Modal
Modal is a serverless, code-first cloud platform for teams that develop, train, and serve AI applications at scale. Developers define their entire AI stack in Python, and Modal handles GPU provisioning, container orchestration, and automatic scaling across a global pool spanning 20+ clouds. The platform supports inference, training, batch processing, and sandboxed code execution for AI agents, with sub-second cold starts and usage-based pricing.
- Artificial Intelligence
- Developer Tools
- Software Only
Funding
Raised to date
$1.2BRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
across 5 rounds
+6$750M · Series D
Announced Oct 2026 · from article date
Disclosed cumulative: $1.2B
Founders
Founder details are not available yet.
Product
Problem
Machine learning teams often face high latency from cold starts, complex multi-cloud GPU provisioning, and heavyweight configuration files that slow iteration on inference, training, and batch pipelines. These friction points increase operational overhead and limit the ability to scale AI workloads quickly and securely.
Solution
Modal delivers a serverless, code-first platform that lets developers define the entire AI stack in Python, eliminating YAML and manual provisioning. Its custom container runtime launches GPU-accelerated functions with sub-second cold starts and automatic scaling across a global pool of GPUs, so workloads expand to thousands of nodes on demand and shrink back to zero when idle. Integrated observability provides real-time logs, metrics, and dashboards for every function, while a built-in distributed storage layer enables fast model loading and data access. Secure sandboxes built on gVisor enforce isolation and support fine-grained RBAC, meeting SOC 2 and HIPAA requirements. First-party integrations let users mount cloud buckets, connect to MLOps tools, and stream telemetry without additional glue code. The platform supports end-to-end AI use cases—including inference, fine-tuning, batch processing, notebooks, and sandboxed code execution—through a unified API and pricing model that charges only for actual compute time.
Target Audience
The platform is aimed at ML engineers, data scientists, and AI product teams—from fast-moving startups to large enterprises—who need to deploy inference, training, or batch pipelines without managing underlying infrastructure. It also serves developers requiring secure, on-demand code execution environments such as sandboxed LLM agents or custom AI services.
Features
- Python SDK that programs infrastructure as code, synchronizing environment and hardware requirements automatically
- Serverless containers with sub-second cold starts and GPU snapshotting for rapid model initialization
- Elastic, multi-cloud GPU scaling that provisions thousands of GPUs on demand without quotas or reservations, spanning 20+ clouds
- Sandboxes: isolated, dynamically-defined environments for coding agents, RL rollouts, and untrusted code, scaling to 1 million concurrent instances
- Multi-node training clusters with RDMA connectivity, provisioned via a single decorator
- Unified observability dashboard offering live logs, metrics, and resource-usage insights per function
- Distributed storage layer optimized for high-throughput model and dataset access across all workloads
- Secure sandboxes using gVisor isolation, RBAC, and compliance certifications (SOC 2, HIPAA)
- Native integrations for cloud bucket mounts, MLOps pipelines, and telemetry services (Datadog, OpenTelemetry)
- OpenAI-compatible endpoints for out-of-the-box inference performance