Modal Labs, a serverless cloud platform for building and running AI applications, secured $355 million in Series C funding at a $4.65 billion valuation. The round represents a significant jump from the company's $1.1 billion valuation in 2025.
Modal Labs has closed a $355 million Series C funding round, with the AI infrastructure startup now valued at $4.65 billion, according to CEO Erik Bernhardsson.
The company provides a serverless cloud platform designed to simplify the deployment and inference of AI models. Modal's platform abstracts away infrastructure complexity, allowing developers to build and scale AI applications without managing underlying servers or hardware.
The valuation marks a substantial increase from Modal's previous $1.1 billion valuation in 2025, reflecting growing investor confidence in the AI infrastructure space. The funding will likely support product development, infrastructure expansion, and team growth as demand for AI deployment tools continues to accelerate.
Modal competes in a crowded market of AI infrastructure providers, including Replicate, Banana, and traditional cloud platforms like AWS and Google Cloud. The company's serverless approach targets developers seeking simplified workflows for AI model deployment, particularly as enterprises scale their AI operations.
The startup has positioned itself as a developer-friendly alternative to managing AI workloads on general-purpose cloud platforms. Its focus on inference and application building addresses a specific pain point for teams working with large language models and other AI systems.
The Series C round comes as AI infrastructure continues to attract significant venture capital. Companies offering tools and platforms that lower barriers to AI development have become increasingly attractive to investors betting on widespread AI adoption across industries.
Modal's funding reflects broader trends in enterprise AI, where infrastructure and tooling companies are benefiting from increased spending on AI capabilities. As organizations move beyond AI experimentation toward production deployments, demand for efficient deployment platforms is expected to grow.
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