizing experiment tracking, orchestration, and on-prem/hybrid deployment control, contrasting with Modal's fully managed serverless execution approach.
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